AI is transforming everything it touches, from the way we work to how we communicate and ideate. However, in many cases, this impact is not translating to meaningful improvements in business outcomes at the scale that many banked on. The time for experimentation is over, and the reality is setting in that stakeholders are expecting AI production to tangibly benefit the bottom line.
As enterprises come to terms with the fact that AI integrations require a higher level of intentionality and strategic thinking to be meaningful rather than just experimental, they’ll need to adjust their AI strategies accordingly.
What’s Holding AI Production Back?
Executives want and expect quick ROI on AI initiatives, but ineffective implementation obstructs AI’s potential. Nearly 70% of CEOs expect AI returns within one to three years, according to a survey from KPMG US, and 82% say AI can improve resource efficiency. https://www.extremenetworks.com/resources/report/state-of-ai-for-networking-2026 And why wouldn’t they? AI was touted as being the magic bullet that would create hyper-efficient workforces, revenue booms, and next-generation technology.
In reality, much of that promise has gone undelivered. A recent report from IBM found that while 79% of executives say AI will significantly contribute to their revenue by 2030, only 24% can clearly see where that revenue will come from.
The technology behind AI is not the issue here. The models are capable of transforming workflows in the enterprise and are continuing to improve on an almost daily basis, and we’ve built the infrastructure to support these models. Where the disconnect lies is in the execution. It’s a uniquely human problem: We have unlimited potential in the palm of our hand, but making it work in the legacy processes entrenched in our world is not so easy.
How To Bridge the Gap Between AI Expectations and Outcomes
Many high-level executives who are pushing for ROI on AI lack clarity on use cases, governance, and paths to value. They fear being left behind in the rush to adopt AI and therefore jump into an AI strategy without a clear picture of these key elements.
The AI market is heavily saturated with hundreds of use cases and niche tools. If you’re tasked with identifying the right ones, it’s hard to know where to dive in. Picture being at an intersection in an unfamiliar location with no map and no destination. You can pick a road and try it, or you can pause, determine where you’d like to end up, and seek out someone more versed in the area to advise you on the best way to get there. Some executives, under both self-imposed and external pressure, are anxious to get started and may think that just starting down a path, even the wrong one, is better than what they see as standing still.
Here are some key elements to consider before you start driving. And if you’ve already started down a road and realized you’re going in the wrong direction, it’s not too late to pause and re-evaluate.
Tech modernization
Is your tech stack ready to support AI? Legacy systems are actively blocking AI adoption. Deloitte reports that nearly 60% of AI leaders said their primary challenge in adopting agentic AI was integrating with legacy systems. If your existing tech is a roadblock to adoption, there’s a silver lining: AI has also given tech leaders the ammunition to turn IT modernization from a cost center conversation to a strategic investment.
An outcomes-driven approach to AI
What can AI help you accomplish? AI adoption should focus on revenue growth, cost reduction, or experience improvement. Picking one of these areas to focus on and then identifying specific use cases is a good place to start. What’s infinitely harder is trying to retrofit purpose into an AI proof-of-concept that may seem impressive on the surface but doesn’t have any depth or significant application within the business.
AI Governance
Do you have a governance plan in place? These controls are a prerequisite, not a “nice-to-have.” Effective AI governance embeds human-in-the-loop controls, explainability, and auditability directly into AI systems, ensuring models can scale without introducing unmanaged risk. Governance must move from policy documents to operational controls that shape how AI is built and used.
Data Governance
What data will your model have access to? Even with the most powerful AI model in the world, if it doesn’t have data to feed on, it won’t succeed. Data must be usable, not just accessible – that means un-siloed, trusted, and industry-specific.
The Next Phase of AI: Moving From Pilot to Production
To find success and ROI from AI, the answer does not lie with building faster models or investing in more infrastructure. Most businesses already have all the tools they need to operationalize AI effectively. Moving AI from pilot to production requires orchestrated architectures and continuous optimization so models, data, and workflows can scale reliably across environments. Once AI is working in the business, at scale, producing outcomes that matter, with governance and trust built in – that’s where the real magic happens.
