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Jai Kumar
Head AI and Data Emerging Technology Partnerships
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Interactions between humans and computers succeed best when the organizational and regulatory context and rules guiding decision-making between AI and people are designed with intention.
Intelligent choice architectures (ICAs) are AI-driven systems built to intentionally shape the context in which people make decisions. ICAs combine agentic, generative, and predictive AI capabilities to create, refine, and present options to human decision makers. These decision environments actively structure the data, options, and trade-offs to produce decisions that lead to better outcomes.
Even the most skilled decision-maker underperforms without a diverse, high-quality set of options to choose from. AI decision-making optimized for efficiency might lack the right context for compliance or customer sentiment. While traditional analytics chase a single ‘optimal’ recommendation, ICAs generate multiple well-crafted options. This richer choice set often inspires better decisions because it gives humans more context and insight to work with and eliminates the bias lurking in efficiency-focused models.
Think of how Netflix or Amazon recommend content. Algorithms learn from your history and suggest new options you might not have found alone. ICAs apply the same principle to business: surfacing options aligned with context that managers might otherwise miss. An ICA can present a manager with bespoke options or analyses that fit their specific scenario. The ICA considers the manager’s prior decisions and current project constraints but also looks more widely to generate options aligned with that context. This means two people in different contexts, such as a sales manager with margins to maintain or an HR manager focused on restructuring, get different sets of AI-suggested choices optimized for their objectives.
Rules are important. They let leaders establish clear ethical and operational boundaries for AI, so that choices align with company values. Not all will look at governance in the same way because their values differ, but most will need to redefine decision-rights frameworks to be more fluid and create new metrics to evaluate the quality of AI-generated choices and outcomes. Ensuring transparency and explainability in how the AI presents options is crucial, so that humans understand the context behind recommendations.
TCS research with MIT Sloan Management Review shows that early adopters are exploring multiple ICA applications and proving that richer context built on a foundation of governance and guardrails can transform decision processes and can lead to better choices.
Retail giant Walmart is developing an ICA to identify hidden talent among its workers. Retailers employ large numbers of employees doing routine jobs across multiple stores. The AI-driven analysis can spot individuals with potential in remote stores or in roles that managers traditionally overlook. Talent spotting not only identifies new managers but helps workforce engagement in an industry that suffers high churn. Reducing levels of staff turnover and holding onto hidden gems has significant cost and operational benefits.
Banking, insurance, and financial services institutions are heavily regulated and must handle customer data sensitively. That’s the context in which they operate. Several companies are experimenting with choice architectures that balance innovation with risk.
Mastercard is reimaging the way customers interact with the company using ICAs to augment human capabilities across multiple processes that include onboarding, payment methods, and credit decisions. By actively generating new possibilities in a tightly regulated environment, the company is creating value across and beyond its ecosystem.”
Liberty Mutual reports that it has identified nine use cases so far including an ICA-enabled claims process. 90% of its engineers have embraced tools such as co-pilots which it claims has boosted productivity, freed capacity for complex challenges, and increased delivery velocity by 10%
Manufacturing firms are also finding ways to introduce context aware, human-centric AI improve both strategic design decisions and day-to-day operational choices on the floor of modern factories.
Cummins, which makes powertrains, is using ICAs to exponentially expand the number of scenarios it can test for. It uses AI to simulate thousands of edge-cases in engine design – far more than engineers could feasibly test manually. Engineers are thus exposed to a vastly expanded range of “what if” or context scenarios such as different operating conditions, rare stress combinations or previously unexplored us cases. AI imagines potential failure modes or optimization opportunities that would have been missed with limited human-scale experimentation.
Smart factories with IoT sensors and machine-learning models can predict when a machine will require maintenance. Rather than waiting for a breakdown, an ICA with access to context can present factory managers with decision options: it might recommend shifting production from to a backup line, rerouting work orders, or calling a maintenance crew for a timely intervention.
Whether in retail, finance or manufacturing, these examples underscore how AI systems can adapt to context and guide human decisions. They provide intelligent choice architectures that respond to sensor data, scenarios, predictions, goals, and anomalies to nudge managers towards better decisions. ICAs even have the power to find talent heroes hidden in the shadows and put them in the spotlight.
What began as a theoretical construct to improve decision making has been combined with the computational power of AI to become a methodology with wide-ranging applications. It’s now helping businesses not just make better individual decisions but to create environments where better decisions become routine.