Why businesses need Intelligent Choice Architectures to make AI a success

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% of enterprise AI 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 World Economic Forum Annual Meeting in Davos 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 realized, new frameworks are required. Leaders must establish structures that enable continuous, informed dialogue with AI, while keeping humans in the loop.

As we look ahead to 2026, a clearer picture of AI’s impact is emerging. We are witnessing the advent of a new form of organizational intelligence, where combinations of humans and machines shape how choices are developed, presented and discussed.

To unlock AI’s true value, businesses need to collaborate across ecosystems, apply deep domain insights, ensure rigorous governance and scale responsibly through co-innovation models. AI should improve the organizational decision-making process by presenting better choices that are backed by data.

A recent study by MIT Sloan and Tata Consultancy Services 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 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, organizations 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 organizations empower AI-driven choice architects, the more empowered human decision-makers can become.

Building this new foundation requires attention to five core principles:

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.

Organizations must track how high-stakes 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.

Intelligent Choice Architectures don’t flatter intuition; in fact, they often challenge it. Organizations 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.

To unlock true value from AI agents, decision-making-related barriers 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.

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.

This time last year, I reflected on why, in the Intelligent Age, enterprises must foster perpetual adaptability. Tata Consultancy Services is embracing this, by training more than 576,000 of our people in AI fluency, forging strategic partnerships with leading AI innovators, building advanced AI agents and embedding AI across every layer of our operations. We’ve also launched global AI innovation hubs to accelerate client transformation.

In January 2026, at the World Economic Forum’s Annual Meeting in Davos, leaders of economies, businesses and institutions will come together to discuss how dialogue can 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 take on perpetually adaptive mindsets that allow us to be open to GenAI insights and unlock a better future together.