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Experimentation Will Determine the Winners of the AI Revolution

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The AI revolution is upon us. Every day, new AI-based intelligent applications are changing the way we live and work. Across all industries, companies of all sizes see the potential of intelligent applications, including how they bring compelling digital experiences to customers. As a result, there’s a mad rush to introduce generative AI into existing applications and build new AI-powered apps, so companies can stay relevant. Meanwhile, product developers are grappling with a rising level of risk as they’re pressured to quickly release AI and iterate without breaking things.

In every technological revolution, there are winners and losers–those who set the tone for innovation, and those who fall behind. Do you still take Polaroids? Neither do I. A once must-have tech is now a retro-novelty at best, forgotten at worst, because the technology leaders at Polaroid were unable to accept the move to digital cameras. That’s what we’re facing right now. The AI revolution is an inflection point. If your enterprise wants to be on the winning side of history, start exploring how to incorporate AI into your business, embracing feature experimentation to ensure success. Don’t wait until tomorrow–after you realize your AI models are riddled with issues and your users are annoyed–today is the time. 

Experimentation is important when releasing AI, because it helps teams safely iterate, measure, and know how their AI features impact performance. At Split, we know this because we’re experts in enterprise software development. Our customers, the market, even AI leaders agree with us. Microsoft, for example, tapped Split to provide our experimentation capabilities natively in Azure, and this functionality will be available to all Azure developers early next year. Yet so many product development teams still lack the experimentation capabilities they need to do it right. Now is the time to adopt the proper tools, especially if your company has dreams of driving the next phase of the AI revolution.

A New Wave of AI Releases Is Crashing In

Recently, we conducted a market study to get a sense of how far the software industry has gotten with their AI development. We expected there would be a large swath of companies working on new AI releases, but the results we surfaced were astounding. A whopping 72% of companies surveyed are working on AI projects as we speak. 

From our study, as well as the closed-door conversations I have with other CEOs and the investor community, AI development is increasingly a top priority at enterprises across the globe. There’s a clear demand for AI capabilities that will only compound the pressure on product developers looking to release quickly and safely.

What happens when the majority of development teams work on AI projects for a year or more? You get a crushing wave of releases. From our study, 58% of teams plan to deploy AI into production within the next 12 months. Some will be great, some will be forgettable, and some will make a real mess. That’s my prediction for 2024.

Feature Experimentation Is Table Stakes

To facilitate any development process, feature experimentation is invaluable. But, in the midst of today’s AI revolution, experimentation is a must-have. Let’s say you need to test multiple variations of a model. Proper experimentation strategies allow you to do so safely (and to targeted segments), so you can quickly uncover which variation will be the winner. Or, if you release a chatbot that causes performance degradation to your app, feature-level monitoring instantly pinpoints the problem before it causes widespread issues. Both of these capabilities are imperative and can be achieved with feature management and experimentation platforms.

Today, engineering teams are telling us that feature experimentation is even more critical than it was before: According to our study, 73% of companies believe that experimentation, including A/B testing, is more important for rolling out AI-powered capabilities versus non-AI rollouts.

I recently spoke with Iavor Bojinov, Data Scientist and Professor of Business Administration at Harvard Business School. He sees the elevated importance of experimentation in today’s AI revolution as well. According to Dr. Bojinov, “Experimentation is a new requirement for developing and releasing AI capabilities. It is the gold standard for quantifying the impact of new AI models and understanding how users will respond to the outputs.” You can read more about Dr. Bojinov’s perspective in his Harvard Business Review article, “Keep Your AI Projects on Track”.

AI is not well-tread territory for most development teams. It’s a rapidly emerging field, where our body of knowledge is growing every day. That means we don’t have a track record of patterns and intuition to lean on. The problem is, at the enterprise level, there is little room for failure or latitude to waste time. So how do you safely and effectively build a body of knowledge without wasting time? By running experiments, teams can move fast, learn fast, detect issues early, and make informed decisions about their AI rollouts. 

Few Developers Are Prepared to Experiment

Too often we know what’s right for us, but we don’t have access to the tools we need to do what’s right. The same can be said about experimentation during the AI revolution. We also found that 72% of respondents believe it is important to catch issues quickly, but only 9% feel they are set up for success. This means there is an understanding and interest for experimentation, but not a lot of technical proficiency yet.

Split Teams Up With Microsoft to Deliver Experimentation in Azure

Along with Split, major players are realizing the need to bring measurement and experimentation capabilities to every developer toolkit. That’s why Microsoft is partnering with Split to integrate a native experimentation solution directly inside the Azure platform. 

This strategic build and partnership solidifies Split’s position as an experimentation expert and reaffirms Microsoft’s commitment to improving the developer experience in the race to quickly release AI features. Because Split was built by devs for devs, we meet the needs of modern developers looking to experiment on Azure. For more info on that, check out this Split + Microsoft press release. And there’s more to come soon.

AI Revolution, Here We Come

In the ever-evolving world of technology, experimentation has become a crucial element in the development and release of AI-powered features. As developers strive to deliver software that truly matters, the ability to test, iterate, and make data-driven decisions is table-stakes. Split’s Feature Data Platform™, rooted in our expertise in understanding the needs of developers, bridges the gap between development teams’ acknowledgement of this need and their ability to execute. Our partnership with Microsoft further solidifies our position as a thought leader in the market. By getting serious about experimentation, companies will establish themselves as the true winners of the AI revolution.

Switch It On With Split

The Split Feature Data Platform™ gives you the confidence to move fast without breaking things. Set up feature flags and safely deploy to production, controlling who sees which features and when. Connect every flag to contextual data, so you can know if your features are making things better or worse and act without hesitation. Effortlessly conduct feature experiments like A/B tests without slowing down. Whether you’re looking to increase your releases, to decrease your MTTR, or to ignite your dev team without burning them out–Split is both a feature management platform and partnership to revolutionize the way the work gets done. Schedule a demo to learn more.

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