Mapping the Landscape: Building Your AI Readiness Frameworks
Previously, I talked about the messy middle between AI ambition and operational reality for chemical and ingredient enterprises. AI readiness isn’t a feature you can bolt on at the end, it’s a foundational capability. And in the chemical industry, that foundation is uniquely hard to build. Complex legacy systems, fragmented data and disconnected teams are the rule, not the exception.
But what does “readiness” actually look like in practice? If you’re a CTO leading digital transformation inside a complex enterprise, where do you start?
Fortunately, you don’t need to start from scratch. Several global firms have released AI frameworks that can help teams assess maturity, set priorities, and guide investments. But they’re not plug-and-play, especially not in a high-variability, multi-system environment like chemicals. Let’s break down a few of the most widely cited frameworks and unpack how they apply in a chemical industry context.
Deloitte: Trustworthy AI as a Readiness Lens
Deloitte's Trustworthy AI Framework anchors readiness in trust. It identifies six key characteristics that should be embedded into AI systems from the start:
This aligns closely with NIST’s AI Risk Management Framework, which emphasizes a structured approach to managing risk across the AI lifecycle. For highly regulated industries like chemicals — where safety, compliance, and traceability are non-negotiable — this lens is particularly relevant. Readiness in this sector isn’t just about compute power or model performance; it’s about making AI systems resilient, auditable, and aligned with organizational values.
Use this if: Your roadmap is constrained by regulatory, safety, or governance concerns. It’s especially useful for large, global players looking to embed AI within critical operations or high-risk environments.
McKinsey: Maturity Across Four Dimensions
McKinsey’s Global AI Trust Maturity Survey takes a diagnostic approach, asking participants to gauge their readiness across four dimensions:
Self-Rated Maturity Score
Share of respondents who are at level 3 maturity level and above in four leading responsible AI (RAI) practices, %
What’s telling is how few companies are there: the average self-rated maturity score is 2.0 out of 4.0. Just 24% say their Strategy is at Level 3 (Taking steps to develop all necessary RAI practices) or higher. 55% are actively working to mitigate inaccuracy, the top AI risk identified.
For chemical enterprises, this approach is useful not just for benchmarking, but for understanding what “maturity” actually looks like: when systems, people, and processes must evolve together.
Use this if: You’re looking to benchmark maturity and set goals across functions. It’s particularly helpful in organizations trying to scale from pilot projects to enterprise-wide impact.
PwC: Six Pillars of Readiness
PwC Poland’s recent report, Ready for Artificial Intelligence, offers a practitioner-oriented view rooted in survey data. The framework emphasizes six pillars:
Notably, 55% of surveyed companies had a defined AI strategy or were actively working on one, yet most projects were still in early stages, with few advanced production deployments. For the chemical sector — where use cases often span from lab to logistics — this gap between
69% of respondents report that their companies lack sufficient AI experts
>60% of respondents are familiar with generative AI]
Use this if: You want a structured checklist that balances technical and organizational dimensions. It’s a practical framework to guide conversations between business and tech leaders.
BCG: National Strategy, Enterprise Relevance
BCG’s ASPIRE framework is designed for national governments, but its components are highly relevant at the enterprise level:
The emphasis here is on systemic readiness. That’s particularly important in chemicals, where no single department can "own" AI. From feedstock procurement to customer service, value is created in the seams.
Use this if: You’re building a long-range transformation strategy that includes ecosystem collaboration — with suppliers, customers, or public institutions.
Choosing a Framework (Or Building Your Own)
No single framework has it all. Each reflects the priorities of the people who built it:
For leaders in the chemical industry, the real opportunity lies in pulling the most relevant elements from each and building a model that fits your business, your systems, and your people.
But even with a framework in hand, two deceptively simple questions matter most:
Getting crisp on those answers — and being honest about the gaps in between — is where real transformation begins.
Inside the Stack is a no-fluff series from Knowde’s CTO, Wojciech Krupa that explores the real technical work behind digital transformation in the chemical industry. From cleaning up messy data to modernizing legacy infrastructure, it’s a roadmap for CTOs ready to move beyond the buzzwords and start building.