I have written extensively about the changes in the traditional consulting model moving forward. One way to protect yourself is to productize your services in your specialty area. The better way to protect yourself is by productizing an AI consulting model. AI consulting is expected to be a 1T market over the next ten years and the supply of qualified AI consultants could not even cover a meaningful portion of the demand.
AI consulting lends itself to continuous recurring revenue engagements because it is changing rapidly and it is expected to continue changing rapidly. The best AI consultants will not react to every change or new model release. They will be able to discern what changes are critical and which ones are not. In either case, they are bringing value every day.
You need to make the change now from a manual based consulting practice to an AI productized consulting practice. I am going to give you 10 diagnostics over the next 30 days that give you everything that you need to start and develop an AI consulting practice. I have used diagnostics for years and I designed them to be far more robust than any diagnostics on the market. DM me to unlock the most valuable door in the consulting profession, AI Readiness.
Each diagnostic does the following things.
Each diagnostic is based on ten factors and five questions per factor that are scored from strongly disagree to strongly agree and take ten minutes to complete.
The questions are assigned a score from 1 to 5 and they are summed to create a score for each factor.
The diagnostics identifies your three strongest factors and your three weakest factors.
The diagnostics provide you with three strategies to double your strengths and three strategies to halve your weaknesses.
Each strength has 30 day, 90 day, and 12 month plans that are highly detailed and establish a strong action, the owner of the action, the expected outcome, and a series of success metrics with KPI's to track
Each weakness identifies several potential root causes and provides 30 day, 90 day, and 12 month plans that are highly detailed and establish a strong action, the owner of the action, the expected outcome, and success metrics with KPI's to track.
The diagnostics conclude with an immediately actionable next step.
The ten factors in the AI Readiness Diagnostic are:
AI Strategy and Business Alignment — Whether the organization has a clear strategic rationale for AI and understands how it connects to business outcomes. It treats AI as a business transformation issue tied to growth, margin, productivity, quality, risk, or enterprise value rather than a standalone technology initiative.
Leadership Commitment and Decision Rights — Whether leadership has visible ownership, accountability, budget, and decision rights for AI adoption. It covers who approves AI priorities, investments, tools, and risk exceptions, and whether leaders are aligned on pace, scope, and risk tolerance.
Use Case Identification and Prioritization — Whether the organization can identify, evaluate, and prioritize AI use cases based on value, feasibility, and risk. It distinguishes attractive ideas from genuinely implementable opportunities and has a process for deciding what to scale, pause, or stop.
Workflow and Process Readiness — Whether workflows are documented and mature enough to support AI-enabled redesign or automation. It assesses understanding of inputs, outputs, decision points, and which steps need human judgment versus AI assistance.
Data Readiness and Knowledge Infrastructure - Whether the organization has reliable, accessible, governed data and knowledge assets for AI use. It covers data quality, ownership, access controls, and whether the infrastructure can support safe, useful AI outputs.
Technology and Integration Readiness — Whether the organization has the technology environment, platforms, security, and integration capability required for AI adoption. It addresses tool selection, system access, technical expertise, and identifying constraints before they derail implementation.
AI Governance, Risk, and Compliance Readiness — Whether the organization has the governance controls needed to adopt AI safely and responsibly. It covers acceptable-use policies, sensitive-data handling, human-review requirements, and risk classification without unnecessarily blocking innovation.
Talent, Training, and Adoption Capacity — Whether the organization has the skills, confidence, training, and support required for AI readiness. It assesses role-relevant training, internal champions, manager preparedness, and a culture that preserves judgment, quality, and trust.
Change Management and Operating Model Readiness — Whether the organization can manage the behavioral, organizational, and operating-model changes AI requires. It covers communication, addressing resistance, and reinforcing adoption after initial tools or workflows launch.
ROI, Metrics, and Value Realization — Whether the organization can define, track, and prove the business value of AI adoption. It covers setting baselines and success metrics before launch and distinguishing AI activity from real productivity, operational, EBITDA, and enterprise-value impact.