Why AI Tools Fail Without Orchestration—And How Leaders Are Fixing It

Brandon Sammut, the Chief People & AI Transformation Officer at Zapier, examines why AI tools fail without orchestration, and then explains how leaders are addressing the issue. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

There’s a common reason why AI feels both promising and frustrating: most people still use it outside the systems where their work actually happens. The tools themselves are capable, but they often operate in isolation. In practice, AI shows up as a set of helpers rather than as part of a coordinated system. For example, a sales rep uses ChatGPT to draft an outreach email, personalizing it with context copied over from a CRM, and then pastes the final draft into Gmail. Or an engineering manager runs meeting transcripts through an AI tool, switches to another app to turn those notes into action items, and then re-enters those tasks into a project tracker so their team can see them.

AI speeds up individual steps, but the work still depends on people manually stitching everything together. That gap is where momentum gets lost. Even when AI produces useful outputs, the systems that move work forward still rely on manual handoffs. Over time, those handoffs become the bottleneck.

For years, automation tools helped reduce that friction by connecting apps and triggering actions: “If this happens, then do this.” As AI becomes part of everyday workflows, another layer is taking shape: AI orchestration.

From Automation to AI Orchestration

Traditional automation works best when inputs are predictable and outcomes are clear. AI introduces a different set of conditions: they’re often unstructured, context-dependent, and open to interpretation. What should happen next might depend on tone, urgency, sentiment, or confidence level. Sometimes the output can move forward automatically; other times, a human needs to review it before anything happens.

That shift creates a gap. AI can generate useful work, but it doesn’t reliably decide how that work should move through the systems teams depend on every day. Without something coordinating those next steps, the burden falls back on people to interpret outputs and push work forward manually.

AI orchestration brings decision-making into the workflow itself. It defines when AI should run, what information it should use, where the output should go, and what happens next under specific conditions.

Why AI Struggles to Scale Without Orchestration

AI often delivers useful outputs, but usefulness alone doesn’t lead to scale. Turning individual successes into something teams can rely on across roles, workflows, and systems takes more than strong models.

In many organizations, the work around AI remains manual. Notes need to be copied into shared systems. Notes still need to be copied, drafts still need to be logged, and insights still need to be turned into action by hand. As usage grows, those manual steps multiply, and progress slows because the work around the AI never really goes away.

That slowdown is clear at the enterprise level. Nearly half of enterprise leaders (46 percent) say integration complexity and system sprawl are the biggest barriers to making AI work in practice, ranking higher than budget constraints or model quality. Experiments continue, but each introduces another handoff, making scaling harder than expected.

This is why so many AI pilots stall. Teams can prove value in a pilot. But without a consistent way to connect AI outputs to the systems that run day-to-day work, those pilots are difficult to repeat, measure, or trust at scale. AI orchestration is what turns those experiments into scalable systems.

What AI Orchestration Looks Like in Everyday Work

At its core, AI orchestration reduces the number of times someone has to stop and ask what should happen next. Take meeting follow-ups. A team records a call, an AI generates a summary, pulls out decisions and action items, and logs those tasks in the team’s project management tool. The notes don’t live in a one-off document that someone has to remember to check. They show up where work gets tracked.

Or consider customer feedback. Incoming tickets and survey responses are analyzed for themes and urgency, then routed to the right teams and logged in a shared system. Patterns surface over time without someone manually reviewing hundreds of entries. Each step is small, but together they remove enough friction that AI becomes something teams can rely on rather than something they have to manage.

How Teams Are Approaching AI Orchestration Today

Some organizations build orchestration directly into custom software. That approach can work in tightly controlled environments with stable requirements and dedicated engineering support. But for teams operating in fast-changing environments, this is difficult to maintain. That’s why many teams turn to AI orchestration platforms instead. These platforms sit between AI models and business tools, handling data flow, decision logic, and follow-through so teams can adjust and scale workflows without involving engineering for every change.

Rather than supporting isolated automations, AI orchestration platforms help teams shape the systems behind their day-to-day work. Because they connect with popular business tools, teams can also layer AI into workflows they already run, maintaining reliability as those workflows span people and systems.

AI is also woven into those workflows where context, judgment, and next steps need to be handled thoughtfully. Teams can use AI to extract structured data from unstructured text, classify incoming requests, summarize conversations, and generate drafts. The AI orchestration platform manages what happens next: which systems are updated, who needs to review the output, and whether the workflow continues automatically or pauses for human input.

In practice, teams see the most impact when they start with workflows they already run frequently. Over time, those workflows evolve. Logic becomes clearer. Exceptions are handled automatically. What begins as an experiment becomes part of the team’s daily rhythm. That reliability is what makes the difference.

What Changes Once AI is Orchestrated

With AI orchestration in place, teams spend less time managing processes and more time applying judgment, working with customers, and getting results. The connective work happens consistently, and people stay focused on decisions, collaboration, and problem-solving. Over time, this shapes how organizations grow. Workflows remain stable as volume increases, new tools fit into existing systems without disruption, and expectations rise while complexity stays manageable. This is how AI tools become durable enough to support real work at scale.