Here are the questions I hear most often from business leaders who are trying to use AI to solve business problems. If you don't find your question here, book a conversation and I'll give you a straight answer.
The most common failure pattern is buying ChatGPT or Copilot licenses before deciding what AI can actually do for your business. Start with a diagnostic to see how AI can free up bottlenecks and alleviate pain points. Once you assess the challenges and the opportunity, you need a strategy to ensure that your AI investments directly address your business objectives.
You're ready when: 1) leadership sees AI as a business priority and not just an IT project, 2) there are concrete business outcomes that AI can improve, and 3) you're willing to invest in adoption / behavior change as much as in the technology itself. Start with the free 2-minute readiness assessment on the home page to get a directional signal of where you stand related to people, data, process, governance and leadership alignment.
The strategy answers 'why AI, and what business outcomes are we pursuing.' The roadmap answers 'what we do in the next 30, 60, and 90 days.' Most companies have neither. Some have a roadmap that's actually a tool wish list or technology implementation plan.
The pattern is consistent: limited executive ownership/support, unclear success metrics, underinvestment in adoption and training, and no plan to scale beyond the pilot team.
Three things. First, I start with business goals, not technology. Second, my background includes 27 years advising Fortune 500 executives on communications, reputation, and transformation. That means I build the strategy alongside leadership and focus on the adoption and culture change that technology consultants often overlook. Third, I run this as a solo practice, so clients get direct access to the senior advisor, not a junior consultant who's learning on the job.
Mid-market companies with anywhere from $500K to $500M in annual revenue, across professional services, financial services, manufacturing, packaging, consumer goods, healthcare, and consulting. The common thread isn't industry, it's readiness and desire to change by taking an outcomes-focused approach to AI.
I am based in St. Louis, Missouri, and I serve mid-market clients across the United States. Most engagements are hybrid, a mix of virtual work with periodic onsite visits for leadership workshops, readiness assessment interviews, and key milestones.
Stakeholder interviews with leadership and key operators, technology inventory review, process observation where relevant, a structured readiness scorecard across five dimensions, a prioritized AI opportunity map with estimated business value and complexity, and an executive-ready briefing deck. Deliverables are designed to be usable by the client long after the engagement ends.
Yes, most clients continue with AI Activation Support after the assessment. The monthly engagement is based on your needs, and may include implementation coaching for pilot programs, monthly leadership briefings, change management support, adoption measurement, and governance guidance. This is where the assessment actually turns into results.
First useful pilot results typically appear within 60 to 90 days of engagement. Full strategic integration and measurable business outcomes usually take 6 to 12 months. The timeline depends on how many departments are involved, the maturity of existing data infrastructure, and the organization's capacity to absorb change. The methodology is designed to front-load quick wins in the pilot phase to build momentum.
Tool recommendations come after the strategy, not before. The right tool depends on the business problem, the team's technical capacity, the existing tech stack, and budget. In practice, most mid-market engagements involve either ChatGPT Team or Enterprise, Claude, Gemini, or Microsoft Copilot, and a small number of purpose-built vertical tools. I work closely with all the leading AI platforms and I take a tool-agnostic approach.
An AI governance framework for a mid-market company typically includes an acceptable use policy, data classification standards, an approved tool list, a lightweight review process for new AI use cases, training and communication standards, and a defined owner (usually a small cross-functional committee, not a new hire). Governance should be right-sized to the organization. Overengineered policies kill adoption.
Book a 30-minute conversation. I'll answer your questions directly, give you a read on your readiness, and only recommend an engagement if it actually makes sense.
