After years of pilots, proofs of concept, and isolated success stories, enterprise AI is entering a new phase, which is the one defined by execution at scale. Vikas Singh, Chief Growth Officer at Turinton AI, believes the industry has reached a critical inflection point. Speaking with Tech Achieve Media, he outlines how shifting CXO expectations, evolving governance models, and smarter architectural choices are accelerating the path from experimentation to impact, enabling companies to deploy artificial intelligence that delivers measurable business value in weeks, not years.
TAM: You believe 2026 will mark a shift “from experimentation to execution.” What concrete signals are you seeing that prove enterprises are finally ready to move beyond pilots?
Vikas Singh: The conversation has fundamentally changed. A year ago, CXOs wanted to know what AI could do. Now they’re asking how to get it into production without breaking the business. That shift tells you something. We’re working with manufacturers and supply chain teams who’ve stopped waiting for the perfect pilot. They’re committing to real deployments in 8 to 12 weeks because they’ve learned that PoCs don’t translate to how the business actually works. When you test in isolation, everything looks clean. When you try to scale, you’re dealing with interconnected decisions, fragmented data, real complexity. The companies moving fastest aren’t the ones with the best models. They’re the ones that accepted this reality early and started solving for it.
TAM: As AI projects mature, what should real ROI look like for enterprises and what are the biggest reasons most pilots still fail to deliver measurable impact?
Vikas Singh: Stop measuring model accuracy. Measure what matters to the business: How much faster are decisions being made? How much manual work disappeared? What’s the impact on inventory, on cash flow, on service levels? J&J cut decision cycles in half. Cisco brought down production lead times by 30 percent. Those are the numbers that matter.
Pilots fail because they exist in a bubble. You optimize for one function, one scenario, and ignore everything connected to it. Demand planning doesn’t exist separately from supply planning or production scheduling. They’re all pulling in different directions. You get a result that looks good on paper but creates problems elsewhere. Add to that the fact that pilots use clean data while real data is scattered across systems that don’t talk to each other. Teams that actually scaled measured business outcomes from the beginning. They didn’t wait until they had perfect AI. They asked what the business needed first, then built to that.
TAM: Governance is emerging as a major stumbling block in scaling AI. What guardrails must enterprises put in place to deploy AI safely, reliably, and at speed?
Vikas Singh: Governance gets blamed for slowing things down, but that’s usually a sign it’s poorly designed. Start simple. Map out which decisions should be autonomous, which need human validation, which require human judgment. Be explicit about it. Then build your processes around that clarity.
Data governance should focus on reliability, not perfection. Real-time monitoring matters more than getting historical data pristine. Create clear escalation paths when AI recommendations touch sensitive business decisions. Monitor continuously. You should know within days if something’s working or drifting.
The enterprises moving fastest aren’t the ones with loose governance. They’re the ones where governance exists to enable speed, not restrict it. Everyone knows what’s being automated, why, and what happens when something goes wrong.
TAM: Most large organizations carry years of tech debt. What does a future-ready AI architecture look like for companies that want to operationalize AI at scale in 2026?
Vikas Singh: You can’t rip out legacy systems. Build a different way instead. Stop trying to centralize everything into data warehouses. Build a knowledge graph layer that connects your fragmented systems without moving data around. That cuts implementation time and risk significantly.
Design for composability, not monolithic solutions. Build modular pieces like what-if simulation, optimization, forecasting. Make them work together, not as isolated black boxes. Invest in real-time connectivity between systems instead of batch processes. Legacy systems weren’t built for AI workflows, but you can layer connectivity on top without rebuilding everything. And design for human-in-the-loop. Your architecture should make it easy for decision-makers to understand why they got a recommendation, push back when they need to, and learn from outcomes.
TAM: What’s the biggest misconception leaders still have about enterprise AI today and how is that slowing their transition from POCs to full-scale deployment?
Vikas Singh: Leaders still think autonomous is the goal. More automation equals more impact. That’s wrong. The companies winning right now are selective about what they automate. They figured out where human judgment creates the most value and built AI that makes those decisions better, not unnecessary.
Second thing that holds people back: thinking they can solve data integration and architecture later. You can’t. Fragmented systems don’t work with AI. The moment you try to scale, you hit that wall. Companies that moved from pilots to production addressed the architecture issue first.
Third: the belief that complex models deliver better results. They don’t. A straightforward model on a well-connected platform beats a sophisticated model on broken systems. The business outcome is what matters, not the sophistication of the math.
TAM: Turinton positions itself as a partner for enterprises moving from AI pilots to real deployment. What unique capabilities or differentiators enable Turinton to help customers achieve production-grade AI at scale?
Vikas Singh: We approach this differently from most platforms. Most spend six months on data integration and ETL. We skip that step entirely with our knowledge graph architecture. We connect fragmented systems without moving data around. Those alone collapses implementation timelines.
We also don’t chase autonomy for its own sake. We build for decision support. What if your planners had better visibility into what-if scenarios? What if optimization recommendations were transparent and actionable? That’s how you get ROI that matters. Knowledge graphs, simulation, optimization working together. Our customers go from pilots to production delivering real impact in 8 to 12 weeks. Most platforms take a year. That difference isn’t just speed. It’s because we’re solving the actual problem from day one instead of doing months of infrastructure work first.
