Many organizations have run an AI pilot by now. The interesting question is: what happened next?
MIT's Center for Information Systems Research (CISR) has been tracking this question across hundreds of companies. Their finding is of critical importance to business leaders - enterprises stuck in the pilot stage have financial performance below their industry average. On the flip side - those that successfully scale AI perform well above average. The gap between experimenting and industrializing is where competitive advantage is won or lost.
The four stages of AI maturity
MIT CISR identifies four stages that enterprises move through:
Stage 1: Educate and Experiment. Organizations focus on AI literacy, policies, and early experiments. They're getting comfortable with the technology and identifying where it might create value.
Stage 2: Pilot and Build Capabilities. Teams run systematic pilots, track value, consolidate data, and tell stories about what they're learning. This is where many organizations are today - and where many of them get stuck.
Stage 3: Scale and Industrialize. AI is embedded across the organization with reusable architecture, transparent dashboards, and a pervasive test-and-learn culture. This is where financial performance jumps above industry average.
Stage 4: AI Future-Ready. AI is embedded in all decision-making, proprietary AI is developed internally, and new services are built on AI capabilities. Only about 7% of enterprises reach this stage.
Incidentaly, we had a similar AI maturity level model we shared with enterprise customers in the digital innovation team, so the MIT model made a lot of sense to me. The critical insight that MIT found is that the leap from Stage 2 to Stage 3 is where the value unlocks. And it's also where most organizations fail.
The four challenges: Strategy, Systems, Synchronization, Stewardship
Based on interviews with executives navigating this transition, MIT CISR identified four challenges that must be addressed together:
Strategy means aligning AI investments with business goals and measurable value - not just exploring what's possible, but prioritizing what matters. Guardian Life Insurance addressed this by creating a value-tracking framework with three phases: developing hypotheses with business leaders, testing solutions and building business cases, and creating plans to scale. This discipline helped them focus on high-impact initiatives rather than scattered experiments.
Systems requires building modular, interoperable platforms that enable enterprise-wide intelligence. You can't scale AI on fragmented data and legacy infrastructure. Both Guardian and Italgas invested heavily in modernizing their data architecture before attempting to scale AI use cases. Italgas built a cloud-based platform with IoT integration, a data platform managing over 300TB, and self-service analytics - modular components that multiple teams could build upon.
Synchronization is about people: creating AI-ready teams while redesigning work around AI capabilities. This isn't just training - it's rethinking roles and how work gets done. Italgas engaged over 1,000 employees in innovation initiatives and delivered 30,000 training hours focused on AI. Guardian pulled people from regular roles to focus on AI transformation while exploring rotations to build diverse skills.
Stewardship involves embedding compliant, human-centered, and transparent AI practices by design. In regulated industries this is non-negotiable, but even in other sectors, governance determines whether AI can scale without creating unacceptable risk. Guardian codified potential barriers and created both formal and fast-track review processes. Italgas created a dedicated AI director and group AI office to oversee integration across all business processes.
Why teams get stuck
The transition from pilots to production is a major organizational change. It encounters both human resistance and technological complexity. A successful pilot proves that AI can work. Scaling proves that your organization can change how it works.
This is why MIT CISR emphasizes that the transition requires a united leadership team - the CEO, CIO, chief strategy officer, and head of human resources working together. Without this alignment, companies remain in pilot stage indefinitely, running experiments that never compound into enterprise value.
So where are you?
Which stage is your organization in? More importantly: what's blocking the move to the next stage? Is it strategy (unclear priorities), systems (fragmented infrastructure), synchronization (people and skills), or stewardship (governance gaps)?
The organizations pulling ahead aren't necessarily using more sophisticated AI. They're the ones that have figured out how to scale what works.
For a deeper look at this research, including detailed case studies of Guardian Life Insurance and Italgas Group, listen to the MIT CISR audio briefing "Grow Enterprise AI Maturity for Bottom-Line Impact" by Stephanie Woerner and her colleagues. The 15-minute recording walks through the four-stage framework and how leading enterprises are navigating the transition from pilots to production.