5 Years On – Pandemic Lessons for IT Leaders

Simon Tindal, the CTO at Smart Communications, shares some lessons IT leaders learned from the pandemic. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

When COVID-19 swept the world in 2020, lockdowns forced businesses to adapt almost overnight. Digital transformation timelines accelerated from years to mere days and weeks as organizations pieced together solutions to keep operations running. The benefits and drawbacks of this necessary but rushed approach are still being felt today as companies struggle with inefficiencies, system gaps, and clunky integrations.

These impacts offer valuable lessons for IT leaders today as AI, the next transformative wave in IT, continues to roll across the enterprise. Here are four lessons from the pandemic’s digital rush that can help shape a more future-proof strategy.

Prioritize the Customer

Adopting digital tools alone wasn’t the key to survival (or success) in 2020. The companies that thrived made digital experiences smoother and more convenient for their customers. Mortgage lender Nations Lending, for example, had already begun using AI and machine learning before the pandemic to reduce the time its customers spent on paperwork and cut down on processing delays. That meant when everything shifted online, they were ready and could offer a better customer experience than competitors still relying on outdated processes.

Customer expectations have only risen since then. Seamless, personalized interactions are considered table stakes for customers across different platforms, whether applying for a loan, making an insurance claim, or booking a doctor’s appointment. Companies that don’t meet these expectations risk losing business to competitors that do.

The lesson from 2020 is that digital transformation isn’t just about adopting new tools; there must be a customer-centric goal in mind when deploying them.

Fix Patchwork Infrastructure Now, Not Later.

The rush to implement digital solutions in 2020 meant that many were not fully integrated, and businesses are now dealing with a complex mix of systems that don’t communicate effectively. The resultant inefficiencies, data silos and poor integrations have created frustrations for employees and customers alike.

The stakes are higher when implementing AI. IT leaders must select platforms that work well together, invest in more flexible cloud-based solutions, and eliminate internal barriers to data sharing. Instead of bolting on new tools and fixing infrastructure later, businesses should prioritize designing a system built for long-term success.

Build for Scalability, Not Just Survival

The rush to online services drove web traffic through the roof in the early days of the pandemic, putting infrastructure to the ultimate test of scale. As flexible, scalable systems handled the surge, others struggled with slowdowns, outages, and lost revenue. But building for scalability isn’t just about handling spikes in demand. It’s about pivoting quickly when market conditions or business needs change. Using more resources than necessary can be just as costly as being unable to handle higher demand, especially with power and compute-heavy AI applications.

Investing in cloud-based solutions, AI-driven automation, and real-time monitoring can deliver the agility and responsiveness that companies need to manage a dynamic market. These investments will put companies in a much better position the next time they need to adapt quickly.

Deploy What Works For You

Just as millions of us spent a little too much online during lockdowns, many companies were all too happy to drop hefty sums on digital tools without pausing to consider their long-term business value. The resulting technology bloat created cost pressures on IT budgets, as subsequent audits found many of these tools did not get anywhere near enough usage to justify their cost.

Rash spending on AI could have far greater consequences for IT teams. Not only are the costs involved higher and the length of investment longer, but the intensive demand for compute resources could significantly spike their IT budgets and compromise the efficiency of the wider tech stack. That’s before factoring in the increase in power use and the resultant environmental impact, which many businesses are now required to report for sustainability purposes.

The business case must come first when deciding whether to deploy any new technology, especially for AI. IT leaders and their teams cannot risk diving headfirst into a shiny new platform without having a full and frank understanding of how it will benefit the business, whether the costs are justified, and what the broader impact on resources will be.

The Data Trifecta: Quality, Management, Security

Enterprise IT has been battling with its data for decades, but the pandemic exposed every issue that had yet to be solved. Problems with data quality, governance, and security had profound and lasting impacts on how companies operated, as any incomplete, outdated, or scattered data became a serious barrier to digital-first operations.

Once again, AI is raising the stakes, as it is only as good as the data given. Companies that struggled with data quality, management, and security during the pandemic cannot successfully utilize AI without addressing these issues.

Adding to the challenge is the heightened consumer sensitivity around how their data is managed when companies deploy AI. Consumers are increasingly concerned about privacy and security regarding AI innovations, which means businesses that prioritize trust and data integrity will be in a stronger position to use AI effectively.

Looking Ahead: Preparing for the AI-Driven Future

Barely three months into this decade, the unforeseen and seismic onset of COVID-19 forced businesses to make quick decisions under pressure. While some of those decisions ensured companies could survive the dramatic upheaval, they also created long-term challenges and highlighted areas where IT leaders need to improve.

With AI reshaping the digital landscape, companies can be more strategic and prepare for what’s next. The businesses that learn from the past and take a proactive approach will lead the AI-driven future.