If you are considering starting with AI within your organisation, there is a question worth asking:
What prerequisites must be in place before launching an AI initiative?
AI is no longer just a technology experiment. It is the mirror for your organisational readiness. This means AI doesn’t just expose system flaws. It highlights gaps in strategy, processes, and adoption. Leaders often focus on tools and dashboards.
In this newsletter, I explore why technology alone doesn’t guarantee value, and how leaders can turn readiness into impact.
AI is everywhere. But Are We Ready?
Executives today face endless AI opportunities, but with very little practical guidance. Media narratives often skip over the realities of legacy systems, fragmented processes, and technical debt.
All too often, “doing AI” looks like:
- Renaming a department “AI & Innovation,”
- Running a single pilot that never scales,
- Buying a tool promising instant insights for sales reps.
Rarely does it mean embedding AI into decision-making, operations, and governance. At the same time, too many organisations overestimate their readiness.
Even experienced organisations struggle with pilot paralysis, overconfidence in data quality, growing technical debt, and weak governance models.
To be honest, we are no exception. Like everyone else, we’re still learning. In many ways, we’re all experimenting in real time.
What experience has taught me, though, is this: When new technology creates uncertainty, it’s often wise to lean on proven methods and disciplines to bring structure to the conversation.
That’s where a maturity assessment becomes more than a checkbox. It becomes a way to replace assumptions with facts.
Step 1: Assessing Your AI Readiness
AI readiness is not about tools. It is about preparation. An AI maturity assessment evaluates how prepared your organisation is to adopt, scale, and extract value from AI. Not whether it has the latest models or tools
Before thinking about models, agents, or dashboards, executives need to understand why AI initiatives stall. Here are the dimensions that most commonly create gaps between ambition and reality:
- Strategy & Leadership: Are AI objectives clear and aligned to business goals?
- Data & Platform: Is data clean, integrated, and structured?
- Technology & Tools: Is infrastructure ready for AI deployment?
- Organisational Capability: Are skills, training, and collaboration in place?
- Governance & Ethics: Are responsible AI policies defined?
- Use Cases & Value: Are initiatives prioritised for impact?
- Culture & Change: Are teams ready to act on AI insights?
Executive takeaway: This assessment reveals the gaps that prevent pilots from scaling and identifies where leadership focus is most needed.
Most leading frameworks (Gartner, Accenture, EY, Deloitte, IBM, BearingPoint) offer five-level progressions:
- Initial / Unready – Minimal preparation
- Awareness / Planning – Discussions underway, no pilots
- Pilot-Ready / Defined – Data cleaned, workflows mapped, governance frameworks in place
- Implementation / Scaling – Pilots integrated, adoption tracked, governance active
- Optimised / Transformational – AI fully embedded, continuously improving
The most frequently cited barriers to AI adoption according to McKinsey:
This graph is slightly dated but, still, presents good information to understand the gaps from an organisation perspective
Guiding principle: Focus on gaps that block value at scale, not just pilot success.
Step 2: AI Readiness in Salesforce
Even with a strong CRM, AI only delivers value if the organisation is ready to leverage it effectively. Technology can amplify gaps if readiness is missing.
For organisations using Salesforce, a second layer is required. The Salesforce AI maturity assessment tailors readiness to the Salesforce ecosystem, focusing on how it can realistically create value within the existing CRM environment.
Key areas for reflection:
- Salesforce landscape: Clouds, customisations, licenses
- Data readiness: Quality, structure, governance, compliance
- Workflow & process readiness: Mapped, consistent, adaptable for AI automation
- Integration: Can AI outputs reliably interact with other systems?
- User trust & adoption: Are teams willing to act on AI insights?
- Governance & ethics: Roles, policies, and oversight defined
- Scalability planning: Can AI grow without creating technical debt?
Key questions for executives:
- Are AI initiatives aligned with CRM strategy and business objectives?
- Which processes benefit most from AI insights or automation?
- Is Salesforce data clean and structured enough to train AI models?
- How flexible is the Salesforce org to support AI features without creating technical debt?
- Can AI scale across teams, regions, and clouds without bottlenecks?
- Are governance and ethical policies in place to secure AI outputs?
Pro tip: External assessments often provide the clarity and objectivity needed to uncover gaps executives may overlook.
Introducing My Personal AI Maturity Framework for CRM and Salesforce
While leading firms like Gartner, Accenture, or EY provide general frameworks, I have developed my personal AI Maturity Framework, tailored to CRM and Salesforce environments. It follows the same general progression but adds granularity for pilots and scaling in real Salesforce workflows:
- Pilot / Proof-of-Value: Split into single-unit and multi-unit stages, reflecting how AI adoption in CRM often starts in one team or cloud before expanding.
- Scaling: Distinguishes between business-unit and organisation-wide deployment, highlighting progressive integration into workflows, governance, and adoption.
This approach ensures the alignment with industry standards while providing guidance for integrating AI realistically within Salesforce.
Step 3: Visualise AI Maturity
See where readiness gaps hide, then decide where to act first.
To help executives reflect on their readiness, here are the AI maturity levels (CRM / Salesforce focus):
Purpose for executives: This framework is a reflection tool, showing where readiness gaps could block AI impact. Not a step-by-step implementation guide.
Reflection questions:
- Where are we on this spectrum today?
- Are our pilots stepping stones or decorative experiments?
- Which gaps in readiness quietly block scaling across Salesforce clouds and teams?
Step 4: Prioritise AI Use Cases
Once maturity is understood, focus and structure matter more than ambition. The real risk is investing in the wrong initiatives, chasing too many “interesting” use cases without clear prioritisation. This leads to fragmented pilots, limited adoption, and growing technical debt.
Prioritisation Principles:
- Strategic Impact: Solve high-frequency, high-value problems, not assumed ones.
- Operational Feasibility: Ensure solutions are simple, scalable, aligned with CRM workflows. Avoid technical debt.
- Adoption & Culture: Validate assumptions with frontline teams. Measure outcomes in behavior, not dashboards.
When to introduce AI agents: Only when processes are stable, data models consistent, exceptions rare, and escalation paths clear. Avoid agents for one-off tasks or unreliable data.
Quick Reflection for Leaders
- AI scales where strategy, data, and adoption intersect
- Treat AI maturity as evolving, not a one-off project
- CRM platform readiness is non-negotiable
- Invest in data quality, process alignment, AI literacy, and governance
- Start with readiness before chasing features
- Prioritise high-value, feasible use cases
- Early pilots matter, but scaling requires a solid foundation
Lesson: AI maturity is a strategic journey that balances operational AI initiatives with organisational transformation in leadership, culture, and adoption.
Food for thought: Are your AI pilots truly preparing your organisation for impact, or are they just “decorative experiments”?