Enterprise AI initiatives are stalling in early 2026 because organizations invest in models and infrastructure without first validating whether the problem actually requires AI.
Preksha Shah, Head of Engineering at EnSpirit Technologies, argues that teams must define clear use cases, assess architectural readiness, and question whether simpler solutions would suffice.
She advises starting small, proving value before scaling, and staying grounded in engineering fundamentals rather than chasing AI for its own sake.
Enterprise AI programs are breaking down at the starting line. Organizations are committing budgets, hiring teams, and spinning up infrastructure before answering a basic question: what problem are we actually solving? The result is a growing roster of stalled pilots, inflated costs, and systems that struggle under operational complexity long before they deliver measurable value.
Preksha Shah, Head of Engineering at EnSpirit Technologies, a UK-based technology consultancy specializing in scalable software and AI-enabled product development, works at the intersection of architectural planning, team execution, and stakeholder delivery. With over six years of experience building and scaling software systems in product-led environments, she sees the same pattern repeating across organizations eager to adopt AI.
"Organizations want to use AI, but they don't know the use cases. They're thinking about the solution, not the problem," says Shah, describing a disconnect that has become the default starting point for many enterprise AI efforts. Under pressure to adopt, teams build models and deploy infrastructure without grounded use cases, leading to weak feasibility assessments, costly architectures, and programs that stall before they produce anything.
Not every problem needs AI: The most common misconception Shah encounters is the assumption that AI is a prerequisite for a product to succeed. "Even in this era, every system doesn't need AI," she says. "If your idea is not going to sell without it, then AI cannot add much effect." She advises teams to start with a clear problem definition, take stock of their existing data and infrastructure, and ask a direct question: does this product or process actually require AI, or would a simpler solution work? Measurement, not instinct, should guide the decision.
Architecture must account for drift: Feasibility goes beyond whether a system works on day one. Shah points to a pattern where teams design architectures that perform well initially but degrade within weeks. "Currently it gives me data in 10 seconds, and after 10 days, without any changes, it takes 50 or 60 seconds," she explains. Monitoring, backup systems, and predictive metrics need to be planned from the start. "Monitor your AI like you are mentoring a junior engineer," she says.
Build versus buy demands realism: The calculus has shifted. Where organizations once defaulted to buying off-the-shelf tools, many now try to build everything internally, treating every system as a potential product. Shah warns this creates its own risks. If capable agents and APIs already exist in the market, the cost and uncertainty of building a competing tool in-house rarely makes sense. "Building makes sense when you are in a profit era or your idea can get traction," she says. "Otherwise you are just adding heavy maintenance and cash flow issues."
Start small, prove value, then scale: Shah advocates for an incremental framework: define a small use case, understand its failure modes and success criteria, validate it, add feedback loops, and only then accelerate. "Start small, prove the value, then scale. There are no shortcuts," she says. That discipline applies equally to data readiness and governance, both of which organizations tend to backfill after problems surface rather than plan for upfront.
The broader risk is not that AI fails to work. It is that organizations misidentify what it should be working on, then compound the error with premature investment. Shah sees roughly half of any given room still operating under the belief that AI integration is a prerequisite for commercial success, a conviction that drives unnecessary complexity and diverts resources from problems that matter.
For all the urgency around adoption, Shah returns to a grounding principle. "AI is super powerful, but it's still a tool," she says. "The engineering flow has never changed. You need to upgrade yourself, stay with the markets, stay with the community." Teams that pair AI adoption with disciplined problem-first thinking and continuous learning are the ones positioned to win. "People who use AI will replace those who don't," she says, "but only if they know what they're building it for."
