In the world of Enterprise IT, cloud adoption and modernization are strategic endeavors riddled with complexity, trade-offs, and high-stakes investment decisions. MIT SMR and TCS introduce Intelligent Choice Architectures (ICAs)—AI-powered systems that couple generative and predictive AI.
The framework augments enterprise cloud teams to make better, context-aware decisions by expanding optionality. As a result, teams can map multicloud adoption pathways, simulate trade-offs across cost, risk, and performance, and design adaptive architectures that scale with their needs. These interventions transform cloud adoption from a high-stakes gamble into a structured, evidence-backed process. They shift decision making away from heuristics and “lift-and-shift” shortcuts, enabling a system where evidence-backed insights reveal the most viable paths.
"The shift we’re seeing — and this is where agentic AI is key — is from autonomous systems to autonomy. Autonomous systems follow pre-set rules; autonomy means the system can learn from its own actions, improve iteratively, and make better decisions over time." – Suranjan Chatterjee, Head of Technology, TCS
The traditional approach to cloud consulting relied on rules and expert playbooks. A system of "if A, then B" logic built from accumulated consulting experience. While effective in standard scenarios, this approach often optimizes single decision points and struggles to address the systemic issues and evolving realities of enterprise IT. It misses the forest for the trees.
The rise of foundation models has reshaped this landscape. These models allow organizations to tap into global knowledge and patterns well beyond codified experience, enabling richer, more adaptive guidance. Instead of making recommendations based on tribal knowledge, AI agents can now simulate thousands of scenarios, weigh risks and trade-offs, and recommend optimal pathways to scale enterprise infrastructure.
For example, ICAs can evaluate whether workloads are best suited to AWS, Azure, or hybrid deployment, quantify compliance risks for each option, and recommend sequencing strategies that minimize downtime while optimizing long-term cost curves. This moves platform choice and migration planning out of the realm of guesswork into a system of transparent, testable trade-offs.
Large enterprises rarely begin cloud journeys on a blank slate. They operate within deeply entangled systems burdened by legacy technical debt, siloed data, and compliance constraints. ICAs are designed precisely for such complexity. They help clarify whether to rehost, replatform, rewrite, retire—or delay action altogether—based on multiple vectors: business value, technical feasibility, ROI, team capability, sustainability, and regulatory risk.
Multi-agent systems deployed by TCS benefit from autonomy and advanced reasoning capabilities. In modernization programs, such as migrating from older to newer versions of technologies, agents understand existing code bases, create descriptive production documentation and engineer technology updates.
Here’s how it works:
Another example is database modernization. Migrating from one database technology to another is highly complex. It requires understanding data integrity, restructuring schemas, and managing compatibility issues between old and new systems. Agents assist here by reasoning through these changes, proposing optimized data schemas, and enabling more automated migration paths. These efforts have shown 40-50% reductions in time and labor.
AI visualizes these trade-offs in real time, allowing decision makers to interrogate assumptions, understand downstream impacts, and converge faster on a viable modernization plan. Human intervention remains critical to ensure technical accuracy but also to uphold responsible AI usage, maintain quality, and enforce ethical standards. These systems are evolving, but even today, they’re delivering measurable impact.
The vision is to move beyond fixed roadmaps to living systems that learn, adapt, and guide enterprise IT decisions as market, technology, and regulatory conditions continue to evolve. These systems will monitor key indicators like cloud costs, performance baselines, and operational anomalies, triggering timely nudges and recalibrations to keep modernization efforts on track.
If cloud spend spikes unexpectedly, ICAs can simulate cost-containment options such as right-sizing, workload shifting, or spot-instance strategies and present calibrated choices to IT leaders. Likewise, when performance baselines dip, the system can propose rebalancing workloads across regions or providers before end-user impact occurs. These nudges convert governance from retrospective reporting into proactive intervention.
ICAs offer a dual track strategy:
"An ICA-led approach to cloud drives two major shifts. First, decision-makers gain deeper, faster insights that traditional consulting can’t match. Second, focus moves from isolated choices to architecting environments where the best approach becomes self-evident." – Suranjan Chatterjee, Head of Technology, TCS
ICA raises the floor for decision quality across organizations. The result is faster scaling, more consistent outcomes, and a fundamental shift in how expertise is distributed. Enterprises that embrace ICAs build capabilities that evolve with every decision, turning cloud strategy into a continuous source of competitive advantage.