Two years ago, when I walked into meetings with industrial operations leaders, the first question was always some version of, “Can AI really do (waves hands) all this?” The conversation centered on technical feasibility: precision, edge cases, network constraints, latency and the overarching question of whether applied artificial intelligence using computer vision (CV) could actually distinguish between safe and unsafe behaviors in complex environments.
That conversation is evolving rapidly. The leaders driving AI adoption in their organizations are no longer asking what AI is on a fundamental level. They’ve moved past that question. Now, they’re asking instead whether the AI they’ve bought and deployed will work for them, for their specific problems and in their unique locations. People aren’t asking, “Does this work?” Instead, they want to know, “Is this valuable?” And once you hit that first metric of AI’s validity and value, where do you go from there?
The shift from “Is this real?” to “Show me the value” marks an inflection point for the entire AI tech ecosystem. Those built purely on engineering chops are discovering that technical superiority no longer guarantees market success. Companies that have novel tech that demos well or gets people excited about the future without meaty results are seeing plateauing usage and high risk of churn.
In 2026, the winners won’t be those with just great tech. They’ll be the companies who’ve built the most effective value creation ecosystems around their products — and not just their tech stacks.
The Technical Moat Myth
Many young technology companies operate under a comforting assumption: build the best product and customers will come. Create a technical moat with superior models, better precision, faster processing, point these toward a problem, and you’ll maintain competitive advantage.
The uncomfortable truth is that pairing great tech with a “first mover advantage” is less valuable than it once was given how rapid technological advancements are happening. It has to come with a purpose-built experience that is heavily contextual for the problems it means to solve. Companies need to increasingly be cognizant of not just what could be built, but how it will be used. Domain expertise, great user experience and tangible results will be what determines which startups breakout.
To clarify further, discrete pattern recognition used to be novel on its own merits. Seeing the previously unseen was inherently valuable, so companies with a strong CV tech stack and quality algorithms (i.e., high precision, low recall) were growing quickly.
Then it became increasingly clear that “seeing” problems is not useful on its own. Rather, what you do thereafter is the key. For example, I started my journey in the CV landscape in 2015, working in professional sports with major professional basketball and soccer leagues around the world. We could win deals and expand simply by providing new data types for the teams to digest and interpret. We were first movers, and just being early was a huge advantage in the market.
Now, however, earning an advantage is less and less about the algorithms themselves — or who was first to market — and much more about both providing the raw data and pairing that with team-specific strategy and expert recommendations to make meaningful adjustments on the soccer pitch. In other words, you have to solve a specific problem.
The Execution Ecosystem Advantage
What will determine competitive advantage in the future? How will winners be decided? The answer lies in what I call the “execution ecosystem,” which is the complex infrastructure of adoption, behavioral change and systematic operational integration that brings AI to life in the physical world.
What Is an Execution Ecosystem?
An execution ecosystem is the comprehensive infrastructure of adoption, behavioral change and systematic operational integration required to transform AI insights into tangible real-world value. Its components include:
- Change Management Infrastructure: The tools and processes that help traditional supervisors and staff integrate AI insights into their established daily workflows.
- Behavioral Adoption Mechanisms: The methods used to build trust among frontline workers so they are willing to act on recommendations generated by AI.
- Organizational Integration: The technical and procedural infrastructure that allows AI data to flow seamlessly into existing legacy systems, such as safety management or operations planning.
- Systematic Operational Change: The ability to scale solutions horizontally, moving from solving a problem at a single pilot site to driving consistent results across dozens of facilities.
Here’s the fundamental problem with most applied AI: If the operator doesn’t take action on the insight, the technology is irrelevant. For example, you can have the world’s most sophisticated CV model detecting workplace hazards, but if supervisors don’t intervene, workers don’t change behaviors and safety protocols don’t adapt, you’ve built an expensive observation system instead of a solution.
Building execution ecosystems requires capabilities beyond just the engine of the product. It requires all of the following.
Change Management Infrastructure
How do you help a 50-year-old retail business supervisor integrate AI insights into a daily workflow?
Behavioral Adoption Mechanisms
What makes frontline workers trust and act on AI-generated recommendations?
Organizational Integration
How does AI-generated data flow into existing safety management, operations planning and training systems?
Systematic Operational Change
How do you move from solving problems at one site to driving change across 50 facilities?
This is where the uncomfortable realization hits technology leaders: The hard part is building the infrastructure that helps customers actually use your product at scale. This transition from tech company to solution provider requires something psychologically difficult for founders and engineering-driven leaders: letting go of what got you here.
For technology companies, the early success formula was intoxicating. Hire the best engineers, build the most advanced algorithm stack possible, obsess over technical details that differentiate you from competitors and lead with product capabilities in sales conversations.
The companies that will thrive in 2026 are those willing to add new capabilities to their technical foundation.
Deep Customer Partnerships
Engineering excellence remains essential, but it must be combined with an intimate understanding of how customers actually operate.
End-to-End Thinking
Building sophisticated AI is critical. Equally critical is building the infrastructure that helps customers deploy and scale it successfully.
Value Translation
The engineering team’s technical achievements matter most when they translate into measurable customer outcomes like reduced injuries, lower costs and improved efficiency.
This doesn’t mean abandoning technical excellence. That remains table stakes. But it means recognizing that technical excellence is the beginning of value creation, not the end.
The Market Is Always Right
There’s a phrase I find myself repeating to my team: The market is always right. Not because markets are perfect or customers always know what they need, but because the only validation that matters is whether customers buy your product, deploy it successfully and use (and buy) more of it.
In 2026, the market is sending a clear signal to technology companies: We don’t need more tech for tech’s sake or investment in LLMs to “automate” frontline jobs away. We need solutions that work in the real world, with real operators, solving real problems. The companies listening to that signal will build sustainable businesses. Those still perfecting their algorithms in isolation will wonder why the “inferior” technology is winning.
