Intelligent choice architectures: Turning AI's potential into performance

Genai can transform enterprise decision-making, but most pilots fail. Intelligent choice architectures offer a way for humans and ai to collaborate and deliver real business value

The rise of GenAI represents a fundamental shift. It transforms how enterprises think, make decisions, and act. Yet despite this enormous potential, research shows that 95 per cent of enterprise AI (artificial intelligence) pilots have failed to deliver measurable value. 

To move beyond the promise of AI and turn potential into performance, leaders must rethink not only what decisions are made but how decision-making itself is designed, and how humans and AI can collaborate most effectively. 

This year’s the annual meeting in Davos of the World Economic Forum focuses on the need for collaboration amid accelerating complexity and exponential innovation — and nowhere is this spirit more urgently needed than in enterprise decision-making.

However, for success to be achieved and AI’s potential to be realised, new frameworks are required. Leaders must establish structures that enable a continuous, informed dialogue with AI, while keeping the humans in the loop. In 2026, we will witness the advent of a form of organisational intelligence where combinations of humans and machines shape how choices are developed, presented, and discussed. 

Intelligent choice architectures and why businesses need them 

A recent study by MIT Sloan and Tata Consultancy Services has confirmed what many leaders already sense: GenAI is reshaping the foundations of enterprise decision-making. This represents a major shift in how humans and machines reason together. To make this type of human-machine collaboration deliver results, “intelligent choice architectures” are the key. 

Intelligent choice architectures combine generative and predictive AI capabilities to create, refine, and present choices for human decision-makers. They actively generate novel possibilities, learn from outcomes, seek information, and shape the range of available choices. 

To successfully implement intelligent choice architectures, organisations must first rethink what constitutes an “agent” when decision-making is distributed across human-AI networks rather than confined to individuals. 

In this new paradigm, leadership becomes less about control and more about designing systems where humans and machines can make informed choices together. The more effectively organisations empower AI-driven choice architects, the more empowered human decision-makers can become. 

5 principles to build AI that works for business 

Building this new foundation requires attention to five core principles: 

Trust must be built over time: To manage risk and improve trust, implementation must be a gradual process. Stakeholders need to feel growing confidence in how decision environments are framed, not just in the correctness of decisions. Taking an iterative process that allows for incremental learning can help colleagues understand how to operate within new architectures successfully. 

Visibility is the key: Organisations must track how high-stake decisions are made and take a holistic view of their business and the wider landscape. Intelligent choice architectures are dependent on accurate data and will fail — or even model the wrong choices — without it. 

Open-mindedness is essential: Intelligent choice architectures don’t flatter intuition; in fact, they often challenge it.  Organisations must build a culture where people are comfortable about getting things wrong in front of others. In the shifts to harnessing these systems, this is part of the journey to success. 

Decision-making hierarchies must evolve: To unlock true value from AI agents, barriers related to making decisions need to be removed. If only credentialled experts of legacy hierarchies can make decisions, intelligent choice architecture insights may be dismissed, regardless of their quality. 

Workflows must change: If cultural shifts can be achieved, new workflows must also be developed to accommodate this shift. Processes must be more flexible so that better options presented by intelligent choice architectures can be implemented. This is not a question of AI literacy but of building internal systems where these human-machine cooperation-derived insights can be seen, understood, and acted upon. 

Facilitating dialogue through technology 

This edition of the meeting in Davos presents leaders of economies, businesses, and institutions with an immediate opportunity to guide progress amid accelerating complexity. 

GenAI has the potential to help elevate this exchange by informing conversations and supporting decision-making. But as outlined above, success is dependent on evolving our systems to allow technology to benefit all. 

Intelligent choice architectures are the blueprint that will ensure dialogue is more informed and inclusive, bringing structured, data-driven perspectives into our discussions. As the humans in the equation, we must challenge ourselves to adopt perpetually adaptive mindsets that enable us to remain open to GenAI insights, which in turn can transform AI’s potential into performance and help unlock a better and more inclusive future together.

 

The author is chief executive officer and managing director, Tata Consultancy Services