Introduction

Artificial intelligence has advanced rapidly, but scaling beyond pilots requires more than powerful models and demands operational context. Without a clear link to how a business actually runs, even the most sophisticated AI can miss critical constraints and produce results that fall flat in real-world processes.

What Happened

A partnership between MIT Technology Review and Celonis examined how context-aware AI can bridge the gap between experimental demos and production-ready enterprise value. The conversation, hosted by Megan Tatum, featured Devavrat Shah, chief scientist of enterprise AI at Celonis, who argued that agents must understand their role within broader decision-making processes to deliver real impact.

Why This Matters

When AI operates without context, organizations face costly mistakes, wasted compute, and decisions that ignore the very processes they’re meant to improve. A dedicated context layer connects AI to existing workflow data, enabling agents to act within guardrails, produce reviewable recommendations, and free human talent for strategic work rather than routine friction.

Key Takeaways

  • Context-aware AI aligns agent actions with operational reality, reducing risk and compute waste.
  • Celonis delivers this context through process intelligence and a real-time digital twin of enterprise workflows.
  • Starting with a specific decision, mapping its hardest steps, and measuring speed and value determines whether an application scales.
  • Workforce education and clear governance ensure humans can interpret AI outputs and maintain appropriate oversight.
  • Future applications span finance planning, workforce management, marketing optimization, and sales acceleration.

Conclusion

Context-aware AI isn't a technical add-on; it's the foundation for enterprise AI that actually delivers measurable value. By grounding agents in process reality, organizations can accelerate decisions, reduce risk, and empower their teams to focus on the work that matters most.