A Checklist for AI Transformation Readiness

AI transformation in 2026 works best when strategy and execution run as one integrated system, each refining the other as implementation surfaces what holds up and what does not.

Strategy work today is substantial and ongoing. Use case definition, ROI modeling, KPI and OKR design, adoption rollout, tech stack assessment, change management. None of these are one-time deliverables. They evolve every quarter as the organization learns what AI actually does in production. The implementation patterns that work are the ones built to feed that learning back to the strategy in real time.

The numbers on what is at stake are consistent across recent research. Gartner reports that only 54% of AI projects make it from pilot to production. RAND Corporation found that 84% of failed AI transformations failed for leadership and organizational reasons, not technical ones. Each failed pilot costs an enterprise between $500,000 and $2 million. The 2026 DORA report from Google Cloud, ROI of AI-Assisted Software Development, locates the cause from a different angle. Nathen Harvey, DORA's team lead, said it directly:

"The greatest returns on AI investment come not from the tools themselves but from a strategic focus on the underlying organizational system: the quality of the internal platform, the clarity of workflows, and the alignment of teams. Without this foundation, AI creates localized pockets of productivity that are often lost in downstream chaos."

Here are a few areas to focus on as the head of AI within your organization to ensure a successful implementation:

» Data and knowledge infrastructure

The most common reason RAG systems underperform is that the organization's knowledge was never structured for machine consumption.

Before scoping any AI build, confirm that institutional knowledge is machine-readable, core business data has a single source of truth, and anything that cannot be sent to an external model for compliance reasons is identified upfront.

• Is institutional knowledge accessible?

• Is core business data centralized or fragmented across systems?

• What data is off-limits to external models, and why?

» Systems and integration landscape

Agents are only as useful as the systems they can reach.

• What are your sources of truth?

• Do they have documented APIs?

• What are humans doing manually that a connected system could do automatically?

» Workflow and process definition

Well-documented workflows are the prerequisite for agentic automation. If a process breaks down when the person who owns it is unavailable, it is not ready for an agent.

• Which workflows are documented well enough for an agent to execute?

• Which high-volume tasks have the clearest inputs, outputs, and decision rules?

» Team and adoption readiness

Adoption failures are rarely technical. Identify who is already using AI informally, confirm there is an executive sponsor with enough authority to mandate adoption, and surface resistance early enough to address it in the strategy rather than during rollout.

• Is there an executive sponsor with real authority over adoption?

• Who is already using AI tools informally, and what are they using them for?

• Who owns AI governance post-deployment?

» Security and compliance

Legal and compliance constraints shape the architecture before a line of code is written. Regulatory requirements, data handling agreements, and acceptable use policies need to be resolved in the strategy phase, not discovered during implementation.

• Have legal and compliance reviewed third-party LLM API usage against data agreements?

• What are the regulatory constraints on data handling in this industry?

• What is the incident response protocol for harmful or incorrect AI output?

» Measurement and accountability

Without a baseline, there is no defensible ROI. Define what success looks like at 90 days, identify who is accountable for it, and establish at minimum an informal baseline for the processes being targeted before the build begins.

• What does success look like at 90 days, and who owns it?

• What is the current baseline for the processes being targeted?

» Vendor and partner readiness

The strategy is only as strong as the team executing it. Confirm that the implementation partner can run in parallel with the strategy engagement, that code and infrastructure ownership is defined from day one, and that there is a clear model for what ongoing support looks like after the initial build.

• Can the implementation partner run alongside the strategy, or only after it concludes?

• Who owns the code, infrastructure, and documentation at engagement end?

• What does ongoing support look like after the initial build?

Dom & Tom is the product and technology team for companies with the vision but not the bandwidth to build it. We work like an in-house team, without the overhead. We partner with strategy consultancies, fractional CTOs, and internal transformation leads who run strategy and execution as one system.

👋 Are you looking for a team to implement your AI strategy? Book a call with us ↗