AI and Enterprise Technology Predictions from Industry Experts for 2026
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By
William Jepma
, Writer, Editor, and Analyst at Solutions Review - Best Practices,
As part of the 7th Annual Insight Jam LIVE event, the Solutions Review editors have compiled a list of predictions for 2026 from some of the most experienced professionals across the Artificial Intelligence (AI) and broader enterprise technology marketplaces.
As part of Solutions Review’s annual Insight Jam LIVE event, we called for the industry’s best and brightest to share their enterprise technology predictions for 2026 and beyond. The experts featured represent some of the top solution providers, consultants, and thought-leaders with experience in these marketplaces. Each projection has been vetted for relevance and its ability to add business value.
Enterprise Technology Predictions for 2026 and Beyond
Guy Adams, Co-Founder of DataOps.live
Solving the AI-Readiness Gap Will Become the Primary Investment Priority for Data Leaders.
“Despite enormous investment in AI, most enterprises still lack AI-ready data, which is considered data that is trustworthy, governed, contextualized, and aligned to specific use cases. In 2026, this readiness gap becomes both the leading cause of AI project failures and the biggest driver of new spending. Organizations will shift aggressively toward capabilities that operationalize AI readiness, including automated pipeline orchestration and deployment, as well as in-line governance enforcement using policy-as-code. Continuous observability and quality checks, automated validation, and regression testing are other areas that businesses will focus on next year to maximize the benefits of their AI efforts. These will be followed by environment management across dev → test → prod and versioned, reproducible data products.
“Gartner consistently highlights DataOps as not just a process, but as a strategic enabler of AI-ready data. With core pillars that include automation, orchestration, observability, testing, and governance, DataOps is quickly becoming the foundation enterprises rely on to make data fit for AI and ensure models can be scaled securely and reliably. In 2026, organizations will finally recognize that AI initiatives succeed only when automated DataOps practices are embedded into every step of the data lifecycle.”
Tyler Akidau, CEO at Redpanda Data
By 2027, AI agents will finally outnumber human employees—but only in the boldest, most operationally mature enterprises.
“In 2026, a few visionaries will come close, but most organizations will simply lack the data infrastructure or the courage to unleash autonomous software workers. Unlike their human counterparts, these agents won’t have titles or emails; they’ll quietly execute work through governed data layers, forcing companies to invent new observability and audit models just to track what their digital workforce is doing. Early adopters will gain a real competitive edge while everyone else spends 2026 pretending their chatbots qualify as ‘AI transformation.'”
In 2026, enterprises will wake up to the governance crisis of AI agents.
“As fleets of autonomous agents proliferate across data systems, CTOs and CIOs will realize that their biggest bottleneck isn’t model performance—it’s governance. They’ll discover that traditional IAM and RBAC tools can’t keep pace with short-lived, dynamic agents acting across hundreds of services. Most organizations won’t have the time or resources to build bespoke control planes, which will accelerate the adoption of open frameworks and shared standards like MCP and A2A.”
The enterprise data stack will become “agent-ready” by default.
“By the end of 2026, connectivity, governance, and context provisioning for AI agents will be built into every serious data platform. SQL and open protocols like MCP will sit side by side, allowing both humans and machines to query, act, and collaborate safely within the same governed data plane.”
Carlos Armada, Head of Product at name.com
AI agents will become the new intermediaries of digital ownership.
“Next year, AI agents will play a major role in building and managing the web. As these agents take on more operational control, domain and hosting providers will help define how agents interact with the internet. The challenge and opportunity ahead lie in defining how ownership, identity, and security function when machines can act on behalf of humans. These organizations will play a role in helping establish clear, transparent frameworks for agent-driven activity that will lead to trust, shaping the foundation of how digital property is managed in the AI era.”
Tiago Azevedo, Chief Information Officer at OutSystems
Software will fade away as agent-as-a-service delivers outcomes.
“The agent-as-a-service market is projected to expand from $5.1 billion in 2024 to $47.1 billion by 2030. In 2026, we’ll see more agent subscriptions and services that operate across multiple repositories and databases without discriminating between backend systems and fewer SaaS instances. This means that employees will command groups of AI agents that orchestrate workflows across systems, rather than opening multiple tabs to use different software or SaaS platforms. Real, tangible outcomes that drive business forward will be the stars of the show, not the software that enables them.”
Enterprise AI infrastructure needs will change how data is stored, managed, used, and accessed by applications.
“Agentic AI and AI-driven workloads require reliable data storage, information streamed in real-time, events organized by context, and the reuse of data for new models. Therefore, the infrastructure to support them will be expected to include powerful compute (CPUs, GPUs, TPUs), high-performance networking, scalable storage, and robust security and governance measures. That’s the reason why the enterprise AI data infrastructure market is expected to reach a $7 trillion valuation by 2030. To meet the surging demand, companies like Dell are enhancing their AI data platforms now so enterprises can convert distributed data into more reliable AI outcomes in 2026.”
Agentic AI will rehumanize the enterprise.
“In 2026, the rate at which agentic AI automates mundane and repetitive tasks will grow exponentially as the technology matures and multi-agent solutions become the norm. That will free up people to focus on creativity, strategy, and genuine human connection. Uniquely human soft skills, such as collaboration, adaptability, emotional intelligence, and judgment, will be more valuable and in higher demand. For example, as the more mundane aspects of talent onboarding and management are handled by agents, HR leaders expect a 30 percent productivity boost per employee. They also believe 23 percent will be shifted to entirely new positions that better leverage their human talents.”
Savinay Berry, Executive Vice President and Chief Product and Technology Officer at OpenText
2026 will be the year to prove real ROAI.
“The time for counting AI pilots and projects is over. In 2026, organizations will need to demonstrate a real return on AI investment (ROAI) through outcomes that enhance performance, reliability, and customer experience. Measuring the percentage of AI-generated code or model activity doesn’t say much. What will matter is whether AI shortens release cycles, improves uptime, and helps teams recover faster from incidents. When AI delivers measurable improvements in speed, quality, and stability, that’s when it will become a trusted business advantage.”
Martin Bitzinger, Senior Vice President of Product Management at Mitel
Flexibility + Control: The Power Duo for 2026
“Enterprise decision-makers are done chasing the next shiny object and locking in on the requirements for IT success in 2026: flexibility and control. With IDC identifying hybrid as the default strategy for most organizations, these two pillars are becoming the backbone of enterprise resilience.
“By 2026, organizations will require communications and workflows that are 24/7 reliable, adapting and scaling seamlessly without disruption. This requires a combination of on-premise and cloud technologies, where the hybrid architecture acts as a backstop for resilience. A true hybrid environment strikes a balance between flexibility and control, keeping mission-critical workloads protected behind adaptable yet secure guardrails. Hybrid isn’t a buzzword; it’s the blueprint for resilience, relevance, and long-term adaptability.”
Matt Blumberg, CEO at Markup AI
“This past year has been a real wake-up call. Everyone embraced generative AI, but now we’re dealing with the hangover. The big realization is that this isn’t just about making things faster—it’s about managing real, bottom-line risk. Moving into 2026, we’re entering the age of responsibility. Everyone’s excited about agentic AI, but the problem is, in the race to adopt it, many organizations are skipping a critical step: Compliance. We have to put the right guardrails in place before we let AI run on its own.
“That’s why I’m convinced Gartner’s new category of guardian agents—AI designed to monitor other AI systems—is set to explode next year. Businesses need to know their AI is operating responsibly and not exposing their operations to new risks. 2026 won’t just be about accelerating what AI can do. It’s about ensuring it does what it should do correctly.”
Kevin Bocek, SVP of Innovation at CyberArk
Shareholders will eventually hire boards of directors entirely comprised of AI agents.
“In 2026, more organizations will take steps towards the eventual reality of fully autonomous, AI-agent Boards of Directors by integrating ‘shadow boards’ into corporate governance. These AI systems won’t replace human board members for many years, but they will function as powerful co-pilots for the CEO and human directors, primarily to simulate scenarios and assist with data analysis and complex decision-making. While the ultimate vision of a board entirely comprised of AI agents remains a distant reality, this integration will introduce immediate security risks, including the challenge of managing the identities and access privileges of these powerful new AI entities as they gain access to the deepest levels of corporate data.”
Alessio Bonfietti, Chief Science Officer of Data, AI, and Analytics at XTEL
“2026 will mark a fundamental shift in how retail mega brands operate—moving from AI as a support tool to AI as the primary decision-maker in both commerce and operations. My three key predictions for 2026 are:
Agent-driven commercial planning becomes the operational standard.
“Trade promotion, forecasting, and commercial planning will be largely AI-powered with human approval, rather than human-led, a reversal of today’s model.”
Agentic shopping goes mainstream.
“As large retailers like Walmart and Target embrace agentic shopping, consumers will quickly grow more comfortable outsourcing large portions of their shopping journeys to AI assistants. To capitalize on this opportunity, companies will need to ensure their product data is accurate, enriched, and retail-ready to be discovered in AI-assisted shopping environments.”
Real-time commercial decisioning yields greater revenue potential
“Retailers and consumer goods companies will dynamically optimize pricing, promotions, and assortment in near real-time, significantly improving responsiveness to market conditions to attract shoppers and drive sales.
“Yet, these predictions hinge on one critical prerequisite: fixing data fragmentation. Today, inconsistent data across retailers and markets slows automation and degrades AI performance just as the technology is ready to scale. Companies that solve this obstacle first will unlock everything that comes next.”
Peter Boumenot, CPO at CentralReach
“The 2026 landscape will be defined by the continued shift to agentic AI, where goal-driven agents move beyond simple assistance to execute multi-step workflows. In the autism and Intellectual and Developmental Disabilities (IDD) care space, this means agents will continue to transform practice management by automating end-to-end processes like claims management, scheduling orchestration, and clinical documentation to significantly reduce administrative burden and enable a greater focus on clinical outcomes.”
Julie Brewer, EVP of Finance at EdgeCore Digital Infrastructure
Data Center Investment Accelerates in 2026
“Data centers remain a relatively new asset class. But unlike any other, they are evolving faster than almost anything else in the infrastructure world. As society becomes increasingly connected and AI drives unprecedented demand, the pace of change keeps the industry perpetually “new,” requiring constant adaptation and innovation.
“Capital investment will continue to be critical to keep up, especially as the costs to build and equip a facility easily reach into the billions. Funding for data centers is accelerating, driven by growth expectations, customer demand, and the need for committed cash flow. In 2026, the rise of the data center asset class will continue at record speed, and those with access to capital will be best positioned to seize the opportunities that come with it.”
Access to Power Remains a Critical Bottleneck
“Access to reliable power continues to be one of the biggest challenges for data centers, and 2026 will be no different. What used to require a small security deposit is now just the starting point. Utilities are asking for bigger financial commitments as they invest in generation, transmission, and on-site infrastructure themselves. However, the landscape is still evolving, and utilities are figuring out the best way to manage these demands. For developers, this creates a race to lock in power early while also planning for long-term infrastructure. Success will go to those who can move quickly, navigate shifting utility requirements, and have the capital and strategy to secure power today and sustain it tomorrow.”
Nick Burling, Chief Product Officer at Nasuni
Unstructured Data Will Be the Backbone of AI Innovation.
“In 2026, unstructured data will emerge as the backbone of AI innovation, redefining how enterprises harness intelligence across their organizations. As AI continues to advance, the availability of high-quality structured data is reaching its limits, creating what many analysts call a ‘data ceiling.’ However, with an estimated 80–90 percent of enterprise data existing in unstructured forms, from documents and emails to images, videos, and design files, the potential to unlock its value has never been greater. This vast, often underutilized data holds the key to deeper insights, smarter automation, and more contextually aware AI systems.
“The next wave of AI progress will depend on how effectively organizations can access, govern, and activate their unstructured data. Doing so will require a strategic shift; one that prioritizes data quality, context, and security in equal measure. Unstructured data is the next iteration of data for AI. In 2026, having a comprehensive strategy for enterprise unstructured data is no longer considered ‘being a step ahead’ but vital for AI success.”
Kyle Campos, CTPO at CloudBolt
The Next Phase of AI: Agents Finally Start Talking to Each Other.
“In 2026, we’ll see a surge in Model Context Protocol (MCP) adoption, cross-agent communication, and effective multi-agent systems. This will be one of several factors pushing enterprises to formalize corporate AI strategies, moving usage out of individual productivity lanes and setting clear organizational requirements. Vendors will be expected to align with these strategies, with RFPs explicitly requiring interoperability and MCP compliance.”
Kubernetes and Cloud Optimization Go Mainstream.
“Automated Kubernetes optimization will hit majority adoption, moving cost management and performance tuning out of ineffective manual processes. This trend toward continuous optimization will expand beyond Kubernetes to data warehouses and other high-growth workloads, fueled in part by AI-driven usage growth.”
The End of Silos: The “School of Fish” Approach.
“Teams responsible for ‘build,’ ‘manage,’ and ‘optimize’ initiatives will no longer operate in isolation. The shift from ‘pets to cattle to school of fish’ will take hold, where orchestrating the entire stack holistically, not just compute clusters – from deployment to management to optimization – will deliver faster, more efficient, and secure operations.”
Ashley Casovan, Managing Director at IAPP
Polarized Public Attitudes Set the Pace for AI Uptake
“Trust has been a major topic of discussion in the AI governance community, especially in the latter half of 2025. I believe that this trend will continue to increase, and we will see stronger opinions from the public on where they feel comfortable and uncomfortable using AI. While the term may change, public sentiment is going to continue to be a limiting (or accelerating) factor for AI adoption. The hope is that with increased commitment from companies to design and deploy AI systems in a responsible and trustworthy manner, there will be a greater sense of confidence and tech acceptance.
“I suspect that the community where people engage and have conversations about the implications of AI will have a strong impact and potential polarization on public sentiment. These lines could be drawn by age, geographic, political, or other demographic categories. We already saw in reporting this year that different regions of the world have different risk tolerances when it comes to the adoption of AI.”
Josh Claman, CEO of Accelsius
The Federal Catalyst for AI Infrastructure and US Leadership.
“In 2026, we’ll see a decisive acceleration in federal investment around AI infrastructure. Policymakers increasingly recognize that global AI leadership depends as much on the sophistication of our infrastructure as on the intelligence of our algorithms. The AI race has shifted from software to infrastructure deployment efficiency. Federal funding will follow this shift, favoring projects and partners that can deliver speed, scale, and reliability as part of a national strategy to secure America’s AI advantage. Programs like DOE’s COOLERCHIPS highlight this urgency. These programs represent strategic positioning for a future where advanced thermal management is a prerequisite for AI leadership.”
The Infrastructure Integration Imperative.
“The era of point solutions is ending. In 2026, data centers will increasingly be defined by co-design—where compute, power delivery, and cooling are engineered as a single system rather than assembled as parts. The M&A wave we’re seeing—with hyperscalers acquiring energy assets and power and cooling vendors consolidating—reflects the market’s understanding of this convergence. The focus has shifted from buying components to building AI-ready ecosystems.”
The Two-phase Tipping Point: From Proof-of-Concept to Production Scale.
“If 2025 was the year of proof, 2026 will be the year of scale. The industry is crossing the chasm from pilot to production. Hyperscalers and Neoclouds are no longer asking whether two-phase cooling works—they’re asking how quickly it can be deployed across global portfolios. As early adopters validate performance gains under the most demanding workloads, advanced liquid cooling becomes less a differentiator and more a requirement for participation in the AI infrastructure economy.”
Kevin Cochrane, Chief Marketing Officer at Vultr
The Great Neocloud Consolidation Begins
“More than 80 percent of the NVIDIA and AMD GPU market share will concentrate among a handful of neocloud and alternative cloud providers worldwide for both the NVIDIA and AMD AI ecosystems. The winners will be those with the trifecta of capital, scale, and go-to-market execution: the ability to raise capital and keep pace with demand, quickly deploy massive GPU clusters, and attract top-tier AI customers to their platforms. Those lacking one or more of these capabilities will struggle to compete and begin to fade from the market. “
The “For What?” Year of the Sovereign Cloud.
“Until now, sovereign cloud has long been treated as a necessary ideal – important, but not yet fully defined, scoped, and prioritized. Despite strong government commitments, progress has been slowed by the absence of clear regulations to drive adoption. In 2026, that will begin to change. Nations will start aligning sovereign cloud initiatives with their broader digital strategies, tying deployments to innovation goals in startups, academic research, and AI ecosystems. This will be the year sovereign cloud shifts from concept to purpose-driven implementation.”
The Rise of the Alternative Hyperscaler.
“More than a neocloud, enterprises will recognize the need for an alternative hyperscaler. This new class of cloud provider will combine full public cloud capabilities with specialized AI infrastructure services, while supporting an open, composable ecosystem. The winners will be platforms that deliver scale, flexibility, and openness, enabling organizations to deploy advanced AI workloads without being locked into a single vendor or limited stack.”
The enterprise AI rebuild shows real impact.
“Enterprises will finally move from AI strategy to execution. Approaches such as platform engineering will drive faster integration, while decision-making shifts from data analysts to developers who prefer open-source over black-box tools. Meanwhile, open ecosystems, alternative hyperscalers, and silicon diversity are reducing the barriers to retooling and scaling, minimizing risk and vendor lock-in. For the first time, significant use cases and success stories will emerge, demonstrating real-world value and providing examples that other organizations can follow.”
Agentic AI at the edge puts industries first.
“Edge AI will be highly industry-specific, supporting use cases that demand domain expertise, such as drones inspecting nuclear power plants with on-device models for real-time detection. Broad deployment of general-purpose AI agents at the edge remains a longer-term goal, with adoption occurring incrementally, use case by use case, and industry by industry.”
Ty Colman, Co-Founder and Chief Revenue Officer at Optera
AI’s Role in Greening the Energy Grid
“AI’s growing footprint is forcing the biggest rethinking of our energy infrastructure in decades. Data centers are driving massive increases in consumption—and electricity rates—but they’re also exposing the slow and carbon-intensive nature of traditional power development. When you need gigawatt-hours of new capacity deployed quickly, you can’t wait years for coal or gas plants. Renewables accounted for over 90 percent of the new utility-scale generating capacity in 2024, partly because they’re faster to deploy and easier to scale. This reality will push utilities, investors, and governments to accelerate clean energy projects in 2026, making AI’s appetite an inadvertent catalyst for the energy transition.”
The 2030 Crunch and Data Pragmatism
“2026 is the year companies must face the progress (or lack thereof) made on their 2030 climate goals. Many are still debating the quality of their data instead of acting on it. Perfect data will never exist; seeking that mythical perfect data will only slow progress. The next phase of climate leadership will be defined by companies that utilize the information they have to take meaningful action towards decarbonization, while continually refining their data as they go. Organizations that move forward with focus and transparency will close the gap between intention and impact as we pass the halfway point of the decade.”
David Colwell, VP of AI & ML at Tricentis
Enterprises Will Enter the ‘Parenting AI’ Phase.
“AI has spent the last two years acting like a gifted but unsupervised teenager. It moves incredibly fast and produces impressive outputs, but it also hallucinates and is too confident for its own good. Vibe coding has made that painfully clear. In 2026, enterprises will begin to limit AI’s ability to complete tasks unchecked. They will start ‘parenting’ it: breaking work into smaller steps, asking it to repeat instructions, demanding it show its work, and evaluating its reasoning before trusting the output. The companies that succeed will be the ones who pair AI’s speed with human judgment.”
Speed Alone Will Stop Being Enough—Trustworthy Output Will Matter Most
“As organizations uncover how often AI-generated outputs turn out fragile, shallow, or flat-out wrong once they enter real workflows, speed will no longer be the primary benchmark for AI value. In 2026, enterprises will prioritize trustworthy output, which means requiring AI to demonstrate how it arrived at an answer, validate the steps it took, and ensure its reasoning aligns with business logic. The advantage will lie with teams who recognize that AI’s confidence is not competence, and who build systems that enforce the reliability and discipline AI currently lacks.”
Andrew Dodd, Worldwide Marketing Communications Manager at Hewlett Packard Enterprise Storage
2026 will be the year of pragmatism and reality for storage.
“As companies are obliged to manage and protect ever increasingly large data sets, pragmatism and reality will set in. Cost, access speed, and physical limitations (racks, power, cooling) will continue to enforce the same traditional considerations around storage that have existed for years. Speed will be crucial for some workloads, but for others, it will be less so. For these capacity-intensive, rather than GPU-intensive, applications and processes, tape will be a saving grace.”
Sam Dorison, CEO and cofounder of ReflexAI
Specialization will overtake scale.
“The next generation of AI innovation won’t come from general-purpose models. It will come from specialized, domain-specific systems. In 2026, industries such as healthcare, finance, and public safety will increasingly turn to tailored AI tools that understand their specific language, data, and regulatory needs. The age of ‘one-size-fits-all’ AI is closing, replaced by highly verticalized solutions that deliver outsized impact within specific contexts.”
Uneven regulation and global shifts will reshape the AI map.
“AI’s future won’t unfold evenly across the world. In 2026, regulatory fragmentation will deepen, with the EU tightening oversight, the U.S. maintaining a looser stance, and emerging markets setting their own pace. At the same time, the Global South will drive the next major wave of AI adoption. With a young, digitally fluent population and growing access to affordable tools, innovation will increasingly flow from regions that were once considered secondary markets.”
Aron England, Chief Product and Technology Officer (CPTO) at Accruent
Today’s AI models are still prone to hallucinations, and the issue isn’t slowing down.
“In environments where facility managers and technicians are leveraging AI to diagnose equipment failures and guide complex repairs, a misstep from a false or inaccurate recommendation can not only result in regulatory violations, but also major safety and cost incidents.
“For example, in manufacturing, if an AI-generated suggestion directs a factory worker to take the wrong action on a critical piece of equipment, such as misidentifying a fault or proposing an incorrect solution to a malfunction, it could result in production disruptions, unplanned downtime, or damaged machinery. These types of hallucinations from AI tools are especially detrimental to industries like healthcare, as liabilities and patients’ livelihoods are at risk when a machine failure occurs that was not properly maintained or addressed in a timely manner.
“In 2026, vendors will be forced to move beyond ‘black box’ assistants to transparent, audit-ready AI that can link back to the exact page, line, or diagram in source documents, building in human review for high-risk decisions. Technicians are already pressed for time, and by designing AI that explicitly supports a ‘human in the loop’ model, workers can quickly verify outputs using the provided citations, thereby avoiding compliance pitfalls and safety concerns for both workers and customers.”
Dave Eyler, VP of Product Management at SingleStore
“Through 2025, most organizations have been prototyping with AI… but 2026 will mark a shift to production. Once applications hit real users and real revenue, latency, concurrency, and cost per query become non-negotiable. Organizations will have to design infrastructure from the ground up to be AI-native. Architectures will seamlessly blend streaming and transactional workloads and tighten compute-storage integration, transforming data systems from passive backbones into active enablers of intelligent decision-making.”
Will Falcon, Founder and CEO of Lightning AI
2026 will mark the tipping point where enterprises begin decentralizing their AI infrastructure to reduce their reliance on hyperscalers.
“With multiple outages impacting millions of people over the past year and 74 percent of enterprises struggling to scale AI value from prototype to production, it’s clear that even the top infrastructure players haven’t successfully solved AI infrastructure. Going into 2026 and beyond, organizations need to implement a mix of build and buy for infrastructure to maintain momentum and avoid failures. Focusing solely on build hinders improvement of your model or product, but on the flip side, if you only buy infrastructure, you won’t have any control if issues or malfunctions arise.
“A mix of build + buy infrastructure and the ability to operate across multiple clouds will be crucial in staying ahead of the AI race. If you rely on a single cloud provider and their infrastructure fails–as we’ve seen a handful of times this year alone– you’re stuck. To build true resiliency, organizations need to adapt a multi-cloud strategy.”
Sean Falconer, Head of AI at Confluent
Context Engineering Will Become the Top Technical Priority for AI Teams in 2026.
“As enterprises scale beyond simple chatbots to deploy sophisticated multi-agent systems, the engineering focus will shift from crafting better prompts to architecting better context. Multi-agent workflows rapidly expand context requirements with tool definitions, conversation history, instructions, and data from multiple sources. This creates two critical problems: context windows quickly fill up, and models suffer from ‘context rot,’ which causes them to forget crucial information buried in lengthy contexts. Companies will discover that managing context is highly domain-specific work that can’t be solved with off-the-shelf tools. By mid-2026, context engineering will emerge as a distinct discipline, with dedicated teams and specialized infrastructure built to dynamically serve the minimal but complete information AI agents need to function reliably.”
The Semantic Layer Will Become AI’s Most Critical Infrastructure in 2026.
“In 2026, enterprises will realize that AI agents don’t just need data – they need meaning. As models evolve from passive assistants to autonomous actors, the absence of a semantic layer will become the chief blocker to trustworthy AI. Companies that have spent years perfecting data lakes and warehouses will discover that those assets are insufficient: AI can retrieve data, but without semantic context, it can’t interpret action, implication, or intent.
“More teams will move beyond vector search and RAG toward building knowledge graphs, ontologies, and metadata-driven ‘maps’ that teach AI how their business actually works. The battleground will shift from owning raw data to owning the interpretation of that data. Off-the-shelf agents will struggle in complex domains because semantics are deeply domain-specific, forcing companies to invest in bespoke semantic infrastructure. By late 2026, the semantic layer will be recognized as the new cornerstone of AI reliability – as fundamental as the database was to analytics.”
Yuval Fernbach, VP and CTO of MLOps at JFrog
AI Governance Will Become the New “DevOps” for the Enterprise
“In 2026, enterprises will realize that AI adoption isn’t primarily a modeling challenge—it’s an operational one. As every team from development to security to business operations begins using AI tools and agents, CIOs and platform leaders will be forced to standardize how AI is discovered, approved, secured, and monitored across the company. The result: AI governance will evolve into a new enterprise discipline, much like DevOps did a decade ago. Companies that treat AI as a governed supply chain, rather than a collection of disconnected pilots, will scale faster and avoid compliance and security pitfalls that slow their competitors. Just as DevOps faded into the background so developers could focus on building, AI governance will mature to the point where developers barely realize it’s there.”
Companies Will Shift Standalone Models to Deeply Integrated, Context-Enriched Systems.
“While today’s AI adoption often starts with generic LLMs and isolated prototypes, enterprises are realizing that real value doesn’t come from the model alone—it comes from how well that model is connected to their internal systems. In 2026, the focus will move away from ‘building your own’ models and toward deploying AI that natively integrates with internal assets: data sources, tools, APIs, operational workflows, and governance layers.
“Models and agents will increasingly use MCP-like connectors to enrich prompts with internal organizational context, retrieve real-time business data, and perform actions across existing enterprise systems. This shift turns AI from a static text generator into an operational participant—one that queries, validates, updates, and orchestrates tasks based on live internal information. As a result, companies will reduce drift, improve reliability, and unlock far faster time-to-value. Instead of experimenting in isolation, enterprises will rely on integrated, governed, production-ready AI systems that understand their business, operate within their environment, and continuously stay aligned with their internal truth.”
Mike Finley, Co-Founder at StellarIQ
AI Gets Small
“Next year, we’ll see booming use of AI on smaller devices, demanding far lower power than AI on larger form factors. Low latency is essential for these smaller AI applications, which generally can’t wait to send data back and forth to the cloud for analysis. This will move processing to the edge. Ultimately, this trend will drive a tidal wave of device replacement over the next 5 years – everything from TVs to thermostats. Models are extremely close to being able to run on existing edge hardware now, but they’re not quite there.
“We’ve all heard of the billions or trillions of parameters that models require, but what are those? In a data center, they are typically hairy numbers with seven-digit precision each, and users notice immediately when that precision is removed. However, experts have been able to ‘quantize’ those values for use on severely constrained hardware, provided we’re willing to forego some of the capabilities.
“What will it look like? Cars coming out in 2027 will be able to talk to EMS when they’re in a wreck. Washing machines will analyze what’s in the load and set itself. Ring doorbell will chat with visitors to determine who they are and what they’re up to. It should be noted that this will not reduce the cost of creating the models: there will continue to be extensive efforts to build powerful AI using very intense resources, which will then be ‘distilled’ for edge devices.”
Garth Fort, CPO at LogicMonitor
The Rise of the Chief AI Agent Officer.
“By 2026, enterprises will institutionalize AI accountability with a new executive seat: the Chief AI Agent Officer. This leader will define, audit, and govern the rules of engagement between humans and autonomous systems. Every AI action will be observable, explainable, and aligned with enterprise ethics. The organizations that adopt this role first will become the ones most trusted, proving that governance is not a brake on innovation but a moral accelerator.”
João Freitas, General Manager and VP of Engineering for AI and Automation at PagerDuty
AI maturity will make GenAI and AI agents easier to adopt.
“As standards like MCP and A2A take hold, best practices around agent deployment and monitoring will be established. This will unlock new use cases and business opportunities, and increase the return on investment from AI.”
Data will remain the foundation for successful AI deployments.
“Investment will continue in data privacy, security, and governance, but the biggest returns will come from extracting value across different data sources. The most successful AI projects will be those that have access to the right information and can access and build relationships between different data sources relevant to the problem they are solving. Additionally, we’ve seen several examples of companies protecting their data from being used to train 3rd party models, and it is expected that this trend will continue.”
AI agents will start to have true agency.
“Many of the examples we see in the market of AI agents are not truly agentic. As technology evolves and matures, we will see an increase in examples of true agency where AI agents become more autonomous, adapt better to their context and to the environment, can make better decisions given a wide range of tools and resources, and independently take actions towards a goal.”
Ethical and regulated AI will define the next phase of adoption.
“We will see continued efforts from governments and companies to increase efforts for the ethical use of AI and to guarantee the trust of their customers. Regulations will continue to evolve in terms of model transparency, bias, fairness, and security, presenting challenges on how we use existing and new technologies in this space.”
Sergio Gago, CTO of Cloudera
“2026 will be remembered as the first year of true convergence; a turning point where the boundaries between human and machine, that leverage cloud and data center alike, begin to blur. After decades defined first by control and then by cloud elasticity, we now enter a reality where both coexist seamlessly through unified control planes. Workloads will run wherever it makes the most sense, governed by security, compliance, and efficiency rather than location. This shift also signals a broader redefinition of performance: as AI and compute demands surge, enterprises will prioritize energy efficiency as a KPI, not an afterthought. The new competitive edge won’t come from the largest models, but from the most intelligent, efficient use of resources.
“In this new era, enterprises will think beyond tools and platforms to focus on the orchestration of intelligence itself. Data pipelines will evolve into self-learning systems, AI will be managed as part of the digital workforce, and sovereignty will extend from data to the algorithms that process it. Interoperability and governance will become the bedrock of progress, enabling systems, agents, and organizations to collaborate securely across clouds, domains, and geographies. The Era of Convergence is not about choosing sides—cloud vs. on-prem, human vs. machine—it’s about uniting them under a shared architecture of trust, efficiency, and intelligence.”
Sascha Giese, Evangelist at SolarWinds
Rethinking IT Investments in 2026: AI vs. Human Talent
“In 2026, we will see a significant shift in the way IT departments allocate their budgets. In the past, labor was expensive, and proper tools and solutions helped deal with understaffed teams and a lack of talent. As a result of global challenges and market changes, many critical solutions now come with a higher price tag than before. At the same time, we are experiencing massive layoffs across industries, which means it is now easier to find talent than it was a few years ago. This leads to a situation where it might make more sense for the business to invest in people rather than the latest and greatest tool, particularly when considering the total cost of ownership of AI.”
Joseph George, General Manager & SVP of IT Solutions Group at GoTo
2026 IT Trends: Ethical Adoption and Agentic Service Delivery Amidst AI Rise
“Despite regulatory pressures and economic uncertainty, organizations will lean even further into AI in 2026. As adoption grows, ethical considerations will become more relevant, prompting AI and IT leaders to emphasize responsible governance and transparency, balancing speed with accountability. IT teams will also focus on deploying agentic AI to transform service management & delivery, which will fundamentally change how IT effectiveness is measured. Instead of examining ticket resolution rates, organizations will focus on how well IT proactively prevents problems and drives business outcomes. However, as AI becomes more pervasive, organizations risk over-automation—applying AI in fully autonomous mode where human judgment should be required to work in concert with AI—and will need to regularly reassess their approaches to maintain the right balance.”
Phillip Goericke, Chief Technology Officer at NMI
“I believe that next year, we’ll see investments in the AI technologies we know today begin to cool after a couple of years of explosive growth. The hype is giving way to realism. Many business and tech leaders have moved past the shiny-object phase and now recognize large language models for what they are: powerful yet probabilistic engines. They’ve learned how difficult it is to build reliable products on systems that can’t guarantee accuracy, especially when those systems still come with high costs.
“Leaders who’ve already adopted AI won’t pull back; they understand its value when applied thoughtfully. But 2026 will be a year of refinement, one focused on strengthening strategies and guardrails and defining what AI-driven success truly means for their business, industry, and customers. Many will pause on further investments in familiar LLM-based technologies. The next wave of AI momentum will likely be sparked by a new breakthrough, and I’m optimistic that the next ‘ChatGPT moment’ will come from AI that can reason, not just react.”
Justin Graham, Director of Innovation Solution Center at Barge Design Solutions
“A key challenge will be managing AI disillusionment. In 2026, we’ll likely continue to face a dual reality: AI hype will outpace current capabilities, yet the value delivered by some AI solutions will be large and real. Staying grounded to implement the best solutions will require a combination of domain experience, technical knowledge, and critical thinking.”
Bennie Grant, COO at Percona
Trust, not Perfection, Will Define the Next Phase of AI.
“The industry will stop chasing ‘bulletproof’ AI. Just as organizations have adapted to occasional cloud outages, they’ll recognize that imperfect answers are an inherent part of generative systems – not fatal flaws, but challenges to be managed. The focus will shift from eradicating every error to designing AI that earns trust through transparency, accountability, and resilience. The leaders of this next phase won’t be those who promise perfection, but those who build systems that users can rely on, even when they’re imperfect.”
Legacy Systems Will Keep the Lights On.
“As enterprises race toward new AI horizons and the infrastructures that enable them—like Postgres, vector databases, and large-scale data pipelines—older systems like MySQL will quietly remain indispensable. These foundations may not make headlines, but they are the foundation of modern enterprise infrastructure. Regardless of how major vendors choose to evolve or exit, the world will always need experts who keep these systems running, secure, and optimized.”
The Open Source Community Continues the Fight Against Restrictive Relicensing.
“It’s unclear if or when another open-source company will change its license, but what’s become abundantly clear is how the community will react. Every time a company attempts to impose restrictions, developers and enterprises respond with innovation and collective action. Moving forward, the community will continue to create alternatives, influence licensing decisions, and ensure that openness and freedom remain the defining principles of the ecosystem. Transparency isn’t just a standard; it’s the bedrock of open-source.”
Dan Graves, Chief Product Officer at WitnessAI
Well-intentioned agents will cause operational disasters through poor decision-making.
“Throughout 2026, enterprises will experience significant operational incidents caused by well-intentioned agents making poor decisions with serious unintended consequences. These agents won’t ‘go rogue’ in a malicious sense—they’ll lack the judgment and foresight to understand the full impact of their actions. This will lead to deleted code bases, downed systems, and other ‘helpful’ disasters.
“The problem stems from agents operating like children who are smart at specific tasks but lack emotional intelligence and long-term thinking. When tasked with ‘improving’ code, an agent might decide that the most efficient approach is to delete the entire existing project and start from scratch. This might be logical from a narrow perspective, but it can be catastrophic in practice. Companies will discover that preventing malicious attacks is only half the battle when their own helpful agents can cause equivalent damage simply by trying to do their jobs. The agents will have been following their instructions perfectly. They simply interpreted ‘make this better’ or ‘optimize this process’ in ways that no human would have chosen. This will reveal the gap between computational logic and human judgment that no amount of training data can currently bridge.”
Alex Halliday, Co-Founder and CEO at AirOps
ChatGPT will launch ads and a new ad-free paid tier.
“We expect OpenAI to launch ads in 1H 2026 along with a higher-priced, ad-free premium tier. This will disrupt existing marketing budgets and create two new channels we need to master. ChatGPT Ads will become a major arena for performance testing and optimization. ‘VIP SEO’ will emerge inside the ad-free product, giving us access to a high-earning, highly engaged segment of ChatGPT power users who cannot be reached through ads.”
Taste and care for craft will become the most sought-after qualities in employees.
“AI has made it easy to create almost anything. What’s hard now is knowing what’s actually good. Having taste and discernment about when something feels right, when it’s off, and when to leave it alone is what will separate great operators from average ones. The companies that protect taste as they scale will be the ones people keep coming back to.”
Nabil Hannan, Field CISO at NetSPI
“In 2026, organizations will realize that AI doesn’t eliminate tool sprawl; it only accelerates it. Every tool, especially those driven by AI, requires ongoing tuning, governance, and integration. Redundant or poorly managed tools quickly bloat developer workflows, degrade efficiency, and expand the attack surface. The result: slower delivery, inconsistent pipelines, and security blind spots that end up being more of a distraction for the teams from true business priorities.
“Enterprises must shift from the ‘buy everything new and shiny’ mindset to a purpose-built toolchain strategy. This involves selecting, configuring, and integrating tools that align with the organization’s architecture, development methodology, compliance requirements, and operational maturity. Just adopting a best-of-breed checklist doesn’t work anymore because there’s no one-size-fits-all. With the rapid influx of AI-powered development and DevOps tools, I am aware that the temptation to adopt without a proper strategy continues to grow. The winners in 2026 will be the teams that treat tooling like a curated ecosystem, not a collection of point solutions. They will ensure that each tool has a clear owner, defined value, and measurable impact on speed, quality, and security.”
Tilman Harmeling, Senior Data Privacy Expert at Usercentrics
The Privacy Paradox will define 2026: people won’t trust AI, but they’ll use it for everything.
“From health consultations to investment advice, users will share more high-risk, personal data than ever, knowingly trading privacy for convenience. The equation is simple: speed wins. For marketers, this means designing experiences that are frictionless yet transparent, showing users that data protection and ease of use can coexist. The challenge is to make privacy intuitive, not invisible. The companies that succeed will make people feel both in control and effortlessly served.”
By 2026, privacy will no longer be a compliance exercise; it will be a core pillar of brand identity.
“Consumers now understand that ‘free’ AI tools come at a hidden cost: their data. As awareness grows, trust becomes the ultimate differentiator. The most successful brands will treat data not as a commodity but as a covenant, communicating openly about how it’s collected, used, and protected. Privacy will move from the legal fine print to the marketing headline. In an age of automation and opacity, the most human brands will be the most valuable ones.”
Eoin Hinchy, CEO and Co-Founder at Tines
In 2026, the companies that succeed with AI won’t be the boldest; they’ll be the ones with real guardrails. The question will shift from “Can AI do this?” to “Should AI do this?”
“2025 was the year of experimentation. Looking ahead to 2026, curiosity will give way to commitment as enterprises start to rely on AI agents as business-critical tools. But this psychological shift – moving from testing agents to trusting them – will widen between those who succeed and those who don’t because of one defining factor: security and governance. Companies that invest upfront in defining clear controls and guardrails will unlock the transformative productivity gains that have long been marketed. Those that rush to deploy without proper oversight, on the other hand, will face public failures that damage their brand and erode trust. Flashy demos may impress, but they rarely endure. The next phase of AI maturity depends on learning to delegate responsibility. Governance is not a box to check; it is the strategy. Those who understand this will turn AI from theater into lasting impact.”
CFOs will kill more AI projects than CTOs launch, as the era for AI for innovation’s sake ends and budget holders demand proof.
“Enterprises are reaching the end of the ‘AI for AI’s sake’ era, and this will crystallize next year when finance teams stop politely nodding at AI roadmaps and start demanding P&L impact in quarters, not years. The vendors who survive will be those who can answer one simple question: What specific salary expense does this replace, or revenue will this generate? A sharp divide will form between vendors offering quantifiable cost reduction and those offering aspirational transformation, and only one will survive procurement.”
Sarah Hoffman, Director of AI Thought Leadership at AlphaSense
Going All in on Proactive AI.
“The shift from reactive to proactive AI will define 2026. With long-term memory capabilities now established, AI will begin to anticipate user needs. Already, ChatGPT Pulse researches for users based on past interactions without a user prompt. In July 2025, leaked documents revealed that Meta is training its chatbots to be more proactive, prompting users to follow up on past conversations without requiring explicit input. Expect both enthusiasm and tension as users adjust to AI that acts before being asked.”
Jon Hyman, Co-Founder and Chief Technology Officer of Braze
E-commerce Traffic Will Grow in 2026, Not Decline.
“While AI search tools and browsers are making transactions easier, they simply can’t replace the human thrill of discovery. While AI browsers will take on a new role in shaping the customer experience, they won’t replace all of the ways we shop and interact with brands today. Digital window shopping will remain a core part of online behavior as we browse, compare, and imagine. AI will enhance this curiosity, creating richer, more engaging experiences, and the scroll will endure because people will always enjoy browsing, not merely buying.”
Universal AI Adoption Among Engineers Will Increase Output, Not Reduce Headcount.
“Every engineer will use AI for coding in 2026, making it an essential part of the job. In fact, this is likely already true as of today. Rather than reducing tech hires, AI efficiency will enable teams to address their existing project backlogs more effectively. Engineers are already oversubscribed with work; AI helps them get more done, but it doesn’t reduce the number of engineers high-growth businesses need.”
2026 Will Reveal AI’s True Business Value.
“This is the year the AI hype meets reality and value. 2026 will provide clearer insights into which companies are genuinely benefiting from AI implementation versus those experiencing temporary efficiency gains. Next year, companies will be able to distinguish sustainable AI value from initial hype, offering a more realistic picture of AI’s long-term impact.”
David Jones, Vice President of NORAM Solution Engineering at Dynatrace
Privacy-by-Design Observability Becomes a Hard Requirement.
“In 2026, privacy-by-design observability will no longer be a nice-to-have; it will be a hard requirement for any enterprise that wants to safely analyze or automate decisions with operational data. Banks, healthcare organizations, insurance providers, and even consumer tech companies are being pushed to treat telemetry with the same level of caution they apply to financial or health records. They’ll demand control over how data is collected, what gets masked, who can view sensitive fields, and whether that information remains in the cloud or stays within their own walls. The companies that succeed will be the ones that build privacy choices into every layer of the platform. Those who treat observability data casually will find themselves written out of RFPs before the conversation even begins.”
Debugging Moves Safely Into Production.
“In 2026, DevOps teams will rethink something foundational: the belief that all debugging must happen in pre-production. For years, companies have poured huge amounts of money into maintaining pre-prod environments that are supposed to mirror production—even though, as anyone who works in the field knows, they never fully match. With safe, real-time debugging and AI-powered analysis, teams will be able to diagnose issues directly in production without the fear that shaped DevOps culture for a decade. The impact will be dramatic. Pre-prod environments will get smaller, feedback loops will likely get shorter, and organizations will finally stop treating massive pre-production setups as a security blanket. That freed-up investment will be directed towards automation, process modernization, and AI tooling that actually improve delivery. It’s a big shift, and most of the industry won’t see it coming until the budget lines start to move.”
Suman Kanuganti, CEO and Co-founder of Personal AI
LLMs become infrastructure. Personal models create value.
“LLMs will remain foundational, but by 2026, they will be treated as background infrastructure. Ubiquitous, necessary, and undifferentiated. Strategic value will come from models that operate closer to the individual. Small language models trained on private data, designed for specific roles, will become the new interface for memory, communication, and context. This shift mirrors the evolution from mainframes to personal computing. Enterprises will no longer ask which LLM to use. They’ll ask how to build memory that is private, precise, and persistent. The value moves up the stack from general models to specific intelligence.”
AGI becomes regulated infrastructure.
“Artificial General Intelligence will not be commercialized in its pure form. It will be nationalized. By 2026, major governments will treat AGI as core infrastructure, regulating it alongside telecom, energy, and cloud. Rather than a singular global agent, we’ll see regional AGIs each reflecting the governance, values, and legal frameworks of its host country. Private AGI may still exist, but access will be controlled, and deployment will be closely monitored.”
Voice becomes the dominant interface for computing.
“By 2026, speaking will become the default way people interact with computers. Keyboards and screens won’t disappear, but they’ll no longer be the starting point. Voice will be faster, more natural, and increasingly tied to AI systems that carry memory and context.”
Alon Kaufman, CEO and Co-Founder at Duality Technologies
The machines will take over internal business functions – but humans will set the tone.
“Sophisticated agentic AI copilots will handle complete, multi-step business processes—seamlessly pulling, transforming, and sharing data across fragmented enterprise platforms like CRM, ERP, finance, and HR, then securely communicating insights to other specialized agents. This shift in operational execution will amplify the human role. People will focus on what AI cannot: setting strategic intent, auditing for alignment and ethics, and managing complex exceptions that require judgment. Built-in data standards and agent communication protocols will ensure humans maintain meaningful oversight even as machines handle the heavy lifting.”
AI agents will be policed by…predictive AI agents.
“The rise of autonomous AI agents demands an equally autonomous oversight system—one that operates at machine speed with predictive capabilities. Autonomous Governance Modules (AGMs) will monitor, audit, and predict the actions of operational AI agents in real-time, catching compliance breaches and ethical risks before they occur. Privacy-enhancing technologies and synthetic data will provide the safe, high-fidelity testing environments these governance agents need, ensuring AI models act responsibly without exposing sensitive information. Trust will not be built through human oversight alone, but through AI systems keeping other AI systems in check.”
Sohrob Kazerounian, Distinguished AI Researcher, Vectra AI
The Impact of Questionable GenAI and LLM Usage
“As organizations rush to bring AI into every aspect of labor-intensive work, calls to understand and implement solutions in a more careful and responsible manner will be drowned out by short-term efficiency gains. For example, one of the fastest-growing application areas of GenAI is in the domain of programming, where new models, tools, and workflows are constantly being invented and adopted. Despite being rather impressive, coding agents and AI largely generate bloated and inefficient code relative to more robust and elegant solutions of an even half-decent human developer.
“The near-term cost-savings of using GenAI over human programmers outweigh the potential long-term risks that may arise from over-reliance on AI workers. Over time, code will become increasingly difficult to understand or debug by anything other than an LLM, and organizations will not be prepared to respond to challenges that result from short-term thinking.”
Jin Kim, CEO and Co-Founder of XCENA
Servers evolve toward memory-driven design.
“In 2026 and beyond, CXL will redefine how hyperscalers think about system architecture. By breaking free from the fixed memory limits of DIMM slots, servers will no longer need to scale through additional nodes. Instead, capacity will expand seamlessly through CXL channels, unlocking a new era of memory scalability. As near-memory processing matures, we’ll see even greater efficiency in data movement, but the real transformation lies in how CXL enables elastic, memory-centric infrastructure.”
Data movement becomes the real limiter.
“Data movement becomes the critical bottleneck. As memory capacity grows, you can’t efficiently shuttle all that data back and forth to the CPU or GPU. The bandwidth requirements become unsustainable. That’s why we’ll see more focus on processing data closer to where it resides – reducing the amount that actually needs to move across the system. The architecture has to evolve beyond just adding more capacity.”
John Kim, CEO of Sendbird
The AI funding bubble will burst when short-term investors exit.
The math is simple: $200 billion invested in a single year must produce multiple trillion-dollar companies within five years – outcomes that historically take decades. Investors are being given 48 hours to decide whether to invest tens of millions into a company. That time compression tells you everything about a potential bubble. When your time horizon shrinks from long-term value creation to just the next fundraising cycle, you’re no longer a value investor. Here’s the math problem: $200 billion invested in AI needs to generate multiple trillion-dollar companies within five years for the economic model to make sense. This has historically never happened, usually taking multiple decades to achieve. When short-term investors move out, the demand shrinks drastically, signalling the burst of a bubble. We must show AI has created real value before that happens to dampen any potential rise of skepticism in AI investments.”
John Kindervag, Chief Evangelist at Illumio
Ditching the cloud, moving data back to data centers.
“In 2026, enterprises will begin migrating select workloads and sensitive data from the public cloud back into their own data centers. The ‘trillion-dollar paradox,’ as Andreessen Horowitz described it, is forcing business leaders to face a hard truth: the cloud’s convenience often hides long-term cost and control tradeoffs. The agility that once justified the cloud premium has become a drag on profitability. We will see more organizations move back to the data center because of the fear that the data entered into the cloud will be consumed by public LLMs. A number of organizations have private LLMs to do their AI work on-premises.
“Customers want tighter control over sensitive data and less exposure to cloud outages or the risk that public large language models will ingest proprietary information. The next phase of cloud adoption will look more balanced. Companies will keep what makes sense in the cloud and bring home the workloads that do not. Many will take a hard look at what they are paying for and what they gain in return, then move critical systems back into environments they can fully control. This shift will create more hybrid models that help organizations cut waste, tighten security, and make more informed decisions about where to store their most sensitive data based on cost, performance, and regulatory needs.”
Kevin Kline, Sr Staff Technical Marketing Manager at SolarWinds
Deep Human Experience: The Key to AI Adoption in 2026
“In 2026, we will see a growing emphasis on expert-led training for successful AI agent adoption. Effective AI requires deep human experience, which will require companies to hire Subject Matter Experts (SMEs) to train their systems. This approach, already proven in fields such as law and radiology, has enabled AI to match the quality of human experts and is now being applied to IT and observability. Integrating SME-level knowledge into observability platforms will unlock significant advances in self-diagnosis and self-healing.”
The Next Era of IT: From Anomaly Detection to Actionable Insights
“Today’s IT environments are more complex and interdependent than ever. Analyzing your IT estate as a collection of interconnected systems is vital to avoid significant risks, major outages, and poor customer experiences. In 2026, we will see observability tools evolve beyond simply detecting anomalies to answering the real question IT pros ask, like: ‘What should I fix first?’ We are entering a new and exciting era for enterprise IT where platforms will expand to provide a complete and integrated workflow that supports detection, diagnosis, remediation, and post-resolution follow-up.”
Ed Klotz, Senior Mathematical Optimization Specialist at Gurobi Optimization
“In the coming year, GenAI will expand the number of people who can use mathematical optimization in their businesses and make it easier for optimization professionals to explain the ROI of optimization solutions. This improved explainability will create a common understanding between all parts of the business, building trust and acceptance around optimization solutions and use cases. Enhanced explainability will also extend beyond optimization to include the comprehension of machine learning predictions, the answers and recommendations provided by GenAI solutions, and how these insights all work together to positively impact the business.”
Harald Kroeger, President of Automotive Business at SiMa.ai
The role AI plays in shifting mentalities from process to product.
“We’re witnessing a shift, that it is no longer only about AI-defined products, but increasingly about AI-defined processes – an all-or-nothing transformation demanding fundamental change rather than incremental improvements that no longer suffice.
“In 2026, this shift will accelerate as AI becomes indispensable in industries where failure is not an option. The defense industry, for example, is rapidly embracing AI as drones evolve into essential tools, while in manufacturing, AI is enabling near-perfect production with minimal waste. This convergence of human craftsmanship and machine marks a new paradigm where intelligence lives within the product itself, not just in distant data centers. Machines will become far more interactive, intuitive, and collaborative, ushering in a new era of seamless human-AI partnership, we call it ‘the ghost in the machine.’
“For the first time, multimodal AI models will operate directly inside products such as robots, vehicles, industrial systems, and medical devices. This shift is enabled by low-power Physical AI platforms that can execute complex pipelines on the edge, allowing machines to perceive, reason, and act without reliance on the cloud. As a result, 2026 will mark a turning point where the defining features of a product are no longer mechanical or digital add-ons, but the intelligence embedded within it.”
Jonathan LaCour, CTO of Mission, a CDW company
In 2026, AI Will Scale Enterprise IT Through Services as Software.
“The concept of ‘services as software’ is going to accelerate in 2026. Traditionally, delivering services required people, which limited scale and consistency. However, now AI agents can handle tasks that previously required manual intervention, enabling enterprises to integrate service delivery directly into their software workflows. This not only improves efficiency and customer outcomes but also reshapes how partners and channels operate. We’re moving toward a model where intelligent agents can support, monitor, and even troubleshoot software in real-time, scaling service delivery in ways that weren’t possible before.”
Neil Lawrence, Professor of Machine Learning at the University of Cambridge
LLM Innovation Will Shift From Model Size to Orchestration.
“LLMs are already ‘good enough’ for most real-world applications; the next breakthrough won’t come from bigger models but from better systems to manage, combine, and control them. In 2026, the real innovation will be in orchestration layers: agent frameworks, reliability tooling, safety guardrails, and workflow automation rather than core model capability.”
Skip Levens, Product Marketing Director at Quantum
“After the industry’s rush of AI experimentation, organizations are now waking up to the chaos they’ve inherited: fragmented tools, stovepiped knowledge bases, disconnected workflows, and probably ungoverned data, duplicated effort. In 2026, successful teams will move from ‘try everything’ to ‘train with purpose.’ The winners will treat AI enablement like any other corporate discipline by building internal sandboxes, AI offices, and shared learning programs that balance curiosity with clear business goals to advance the company’s mission and better connect with its customers. The goal isn’t to slow innovation; it’s to stop mistaking activity for progress.”
Itzik Levy, CEO at vcita
AI handles business growth, not just the execution.
“In 2026, AI won’t just draft messages or execute tasks occasionally. It will become a routine part of business operations by closing loops such as answering calls, scheduling multi-step services, issuing estimates, collecting payment links, and writing structured CRM updates, amongst many others. The design pattern shifts from “assistant that writes” to “agent that resolves,” measured by tasks completed (bookings, paid invoices, verified leads) rather than just the quality of its output. Expect SMB suites and service bundles to implement out-of-the-box flows (e.g., quote → calendar → pay → receipt) with guardrails and audit trails to satisfy AI governance and SMB skepticism.”
The end of the disjointed tech stack: Forced consolidation by enterprises.
“Driven by the overwhelming challenge of ‘app fatigue’, where a majority of SMBs are using 6+ core apps and 91% want an all-in-one solution, major enterprise service providers (Telcos, Banks, Payment Processors) have mainly stopped offering fragmented, à la carte partner ecosystems. The 2026 competitive edge will continue the shift from providing the most individual apps to offering a single, AI-powered “Unified Business OS” bundled as a core utility. These platforms integrate scheduling, payments, billing, basic marketing, and other business operations under one provider’s brand, transforming them into the default software vendor for their SMB clients. This strategic move significantly reduces SMB friction and makes it nearly impossible for a standalone SaaS vendor to compete on the core utility level.”
Jon Lucas, Director and Co-founder of Hyve Managed Hosting
After the AI Hype: Rebuilding Trust Through Human Oversight and Responsible Design.
“It seems like in 2026, the AI hype bubble might begin to burst, and that might be exactly what the industry needs. AI has become an increasingly prevalent tool in decision-making, operations, and innovation across various industries, but a dangerous overdependence is forming. The reality is that AI delivers outputs with confidence, but it’s not always right. It lacks contextual judgment, interpretive nuance, and, most importantly, accountability. Too many early-stage companies are building on AI from day one without the human overnight or extensive cloud infrastructure needed to make it scalable and secure. This poses both a technical challenge and a governance issue.
“The future of AI lies in balance: embedding humans at the helm, with oversight from data scientists, ethicists, and infrastructure experts, rather than relying solely on AI developers. Left unchecked without a human layer of accountability, overreliance on AI will expose companies to risk, compliance breaches, and reputational damage. The cloud sector, in particular, is reaching a crossroads. We envision the future as one where AI is a powerful tool, but not the ultimate decision-maker. The bubble’s burst might be a chance to recalibrate the industry, focusing on human judgment and critical thinking as the foundation for faster, better, and more secure systems. It’s not about abandoning AI; it’s about using it responsibly and sustainably with human insight as the real source of truth.”
David Maffei, SVP & GM at Staffbase
Anticipatory AI becomes the new workflow engine.
“AI will shift from reactive Q&A to proactively surfacing insights, decisions, and action items before employees even go looking. This push-first model will streamline workflows and reduce friction in ways legacy tools never could.”
A conversational interface becomes employees’ primary gateway to work.
“Chatbots, voice assistants, and multimodal AI interfaces will replace cluttered inboxes and outdated intranets, giving employees natural-language access to updates, documents, and decisions with no searching required.”
Intelligent content maintenance finally eliminates outdated information.
“AI systems will continuously scan, flag, and refresh internal content, putting an end to stale intranet pages, version confusion, and manual ‘information cleanup.’ This shift will dramatically boost accuracy, trust, and compliance.”
Gord Mawhinney, President of the Americas at Avanade
How challenger organizations become AI Frontier firms and will compete (and win) with smarter AI adoption in 2026.
“In 2026, Mid-market organizations are poised to gain real ground in the AI race. How will they break through? By adopting advanced technologies more quickly and strategically than their larger competitors. Mid-market companies often lack resources and governance structures, but they can benefit from their agility and fewer legacy systems slowing them down. This combination will set the stage for them to emerge as true challengers, able to compete with enterprise giants by deploying AI with greater speed and creativity.
“By harnessing data, using pre-built AI solutions to boost productivity, partnering with trusted experts, and differentiating through value and better customer experience, these companies are poised to turn their constraints into clear advantages. This will be especially evident in retail and healthcare, where high-impact personalization and intelligent supply chains are the key to driving growth. In the year ahead, mid-market organizations will seize the moment and become AI Frontier firms, not by mimicking enterprise models, but by competing through their unique adaptability and smarter AI execution.”
Sydnee Mayers, Product Lead at Cribl
Enterprise technology will see an explosion of agentic AI usage in 2026 across every industry.
“Agentic AI will act as teammates and collaborators, aiming to increase the efficiency of every human operator by 5x or more. Enterprise platforms and products that do not offer native agentic AI experiences will lose market share and leadership to more nimble competitors.
“I would also love to see Generative AI become available at the edge, enabling devices as small as mobile phones to be completely AI-enabled without an internet connection. If we are able to bring the capabilities of LLMs to handheld devices effectively, it will completely upend hardware, software, and consumer-focused industries that rely on a captive audience for competitive advantage. Just think, if my cellphone can do everything from answer questions intelligently to manage complex tasks, why would I need additional devices or even cloud infrastructure?”
Ross Meyercord, CEO at Propel Software
Agentic AI ends the era of standalone software.
“Next year will mark the tipping point for connected intelligence. Software platforms that extend data and workflows across the enterprise will dominate, while isolated tools will fade into irrelevance. Agentic AI is already proving that productivity breakthroughs come from collaboration, between systems as much as people. The next generation of AI agents won’t live inside individual apps. They’ll communicate, coordinate, and act across entire tech ecosystems, turning fragmented processes into fluid, intelligent networks. In this new era, standalone software will simply be unable to compete. The demise of remaining on-premise software will accelerate, leaving just 15 percent of those companies over the next three years.”
Silicon Valley becomes the next Hollywood for agents.
“Managing AI agents is the hottest gig in town. From picking the right AI for the right job to connecting who’s who to get the best data, only the most optimized queries at the best price will win out. But 2026 will prove that omniscient agents do not exist. Domain-specific agents will emerge as clear winners as users rely on tribal knowledge and industry expertise to propel business. As a result, companies will be investing in service terms to revolutionize how AI is monetized over the next 12-18 months. Buyers will flock to AI services that deliver domain expertise in areas like legal, finance, and healthcare, leveraging best practices to fulfill productivity and efficiency gains.”
SaaS is far from dead; its resurgence coexists with AI agents.
“In 2026, the winners will be those who combine the agility of AI agents with the reliability of SaaS to deliver measurable business value. SaaS brings the workflows, governance, and guardrails that enterprises demand, while AI agents extend productivity and speed. One without the other falls short, but together, they set the new standard for enterprise software.”
Lee McClendon, CDTO at Tricentis
2026 Will Expose That Many Organizations Still Lack Quality Fundamentals.
“Despite the rapid pace of innovation, recent industry incidents show that many organizations still haven’t nailed the basics. Just this November, major websites like ChatGPT and X went down for hours due to an overlooked Cloudflare configuration issue. Our own data indicate that two-thirds of organizations were at risk of a software outage this year, with roughly a third of teams citing communication gaps or unclear quality metrics as barriers to quality. As generative and agentic AI enters more pipelines, the cost of rushed or poorly tested releases will escalate dramatically.”
Matt McLarty, Chief Technology Officer at Boomi
In 2026, Governance Will Define the Future of Enterprise AI
“In 2026, the defining feature of successful enterprise AI will be robust data governance. As organizations shift from pilot projects to deploying thousands of autonomous agents, companies that excel will be those that embed control, compliance, and quality management into every layer of their AI systems. Every AI agent will need to be governed by clear rules and oversight from dedicated data stewards. Regulatory compliance and ethical considerations should be built into workflows, positioning businesses to adapt quickly as new laws emerge and to make decisions that are fair and transparent. Comprehensive tracking of AI actions and data access will create auditable records, enabling swift detection and resolution of errors. In this environment, companies that prioritize data governance will harness the full power of AI, achieving scale and innovation without sacrificing trust or accountability.”
In 2026, the Cloud Race Will Shift From Power to Responsibility.
“By 2026, cloud computing will reach an inflection point defined by two opposing forces: the exploding energy demands of AI and the strategic shift toward decentralized data architectures. The cloud’s next battleground will be sustainability. The race for more computing power is starting to hit real limits. Energy costs, carbon impact, and new data regulations are forcing companies to rethink what growth truly means. Data centers are now among the world’s fastest-growing energy consumers, prompting providers to compete on more than just performance, but also on efficiency and transparency. The organizations that thrive in 2026 will treat sustainability, sovereignty, and auditability as pillars of their cloud strategy and, in turn, balance centralized power with distributed control to build systems that are intelligent and responsible.”
Alex Merced, Head of DevRel at Dremio
AI-Native Lakehouses Will Become the Standard Architecture.
“By 2026, the lakehouse will no longer be an emerging pattern; it will be the default. Organizations will demand platforms that natively support AI and analytics at scale. These modern lakehouses will integrate open table formats, vector search, and retrieval capabilities as core features, allowing them to handle both structured and unstructured data within a single system. It will also enable real-time, AI-driven use cases like retrieval-augmented generation (RAG) and intelligent applications without the need for duplicated infrastructure.
Generative AI Will Redefine Self-Service Analytics.
“Generative AI will transform how business users interact with data. Natural-language querying, voice interfaces, and intelligent discovery will allow non-technical users to access insights on demand, without needing SQL or data engineering support. In 2026, semantic layers and intelligent data catalogs will play a pivotal role in interpreting intent and ensuring that users access trusted, governed data. This shift will empower business teams to make faster decisions, while freeing data teams to focus on higher-value innovation.”
DataOps Will Be Automated by AI, from Pipeline to Governance.
“AI will revolutionize DataOps by automating everything from pipeline creation to metadata enrichment. Generative tools will produce ETL logic, detect anomalies, and recommend fixes in real-time, significantly reducing the manual overhead of data engineering. Simultaneously, AI-powered governance and FinOps tools will proactively optimize performance, cost, and compliance. As a result, in 2026, data teams will spend less time firefighting and more time driving strategic value across the business.”
John Morris, Chief Executive Officer at Ocient
AI overconfidence in 2025 leads to a reality check in 2026.
“In 2025, enterprise adoption of artificial intelligence surged, yet so did overconfidence. Despite bold claims of AI readiness, many organizations unknowingly built their AI strategies on shaky ground. The rush to deploy AI solutions, driven by competitive pressure, outpaced the development of resilient data architectures and governance, resulting in a wave of initiatives built on incomplete or unvalidated data pipelines.
“Similar to the dot-com bubble, 2026 will bring a more pragmatic reset to data and AI strategies. Organizations that attempt to layer AI on top of technical debt, fragmented data ecosystems, or poorly governed systems will face operations expenditures (OpEx) challenges, while forward-looking enterprises will shift spending toward strengthening data integrity, security, and performance to ensure their environments can support AI at scale.
“The gap between entrant-level AI adoption and truly enterprise-ready AI architectures will become impossible to ignore. In the year to come, organizations will recognize that sustainable, high-performing AI depends less on model choice and more on the quality, governance, and scalability of the data foundation supporting those systems.”
The AI talent gap begins to close as 2026 curriculums catch up.
“In 2025, a surprising disconnect defined the AI hiring landscape. While a flood of graduating software engineers entered the market, many companies struggled to find candidates with the specialized AI expertise needed to drive their most ambitious initiatives. This resulted in a paradox: surplus talent alongside unfilled roles, highlighting a gap between academic output and demand from the enterprise.
“That mismatch won’t go unaddressed for long. In 2026, top universities and technical institutions will further evolve their curricula, infusing core programs with AI-first thinking, hands-on model development, data infrastructure training, and cross-disciplinary applications. This shift will be driven by industry pressure, funding incentives, and student demand, and will help close the AI skills gap at its source.”
Dinakar Munagala, CEO and Co-Founder of Blaize
Practical AI Will Shape How Organizations Deliver Real-World Outcomes in 2026.
“The next phase of artificial intelligence will be shaped by Practical AI. This is the shift from experimental systems to intelligence that delivers reliable outcomes in the physical world. In 2026, organizations will face growing pressure to move beyond pilots as they seek efficiency, resilience, and measurable impact. AI that works only in controlled environments will no longer be enough. The leaders of this next era will focus on systems that adapt to real conditions, operate responsibly, and deliver value at scale.
“Practical AI is already changing how industries function by bringing intelligence closer to where data is created. Cities are utilizing localized analytics to enhance mobility and bolster public safety. Retailers are using real-time signals to avoid stockouts that quietly drain revenue. Industrial and defense teams are relying on on-device intelligence to make faster decisions in environments where power, bandwidth, and time are limited.
“In the year ahead, AI will move from a promising tool to a core part of digital infrastructure. Organizations that succeed will prioritize efficiency, adaptability, and business outcomes rather than model size or theoretical benchmarks. Practical AI will define this new phase by making intelligence dependable, accessible, and better aligned with the way people and systems actually work. It is how AI becomes something we trust in everyday life, not just something we test in isolation.”
Ariel Pisetzky, Chief Information Officer at CyberArk
For Many, AI’s Promise Will Remain Unfulfilled.
“We are witnessing a global economic arms race for AI hardware. The demand for minerals, manufacturing capacity, and advanced chips is intensifying, especially between the US and China. Tariffs and export restrictions complicate access to critical hardware, creating logistical challenges for organizations seeking to deploy AI at scale.
“For enterprises, these dynamics mean significant hurdles in building or scaling AI infrastructure. The scarcity of hardware, rising costs, and limited access to power and data centre space make it increasingly difficult for smaller organizations to compete. The giants—major technology companies—are securing their dominance by acquiring not only hardware but also entire power plants to ensure a stable energy supply for their data centres. This leaves smaller organizations reliant on expensive cloud services, often priced out of the market due to the giants’ ability to negotiate better terms and access proprietary accelerator hardware unavailable to others.
“The cost of running AI workloads in the cloud is prohibitive. For example, a server with GPUs can cost $350,000, while cloud GPU access can run to $7,000 per day, making sustained AI operations financially unviable for all but the most prominent players. This economic reality, compounded by the giants’ control over hardware and energy, means smaller organizations are increasingly priced out of the AI market. The promise of AI may remain just that for some: a promise.”
Molly Presley, SVP of Global Marketing at Hammerspace
The Year of the AI Factory—Where Efficiency Defines Intelligence
“2026 will be remembered as the year AI moved from experimentation to industrialization — the dawn of the AI Factory. Across industries, organizations will shift their focus from simply training bigger models to operationalizing intelligence at scale. The frontier will no longer be just about model size, but about how efficiently those models are fed, reasoned with, and deployed.
“The world’s computing capacity is now bounded by energy and data movement, not transistors. As a result, efficiency will become the new metric for AI progress — measured in tokens per watt, throughput per rack, and time to insight. Enterprises will realize that GPUs sitting idle due to data fragmentation or latency are not just a technical problem, but an economic one.
“In 2026, AI Factories will rise as the modern equivalent of industrial power plants—unifying data, compute, and automation into tightly orchestrated systems that transform raw information into actionable intelligence at unprecedented speed. These environments will blur the boundaries between cloud and on-premises, between inference and training, and between virtual and physical AI.”
Sovereign AI Will be a Driving Function of Infrastructure Decisions
“By 2026, organizations will increasingly pivot from relying on commercial APIs to deploying AI workloads on-premises. Security, compliance, and governance concerns will drive demand for AI environments built on enterprise infrastructure rather than public APIs. This shift ensures organizations retain complete control of their data, models, and intellectual property—a priority as generative AI moves deeper into regulated and mission-critical use cases.”
Marinela Profi, Global AI & Generative AI Market Strategy Lead at SAS
Trust overtakes performance as the #1 AI KPI.
“We’ve entered an era where users trust GenAI models more than explainable ones – not because they’re more accurate, but because they feel more human. In 2026, trust will become a measurable business metric. Companies will compete not just on what their AI can do, but on whether employees, customers, and regulators believe in it.”
Data readiness becomes the new competitive edge.
“The age of ‘more data’ is over, and the age of right data begins. Organizations that have mastered lineage, quality, and access will finally unlock scalable AI. Data will stop being an afterthought and become the foundation of agentic systems, enabling reliable automation and contextual decisioning.”
AI stops being a lab experiment – and starts being an economic one.
“2026 will mark a shift from proof-of-concept to proof-of-impact. CFOs will measure AI not by how much it automates, but by how much it improves decision quality. The ROI conversation will evolve from “How fast can we deploy?” to ‘How confidently can we decide?'”
Alekhya Reddy, VP of Product at Optera
“AI will reshape corporate sustainability in two distinct waves. The first (and most immediate) is in reporting and data organization. Companies are drowning in stakeholder requests and struggling to get data in the right format. AI will eliminate hours of manual work here. But once that operational foundation is solid, we’ll unlock the real power: predictive modeling, scenario analysis, and understanding where companies are actually headed with their climate commitments. You can’t do sophisticated forecasting until you have the basics automated, and 2026 is when we’ll start seeing companies make that transition from the first stage to the second.”
Jagan Reddy, Founder & CEO of RightRev
Automation will become mission-critical for companies operating under the EU Data Act.
“The EU Data Act, which went into effect in September 2025, will become deeply embedded in SaaS financial models, forcing a permanent shift in how companies recognize revenue. The Act empowers customers with the right to cancel any fixed-term contract with just two months’ notice, undermining long-term commitment as a foundation of SaaS predictability. That change means SaaS providers should rethink their discount strategies. Upfront multi-year discounts can no longer be assumed ‘earned,’ because if a customer exits early, providers will need to reprice the used portion of service at a higher, monthly rate and reverse previously recognized revenue.
“Forecasting becomes more complex; churn risk grows, refund liabilities rise, and traditional metrics like net and gross retention may fluctuate more. To stay ahead, companies will lean heavily on automated revenue recognition systems that can detect cancellations, recalculate discount forfeitures, reprice past service, and update forecasts in real-time. In short: 2026 will mark a turning point where automation and agile RevRec become mission-critical for SaaS businesses operating under the EU Data Act.”
Usage and consumption-based pricing models will continue to gain traction in 2026, forcing a modernized revenue recognition process.
“The rapid adoption of usage- and consumption-based pricing models, largely brought on by the prevalence of AI applications, will push finance teams toward more dynamic, real-time accounting than ever before. As companies shift away from fixed subscriptions toward pay-as-you-go structures, revenue will increasingly be tied to actual customer behavior rather than predetermined contract values.
“In 2026, revenue recognition teams will grapple with more frequent recalculations of transaction price as usage spikes, dips, or deviates from forecasts. Performance obligations will also become more granular, tied to discrete units of consumption rather than broad service periods. This added complexity will make manual revenue processes impractical, especially for businesses with high-volume usage events across thousands or millions of customers. Ultimately, 2026 will be the year usage-based pricing forces a new revenue recognition operating model: one grounded in automation, continuous measurement, and high-fidelity consumption data. Companies that modernize will thrive; those that don’t will struggle with accuracy, audit pressure, and predictability.”
Rob Reid, Technical Evangelist at Cockroach Labs
“Resilience will replace performance as the defining benchmark. The question won’t be ‘how fast can your database run in ideal conditions?’ but ‘what does it look like when everything breaks?’ Stress tests under outages, blackouts, and cascading failures will become the new industry yardstick.”
Cassius Rhue, VP of Customer Experience at SIOS Technology
Hybrid and Multicloud Strategies Gain Momentum
“Hybrid and Multicloud solutions have become a more proven option to help organizations balance performance, cost, and resilience while avoiding vendor lock-in. More enterprises will continue to consider and adopt hybrid and multi-cloud architectures in 2026. As a result, HA solutions that can seamlessly operate across diverse infrastructures will become indispensable to modern IT strategies.”
Continuous Availability: The New Foundation for Trusted AI
“AI and ML workloads will run more frequently on distributed clusters and GPU-intensive systems, where downtime creates costly disruptions. In 2026, IT admins will demand high-availability solutions that simplify complex AI stacks and expose full visibility into data, storage, and node health. Continuous availability becomes a prerequisite for AI reliability and trust.”
Manny Rivelo, Chief Executive Officer of ConnectWise
The Year of Practical AI for SMBs and MSPs.
“As we head into 2026, we’ll see the managed services and small business ecosystem move from talking about AI to truly applying it. Currently, most organizations are experimenting—dabbling in one or two use cases that simplify operations or enhance customer experience. However, the real breakthrough will come when vendors make AI accessible: packaging solutions in ways that don’t require deep technical expertise. MSPs and SMBs are facing a talent crunch, and expecting them to hire specialized AI roles overnight isn’t realistic. Instead, success will hinge on simplicity—delivering clear value without complexity.
“In the year ahead, there will be a major shift toward ‘AI in a box’ solutions: ready-to-adopt tools that help SMBs automate repetitive tasks, strengthen cybersecurity posture, and gain actionable insights without needing to reinvent their operations. The partners and service providers who embrace this mindset early—focusing on practical, consumable AI rather than abstract innovation—will define the next wave of business growth. The opportunity is massive, but only if we make AI understandable, attainable, and valuable for every business, not just the largest ones.”
Josh Rogers, Chief Executive Officer of Precisely
Infrastructure may be the hottest AI investment, but data will only accrue in value.
“Companies are pouring billions into AI infrastructure to meet the capacity demands of the AI moment. However, we’re only just starting to see some of these same companies consider the data that will reside in that infrastructure. In 2025, we saw several high-profile acquisitions of data players, as top enterprises look for competitive differentiation. 2026 will further ignite the data industry consolidation, as the organizations that have invested in major infrastructure projects look to fill those data centers with high-quality, context-rich data to fuel their AI.”
Kevin Roof, Director of Offer & Capture Management at LiquidStack
Data center investors and operators will trade in the classic PUE metric for “tokens per watt per dollar.”
“Infrastructure buildout is beginning to shift the economics of AI, with data centers transitioning from cost centers to revenue generators. With this transition, metrics for success are shifting from sustainability and conventional efficiency toward revenue generation. The new, top-of-mind metric discussed in industry circles is “tokens per watt per dollar.” This new focus means it is no longer about simply using less energy, but about using energy as efficiently as possible. Since power constraints are the threshold preventing data center growth, organizations must use the power they have most effectively. Stranded power represents lost revenue.”
Jaime Schultheis, Head of Global Data Partnerships at Bombora
DaaS Will Define Publisher Survival.
“2026 will be the year publishers fully embrace their evolution into full-fledged Data-as-a-Service companies. The rise of AI-driven search and generative content has triggered a dramatic collapse in site traffic, eroding the value of traditional ad models and forcing publishers to reimagine their business foundations. The most forward-thinking publishers are transforming into data engines, leveraging onsite behaviors, first-party signals, and contextual engagement to build scalable, compliant, and high-performance DaaS offerings.
“Readers are no longer just an audience—they’re a signal. And in this new era, success belongs to those who can prove they know who’s in-market, what they care about, and when they’re ready to act.”
Trevor Schulze, CIO at Genesys
CIOs Will Turn Generative AI Failures into Agentic AI Advantages in 2026.
“After a year of rapid experimentation with generative AI, many enterprises struggled to tie the technology to financial results, with around 95 percent of pilots failing to drive revenue acceleration. But those early efforts weren’t wasted. They gave CIOs critical insight into what it takes to build the right foundations for the next phase of AI maturity. The organizations that rapidly apply those lessons will be best positioned to capture real ROI.
“In 2026, I expect CIOs to focus on four strategic areas: context engineering, trusted data foundations, agent orchestration, and AI governance. Designing systems that deliver the right information at the right time will be essential to unlocking the potential of agentic AI. These shifts will move AI from experimentation to execution and unlock measurable business value at scale.”
Andrew Sellers, VP of Technology Strategy and Enablement at Confluent
For Smarter AI, 2026 Will Be The Year of Context. 2024 was the year of RAG.
“2025 is the year of autonomous agents, and 2026 will be the year of context. LLMs are inherently stateless, yet operational agents require deep, domain-specific memory for complex decisions. While AI frameworks have made standalone agents easy to build, the true differentiator lies in access to comprehensive, real-time knowledge about the enterprise.
“RAG excels with unstructured data, but it struggles to interpret vast amounts of structured data, where meaning resides in precise field values and schema. The industry’s next challenge is context management: building platforms that allow agents to query and absorb structured enterprise data, such as customer records or inventory status. In 2026, this ability to synthesize real-time, domain-specific context will separate good agents from indispensable ones.”
2026 Will See New Protocols for Multi-Agent Coordination and Metadata Exchange.
“Two critical standards are likely to emerge in 2026 as AI operations become autonomous. First, as single-agent systems evolve into complex multi-agent teams, the industry requires an orchestration protocol to manage how agents collaborate. Current frameworks handle individual agents well, but coordinating multiple agents—determining which agent leads, which executes tasks, and how they share results—requires a standardized approach to avoid custom coding for each implementation.
“Second, we need a comprehensive metadata standard to solve the structured data problem. Current metadata catalogs, like AWS’s Glue, Snowflake’s Polaris, and Databricks Unity, lack conventions for transferring metadata between platforms. Without this, data loses critical contextual information each time it moves between systems, undermining the governance agents require for trustworthy decision-making. As the industry continues to build out the technologies to enable operational agentic AI, it’s likely we’ll see these new protocols emerge sooner rather than later.”
Aron Semle, Chief Technology Officer at HighByte
“There are a few areas to watch in 2026. One of the top focuses will be the shift away from Large Language Models (LLMs) and towards Small Language Models (SLMs) that run locally. This will be reminiscent of DeepSeek’s disruption of the market as those SMLs continue to improve. 2026 will also be a year of ‘AI application,’ and we’ll see software vendors challenged to embed useful AI features specific to their products and domains, such as AI agents, especially as industrial companies focus more on AI value and less on hype. These product-specific, embedded AI agents will start to evolve into the new API, and agent orchestration and governance will come into focus as agents begin to ‘talk’ to other agents.”
Dave Shuman, Chief Data Officer at Precisely
“In 2026, semantics will be the most important AI governance guardrail. Training AI is akin to managing well-intentioned interns. AI models may be smart and capable, but like any agent – human or otherwise – they still require clear direction, oversight, and consistent evaluation. Adding a semantic layer transforms complex data into a business-friendly format that’s more digestible, helping AI interpret and translate data into reliable output. As AI conversations shift from implementation to purposeful action in 2026, leaders will prioritize the people and resources needed to build the semantic layer, to ensure that the input data directly aligns with the desired, measurable outputs.”
Oliver Steil, Chief Executive Officer of TeamViewer
The year of ‘Monday Morning ROI.’
“After years of conceptual talk about AI’s promise, 2026 will be the year that business leaders shift their attention to a far more practical question: what actual value does AI deliver on a Monday morning? The next phase of AI maturity will be measured not by research breakthroughs, but by daily relevance—the tangible impact on productivity, quality, and output that teams experience at work.
“Agentic AI is making this shift possible. Instead of generic, open-ended models prone to hallucinations, organizations are deploying specialized agents trained on company-specific data to perform focused, high-value tasks. Whether it’s an agent running hundreds of engineering simulations overnight or summarizing insights from customer service interactions, AI’s new utility is grounded in specificity. The broad ambition of the past few years is giving way to targeted efficiency, and 2026 will be the moment ROI shows up not in theory, but in the real world.”
Workflows start working back.
“For decades, people have had to adapt themselves to the way work is structured, navigating outdated processes and systems, and layers of manual steps that slow everything down. In 2026, that relationship flips. With the rise of agentic AI and continuous optimization, workflows will begin adapting to people in real-time. Tasks will automatically re-route based on availability, context, and urgency; administrative work will run silently in the background, and systems will coordinate themselves, allowing humans to focus on the parts of work that actually require judgment, creativity, and empathy.
“The most successful organizations will be those that recognize this shift early: work becomes something that shapes itself around people’s strengths rather than forcing everyone into the same process. Instead of humans working for the workflow, the workflow finally starts working for them. It marks the beginning of a more frictionless, human-centric era of productivity–one where employees feel less drained by the mechanics of work and more energized by the meaning of it.”
Julia Tarasenko, Chief Commercial and Strategy Officer at LabConnect
“AI is changing the way clinical trials are run, making them faster and more accurate. By automating everything from patient recruitment to sample handling, AI is reducing delays and errors. This means new treatments can reach patients much quicker, and smaller companies can now compete with Big Pharma. In the long run, this could dramatically lower the cost of drug development and make trials more accessible for everyone involved.”
Chrystal Taylor, Evangelist and Staff Product Marketing Manager at SolarWinds
“I think the biggest blind spot we’ll see in 2026 will come from the disconnection between IT teams. The blame game is still prevalent, and teams continue to use different tools with disparate data sets, making it challenging to fully collaborate. According to recent data, more than one in three DBAs (38 percent) say they’ve considered leaving their current role, with the top reason being poor management. Additionally, misunderstanding what our observability tools are telling us can also occur when we lack the necessary general knowledge to process the information properly. We’re on our way to human-readable outputs, but in IT, you still need to grasp the basic concepts of how things are connected and how they work towards the greater goal—for example, how the database contributes to the end-user web experience. Enhancing our own technical and interpersonal skills is critical to avoiding the blind spots created by these biases and games.“
Ban-Seng Teh, Executive Vice President and Chief Commercial Officer at Seagate Technology
“AI has made data the most prized asset in the digital economy, and that is necessitating a significant shift in enterprise computing that will dominate data center planning and investments in 2026. Nearly 75 percent of business leaders say they are making the transition from a ‘cloud-first’ to a hybrid model that blends public cloud, private infrastructure, and edge. Why? To strengthen security, power real-time edge apps, and cut costs while supporting a surge in AI-driven content. The bottom line: all data now has value; unlocking it requires a smarter, hybrid approach with IT infrastructure and data storage built for today and tomorrow.”
J.J. Thompson, founder & CEO of Spektrum Labs
Over the next five years, CISOs will take the driver’s seat when it comes to cyber insurance decisions.
“Cyber insurance coverage and claims are now directly tied to technical safeguards, and only the CISO can prove that they actually work. If a gap can void an insurance policy or deny a claim, someone must notice (and it won’t be the finance department or the insurance broker, as neither has access to the data). And buying ‘the best’ endpoint protection or backup in isolation won’t cut it. The next wave of resilience favors ecosystems where safeguards and coverage align, and proof is built in and continuously updated. So, CISOs will gravitate toward insurer, broker, and technology vendor combinations that unify proof, protection, and insurance policy into one seamless and simple flow.”
Kevin Thompson, CEO at Tricentis
Outpacing AI Will Force a Reinvention of Quality.
“The speed of AI-driven change has surpassed anything the business world has faced, and it will only continue to accelerate from here. Application updates have shifted from quarterly to weekly or daily, integrations are breaking more often, and AI-generated code is flooding environments faster than teams can validate it. Quality is dropping, rollbacks are rising, and organizations are realizing that the way they’ve built and tested software for the past decade simply cannot keep up. In 2026, this pressure will force a fundamental transformation toward autonomous, AI-driven testing and risk-based intelligence designed for AI-era velocity.”
The 90-95 percent Test Coverage Mindset Will Break Down.
“Next year, enterprises will move away from the traditional ‘just hit 90 – 95 percent of test coverage’ mindset. That approach is too slow, too manual, and often misses the 5-10 percent of code where the most change occurs–where real risk and dependencies reside. With 80 percent of QA budgets still spent on human labor, organizations will shift toward approaches that identify exactly what changed and what must be validated. 2026 becomes the moment when companies redesign their development and delivery processes around quality-first, AI-first, and automation-first principles.”
The AI ROI Breakthrough Will Come from Specific Intelligence.
“Enterprises have largely struggled to capture measurable ROI from AI because their focus has been on generic intelligence and LLMs that ‘know a lot about a lot,’ but are experts in nothing. In 2026, that changes. Companies will shift from experimenting with general-purpose AI to deploying applications built on specific intelligence, trained on the domain context required to execute tasks reliably and meaningfully. This is where businesses will finally see tangible ROI: in systems that understand their workflows and their data, not in generic chatbots or broad AI pilots.”
Ratan Tipirneni, CEO at Tigera
The Shift to Agent Workloads.
“In 2026, the emphasis in Kubernetes environments will move beyond traditional cloud native workloads and increasingly toward agent-based workloads. More organizations will deploy agents directly in their clusters, which will introduce new challenges for platform teams. They will need to manage both known and unknown agents, ensure each agent has an immutable identity, and enforce strict authorization rules so that only properly authorized users or systems can instruct an agent. API governance through systems such as MCP servers will become essential because these servers define which actions each agent is allowed to perform. Security, trust, and compliance controls will also become critical as organizations look for reliable ways to detect compromised or malicious agents. As agentic workflows grow, many companies will encounter problems they did not anticipate.”
The Return of the Service Mesh.
“Service meshes will make a strong comeback. Early excitement eventually gave way to disillusionment because sidecar-based architectures were difficult to manage. The introduction of ambient mode has simplified adoption by moving proxies to the node level. This reduction in complexity will encourage renewed adoption, and Istio ambient mode will likely become the most widely used service mesh technology.”
Ingress Gives Way to the Gateway API.
“Traditional ingress controllers, which have been the long-standing standard for Kubernetes traffic management, will begin to decline. Most organizations will start transitioning to Gateway API based solutions, with Envoy Gateway leading the shift. Gateway APIs offer clearer separation of responsibilities, better extensibility, and more consistent traffic management, which will make them the preferred choice for modern clusters.”
Marcus Torres, CPO at Quickbase
“In 2025, everyone was asking what AI could do. In 2026, the question changes: what does success actually look like? AI is finally moving from promise to proof—and the gap between hype and real impact will widen fast. The next wave won’t reward the fastest; it’ll reward the most prepared. The companies that win will treat responsibility, not speed, as their advantage. That means clean, connected data. Practical integrations that enhance existing processes instead of reinventing them. And governance that keeps people in control while letting innovation move safely. AI tools are incredibly capable, but capability without discipline is chaos. We’ve hit a moment where speed without control isn’t progress; it’s a fast horse on the wrong road.
“The real winners will be the ones that design for accountability from day one: building AI-first interfaces, portable code, and transparent systems that can trace every answer back to its source. The rise of vibe coding and agentic orchestration will make it easier than ever to build from intent, but it won’t replace judgment. Success will come from balancing autonomy with oversight, acceleration with assurance. The real measure of AI maturity in 2026 won’t be how fast you move—it’ll be how responsibly you do it. Because in the race ahead, trust is traction.”
Tom Traugott, SVP of Emerging Technologies at EdgeCore Digital Infrastructure
AI’s Next Leap: Expect Some Surprises in 2026
“In 2026, we’ll see the true ‘second wave’ of AI innovation, driven not by new algorithms, but by the arrival of massive compute capacity finally coming online. Over the past year, leading organizations like OpenAI have hinted at projects they can’t yet launch due to compute constraints. That’s about to change.
“The world’s largest super-scale clusters are only in their infancy, with several just beginning to come online. As they do, we’ll see breakthroughs that have been quietly waiting in the background, including new multimodal models, real-time video generation tools that go beyond Sora, and entirely new categories of AI services. Many of the business concerns around cost, scalability, and accessibility will start to ease as this infrastructure matures. Expect surprises–some truly magical–because, at this point, we still don’t know what we don’t know.”
Location Still Matters: Power, Proximity, and the Next Generation of AI Campuses
“In 2026, location strategy will once again define the winners in the data center race. While massive campuses are emerging in what some call ‘the middle of nowhere,’ proximity to both power and population centers is becoming increasingly complex, yet critical.
“The evolving rules around AI training and inference are putting new pressure on latency, making speed a deciding factor much like it was in the early days of search engines. I expect continued growth in regions such as Phoenix, Northern Virginia, and the Northeast, where proximity to key markets remains important. I also expect a surge of development in adjacent, power-rich areas like Wisconsin and Indiana, which are close enough to connect but scalable enough to host gigawatt-class facilities spanning more than 500 acres.
“The challenge is no longer finding land–it’s securing power. The ‘powered land’ heyday of the last 5-10 years is increasingly over, with interconnection and grid upgrade costs now materially exceeding land value. As the grid struggles to keep pace, natural gas will continue to serve as a crucial bridge to sustainable baseload solutions, such as geothermal and new nuclear. The new geography of AI infrastructure will be defined not just by space, but by speed and power.”
Tommy Vacek, Chief Technology Officer at Bloomerang
From Constraints to Catalysts: The Role of AI in the Future of Nonprofits
“For too long, ‘good enough’ has defined how nonprofits operate—doing remarkable work with limited tools. But AI is starting to flip that script. In the wake of ongoing funding uncertainty and the government shutdown, nonprofits are rethinking how to bridge widening resource gaps. By 2026, they’ll use AI not just to automate tasks, but to transform fragmented data and manual processes into personalized, high-impact donor experiences. This is where real opportunity lies: AI can help organizations strategically scale meaningful, high-touch interactions that were previously impossible due to limited resources. The nonprofits that succeed will be those who approach AI thoughtfully and transparently, leveraging human-centered intelligence to turn constraints into catalysts, enabling nonprofits to unlock greater impact, deeper trust, and ultimately, increased generosity.”
Cameron van Orman, Chief Strategy, Marketing Officer, and GM of Automotive Solutions at Planview
Move Over, AI Slop – MVG is the New Boardroom Buzzword
“As AI scales, minimum viable governance (MVG) will become a mainstream term across boardrooms in 2026. Enterprises will adopt continuous, risk-based oversight as standard as leaders demand governance that is lightweight, automated, and embedded in initiative delivery pipelines. The key will be to offer just enough oversight to ensure trust and compliance, without slowing AI-driven transformation. MVG will be seen as the only sustainable path forward.”
Srini Varadarajan, Chief Technology Officer at Inmar Intelligence
Transparency is the new data currency.
“As AI adoption accelerates, organizations that win will not be those with the most volume of data but those with the cleanest, most trustworthy data ecosystems. Transparency, security, traceability, and auditability will become competitive differentiators.”
Compliance as a catalyst for innovation.
“Compliance will evolve from a chore to a catalyst. In 2026, the most innovative organizations will treat regulation not as a constraint, but as a framework for building confidence with their stakeholders, protecting consumers, and advancing responsible data and AI use.”
AI’s next chapter: From Automation to Accountability
“The next evolution of AI in enterprise environments will be less about automation and more about explainability. Businesses that can make AI decisions understandable to their non-technical stakeholders will establish more trust with their customers and set the standard for ethical and effective innovation.”
Manasi Vartak, Chief AI Architect at Cloudera
“In 2026, AI adoption will continue to grow at a steady pace, despite predictions of a market slowdown. Enterprises will continue driving strong demand for both generative and agentic AI as they move beyond experimentation and pursue measurable ROI. The most critical challenge ahead will be connecting AI agents to enterprise data and context, a prerequisite for making these systems truly useful. Many organizations have already demonstrated agentic capabilities, but they must now turn those demonstrations into production-ready systems by overcoming persistent barriers related to data access, governance, security, and permissions.
“At the same time, the definition of Responsible AI will evolve further. As AI systems become increasingly complex, Responsible AI must address not only model bias and fairness but also end-to-end accountability, encompassing data handling and system behavior. Enterprises adopting agentic AI will need to implement stronger governance frameworks, much like security and compliance reviews in traditional software procurement. Effective management of these systems will require new capabilities such as agent registries, observability, and versioning of entire agentic workflows. While public models will continue to dominate in 2026, enterprise-specific adaptation through fine-tuning and model distillation will accelerate rapidly. I also expect voice AI to gain significant traction, emerging as one of the most transformative areas of growth in the coming year.”
Catalin Voicu, Cloud Solutions Engineer at N2W
The Dawn of AI-Powered Backup Automation
“2025 hinted at AI-driven backup automation, but the real revolution is coming in 2026. This year marks the start of backup systems that require much less human intervention. AI tools will evolve to truly understand your organization’s data ecosystem, learning the complex patterns of usage, compliance requirements, and operational needs. These systems won’t just store data. They will act like data management experts, deciding:
- What data truly needs backup
- The optimal frequency and retention for each dataset
- Smart archiving strategies
- Full compliance across GDPR, HIPAA, and emerging regulations
“Backups will anticipate your needs, and AI tools will protect your data before you even realize it. The future of backup is hands-off, smart, and proactive.”
Paul Walker, Field Strategist at Omada
Regulators will demand that autonomous agents become glass boxes, not black boxes.
“The EU AI Act and California’s transparency laws now mandate that organizations document every decision made by AI agents, justify their reasoning, and maintain complete audit trails of what systems agents accessed and what actions they took. High-risk AI systems must enable users to interpret outputs and understand how decisions were made. Translation: if your agent autonomously executes a transaction, fires an employee, or denies a loan, you’ll need to explain exactly why it made that decision in terms that regulators and affected individuals can understand. The age of ‘the AI decided’ as an acceptable answer is over.”
Adam Weinstein, Founder and CEO at Writ
Chat-based Analytics Help Interpret Dashboards, but Analysts Won’t Be Replaced.
“As teams shift toward beginning to implement AI into their data analysis, users should expect dashboards that are connected to user-friendly chat-based tools that feel alive and intuitive. These tools will help translate complex signals into a shared understanding, making it easier for even the most junior users to gain a thoughtful understanding of their data. These tools are unlikely to replace expert data analysts, though, as AI trust still lags the market, and many experts will still be able to reach a deeper level of analysis.”
Everything Everywhere; Data All-At-Once
“As companies continue to implement AI into their tech stack, what will set tools apart is their interconnectivity and ability to access information both in and outside the data warehouse. Tools will be judged on their ability to connect with as many data points within the tech stack, alongside their ability to manipulate it and provide workable insights.”
Pascal Yammine, CEO at Zilliant
Resilience Will Expose a Small-to-Mid-Market Gap
“The economic resilience in 2025 will give way to a crisis for smaller B2B and enterprise companies in 2026. These companies, lacking the budget and resource agility of their larger enterprise counterparts, will struggle to quickly adjust pre-negotiated sales agreements amid ongoing volatility stemming from tariffs, the government shutdown, labor strikes, and other factors. CFOs at these companies will recognize that the ability to swiftly align customer preferences with market forces will be the best lever when facing budget constraints.”
The Barrier to AI Adoption Shifts from Technology to Trust
“The key bottleneck to realizing truly autonomous agents in 2026 will not be the technology itself, but a combination of cultural resistance and systemic data quality issues. Leaders will be forced to shift their investment from building AI to securing, validating, and governing their data sources. Due to anxiety around things like source manipulation and ownership of code, more companies will make on-site/private-cloud data governance a strategic differentiator.”
Tendü Yoğurtçu, Chief Technology Officer (CTO) at Precisely
The Oasis in the Data Drought: Geospatial Data Will Be Key in 2026.
“As we move into 2026, geospatial data will play an increasingly critical role in AI training, shaping how systems perceive, interpret, and interact with the world around them. The current reality is that large language models are trained on publicly available data, information that is finite in volume and often limited in accuracy and representation. This emerging “data drought” risks slowing innovation but also presents a strategic opportunity to unlock value through proprietary and curated data.
“Geospatial intelligence, including satellite imagery, GPS coordinates, and other location-based insights, introduces a new dimension of context. It helps fill information gaps where data is incomplete, offering a more objective, complete, and verifiable view of real-world conditions. When combined with an organization’s own proprietary data, such as customer information, transaction patterns, or operational signals, geospatial data creates a powerful foundation for differentiated insights and lasting competitive advantage.”
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William Jepma
Writer, Editor, and Analyst
William Jepma is an editor, writer, and analyst at Solutions Review who aims to keep readers across industries informed and excited about the newest developments in Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Business Process Management (BPM), Marketing Automation, and other WorkTech categories. You can connect with him on LinkedIn or reach him via email at wjepma@solutionsreview.com.
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