We often confuse velocity with strategy. The rush to “do AI” has become more theater than transformation – a performance driven by FOMO, procurement checklists and quarterly narratives, not by a clear product spec or measurable outcome.
Context
I recently read an interview with a design leader who argued that large language models are being sold as features without a spec: “it’ll do anything for anyone.” That observation captures a common enterprise mistake today – buying raw capability and hoping it will magically translate into durable business value.
Analysis – what this means for architecture and leaders
The fundamental issue is productization. In engineering, a product begins with a spec: who it’s for, what it must do, and what it intentionally will not do. LLMs are powerful, but they are engines, not end solutions. Dropping them into processes without defining boundaries creates three predictable problems:
– Misaligned investment and outcomes. Token-heavy pilots inflate vendor bills without changing unit economics. CFOs will – and should – ask for measurable gains, not anecdotal productivity boosts.
– Operational risk and tech debt. Rapid, ungoverned deployments introduce brittleness: hallucinations, drift, security exposures (prompt injection, data leakage), and fragile integrations that later cost far more to stabilize than to build properly.
– User experience failure modes. Automating decisions without mapping where human judgment is essential leads to poor outcomes and reputational loss – as several high-profile rollbacks have shown.
As a chief architect I view AI adoption through classic trade-offs: Speed vs Stability, Build vs Buy, Innovation vs Governance. The right approach is to treat AI like any other platform technology: define SLAs, testability, observability, rollback paths, and an economic model before large-scale roll-out.
Concrete actions for CTOs and founders
– Start with outcomes, not models. Define the metric you will move (cycle time, error rate, cost per transaction), then design an experiment that ties model outputs to that metric.
– Map decision boundaries. Identify which steps require human verification, which can be automated, and where a probabilistic model is unacceptable.
– Pilot with narrow scope and measurable gates. Use a spreadsheet-level automation or an invoice-anomaly app as the MVP; measure lift, cost, and user satisfaction before scaling.
– Build an AI playbook: model cards, evaluation suites, drift detection, cost monitoring, and incident runbooks. Treat ModelOps like SRE for models.
– Govern spend. Implement token/cost quotas, approval gates for expensive endpoints, and vendor evaluation that includes data residency and exit strategy clauses.
– Invest in explainability and audit trails. For enterprise and government use-cases, traceability is non-negotiable.
A note for India – and my region
In geographies like Northeast India, where budgets, skills and connectivity can be constrained, these lessons are amplified. Lightweight, offline-capable automations that augment human workflows often deliver higher net value than brittle, always-online LLM integrations. Public-sector DPI and SMEs should prioritise resilient, auditable automations that degrade gracefully and preserve local control.
Takeaways
– AI is a tool, not a product: define the product first, then choose the tool.
– Measure before scale: pilot with clear success criteria and financial gates.
– Design for failure: observability, rollback and human-in-the-loop are mandatory.
– Control cost and vendor lock-in through governance and procurement discipline.
– Local constraints matter: favour resilient, augmentative solutions for constrained environments.
Closing thought
If we want AI to deliver as more than theater, leaders must slow down enough to think like product people and operate like platform engineers. That discipline – not hype – will determine who truly gains from this technology.
About the Author Sanjeev Sarma is the Founder Director of Webx Technologies Private Limited, a leading Technology Consulting firm with over two decades of experience. A seasoned technology strategist and Chief Software Architect, he specializes in Enterprise Software Architecture, Cloud-Native Applications, AI-Driven Platforms, and Mobile-First Solutions. Recognized as a “Technology Hero” by Microsoft for his pioneering work in e-Governance, Sanjeev actively advises state and central technology committees, including the Advisory Board for Software Technology Parks of India (STPI) across multiple Northeast Indian states. He is also the Managing Editor for Mahabahu.com, an international journal. Passionate about fostering innovation, he actively mentors aspiring entrepreneurs and leads transformative digital solutions for enterprises and government sectors from his base in Northeast India.
