A decade ago, my team delivered the #1 mobile e-comm site in the big box electronics and appliances category. How we did it can teach you about success with AI.

Aaron Godby, CEO & Founder

AI is changing technology as we know it, and it’s only going to speed up from here. Winners and losers will be created by this shift. Leaders everywhere are scrambling to figure out how to prepare their organizations to consume this change. 

A similar disruptive situation unfolded with smartphones in the 2012 – 2014 timeframe. Entering those years, there were very few mobile-specific web experiences. Just about everything required “pinch/zoom” navigation, and as mobile traffic began to increase materially in this timeframe, brands knew they had to adapt quickly. How to adapt and common obstacles for delivering mobile experiences were still big mysteries. 

With OpenAI CEO Sam Altman frequently implying we’re only at the beginning of an exponential period of innovation and with everyone agreeing that AI will be a game changer, this situation repeats itself. Brands know they need to adapt quickly to AI–even more so than mobile–but how to prepare for AI is largely a mystery to most organizations. 

Read on to discover lessons learned from my history working with companies to adopt mobile and how to apply these lessons as you prepare for AI.

Look to the past as a crystal ball to the future

Think back to the days before smartphone usage hit critical mass.

Calling an Uber. Scrolling Instagram. Looking up directions on Google Maps. Tracking your steps with a smartwatch. Watching high-definition video. None of these now-everyday activities were obvious when the iPhone 2G was released by Apple and Steve Jobs in 2007. 

Thinking back, I recall much less fanfare than there was skepticism. We had to watch it unfold in the wild, gradually resetting what the idea of normal was and layering idea on top of idea in this new normal. 

We believe the same is currently true for AI. We don’t know exactly where it’ll go. But we know its impact will be widespread, like nothing we’ve experienced since the smartphone disrupted every major business. 

40% of organizations surveyed by CapGemini said they’re already funding AI endeavors – and another 49% plan to do so within the next 12 months, and 74% believe the benefits will outweigh associated concerns. The businesses that create the right building blocks for GenAI early stand to realize value from AI first. 

So 2024 corporate AI objectives will be well-funded, but most organizations I talk to are searching for a roadmap to success. Understanding of major hurdles will be critical to ensuring these budgets are put to use wisely to enable high-value AI use cases. 

An old, boring, and (largely) unsolved IT problem

In the summer of 2013, I had the privilege of leading a team of engineers at a major big-box electronics and appliances retailer. Like every retailer in that time period, evolving buyer web traffic patterns shifting to mobile meant that our customer needed a mobile site to be competitive, and it needed it now. Our goal was to deliver this experience in a very narrow timeframe for an October go-live so that we had ample time to tune it before Black Friday, which was like retail’s Super Bowl.

Our technical challenge was that desktop websites at that time rendered entire megabyte-heavy Web pages in one shot and that Browser technology was still evolving from its infancy. Mobile required a lighter-weight solution that enabled devices to fetch new data without a full page refresh to avoid overtaxing the limited resources of the iPhone 3G and 4. As a result, the industry shifted to multi-tiered apps with heavyweight back-ends exposed by APIs, and front-ends driven by newer JavaScript frameworks.

We were trailblazers at the beginning of this shift with our client. The mobile site we delivered received a lot of accolades and won the prestigious JDPower award for its category, beating out much bigger budgeted brands like Home Depot, Lowes, Costco, and Best Buy. It was praised for its user experience, page speed, and for taking advantage of newer device and browser capabilities. Most importantly, it produced great revenue funnel metrics for our customer and was easily scalable to handle the huge traffic spikes of retail. 

In a world of poorly-operating mobile sites, what was our secret? The same secret that will enable AI: a flexible, big-picture systems integration strategy.

None of what we achieved would have been possible without the flexibility created when our engineering team prioritized solving the integration problem as a necessity for solving everything else. Solving integration enabled our rapid delivery schedule, unlocked features to lift the buyer journey, and enabled our site to perform at scale. We knew it was a must-have for our client to be successful.

Fast forward a little over a decade, and we sit at the precipice of another huge technological shift with AI. And with AI, the integration demands will be even higher. For it to have a widespread impact, it needs to understand which systems have which data and which systems are used to trigger which business actions. 

In 2023, Shyam Sankar, CTO of Palantir, said “The popular view is that [systems integration] is a boring and solved problem, but it might be a boring and highly unsolved problem, where people are just duct taping everything together.”

Sankar goes on to link this problem to AI enablement, implying that only companies who solve this problem will reap the biggest benefits from AI. This belief is shared by MuleSoft Founder Ross Mason, who wrote about it in a 2018 blog. Green Irony’s own experience building custom AI solutions to address high-value use cases also backs up the need for scalable systems integration. 

Unfortunately, too many organizations today brute-force integration strategies to get them to “just work.” The result is that these systems integrations are inherently brittle and sloppy, serving only the narrow needs of each integration consumer instead of being flexible enough to quickly meet the needs of ANY consumer. 

Engineering teams must prioritize unlocking key systems in a scalable way if their organizations are to be successful with AI.

Getting your house in order

Acknowledging a problem is the first step toward solving it. 

GenAI solutions, much like mobile, will presuppose that this integration problem has been solved. Savvy enterprises will learn from mobile and ensure that this presupposition is correct, paving the road to AI success for their organizations. 

Don’t be caught off guard. Educate yourself by taking a deep dive into this topic with our AI Enablement White Paper. Learn more about AI enablement and why a flexible, composable integration strategy is critical to your success.

Interested in a discussion on this topic? Reach out.