Stop Building Toys

Artificial Intelligence is not merely a technology upgrade cycle but a once-in-a-generation test of managerial courage and operational discipline.

In 1876, when Alexander Graham Bell demonstrated the telephone, a senior executive at Western Union reportedly dismissed it as an “idiotic toy” with no commercial possibilities. The company famously declined to buy the patent. A few decades later, that ‘toy’ had rewired global commerce. Similarly, in the early 1900s, automobiles were mocked by horse-breeders as noisy novelties for the wealthy, oblivious to the fact that the internal combustion engine would soon birth highways, suburbs, and global logistics.

History is littered with inventions that appeared impractical or overhyped in their infancy, only to eventually become the bedrock of civilization. Today, Artificial Intelligence stands at a remarkably similar crossroads.

Pilot Purgatory

While global enterprises are pouring billions into GenAI, a quiet frustration is brewing in the boardrooms of Mumbai and Bengaluru. Most AI initiatives are trapped in a ‘pilot purgatory.’ Internal demos win applause and proof-of-concepts (PoCs) generate headlines, but measurable business value remains elusive. The scepticism is rising whether or not Is AI failing?

The answer is no. AI is not failing. It is our traditional organizational systems that are failing to absorb it. The Industrial Revolution did not succeed simply because steam engines were invented; it succeeded because factories were entirely redesigned around them. Electricity did not transform the world the day Thomas Edison patented the light bulb; it changed the world when manufacturers replaced central steam shafts with distributed electric power, allowing for the modern assembly line.

Technology alone never transforms a society; systems do. Today, many organizations are treating AI as a ‘gadget’ rather than infrastructure. They experiment with chatbots to handle FAQs or use AI to summarize meetings, but they leave the underlying business processes untouched. Without answering who owns the outcome, how to scale across the enterprise, and how to track ROI, AI remains a high-priced showcase project rather than a growth engine.

The common narrative is that AI’s limitations are technical and that we need more compute or cleaner data. In reality, the bottleneck is cultural and structural. AI initiatives often stall because sales teams are misaligned with AI-driven insights, delivery teams lack implementation clarity, and governance frameworks are treated as an afterthought.

Breakthrough technologies do not scale through enthusiasm; they scale through discipline. When the internet arrived, the winners were not just those who built websites, but those who restructured their entire supply chains and customer engagement models. AI demands a similar ‘architectural’ seriousness.

Architectural Seriousness

To move from proof to profit, leadership must drive the following strategic shifts. AI must be tied to specific P&L goals - revenue growth, churn reduction, or speed-to-market – and not experimentation for its own sake. A PoC proves possibility while an enterprise-wide implementation proves value.

If a tool doesn’t have a clear path to 1,000 users, it shouldn't be built for ten.

AI must be integrated into the core. It should not be a ‘bolt-on’ feature. Like Amazon’s recommendation engine or Netflix’s personalization algorithms, it must be woven into the core product.

Governance must function as an accelerator. Without clear accountability for AI-generated outcomes, organizations create more risk than value.

Execution must take precedence over hype, for the winners of this era will not necessarily be the ones who invent the most models, but the executors who combine technical clarity with operational discipline.

For a nation like India, which sits at the heart of global IT services, this moment is pivotal. We have moved from being the world’s back-office to its R&D lab. If our institutions, incentives, and execution models are redesigned around AI, we define the next economic era. If we treat it as a fleeting trend, we remain spectators. JPMorgan Chase now uses AI to review legal documents in seconds - a task that previously took 360,000 human hours annually.

When they first arrived on the scene, the telephone was dismissed as an impractical curiosity. The automobile was mocked as a noisy indulgence for the rich. Electricity was underestimated as an incremental convenience rather than a transformative force. Similarly, AI, too, faces scepticism but it will not disappear. The only remaining question for the Indian C-suite is whether we will build with it seriously today, or look back and regret our hesitation tomorrow.

(The author is a strategy and transformation leader who writes extensively on technology and future of work.)