AI Clarity Workshops
A facilitated on-site engagement for leadership teams to evaluate AI readiness, explore realistic use cases, assess the ROI of high-impact initiatives, and develop a roadmap rooted in phased delivery, from pilot to scale, to minimize risk.
Why This Workshop Exists
The six places mid-market AI efforts
get stuck before they start.
We know AI matters. We're not sure where to start.
Most teams that stall aren’t short on interest, they’re short on a starting point that isn’t “buy a tool and see what happens.” Readiness research backs this up: organizations with a clearly defined AI strategy see meaningfully better outcomes than those without one, and starting from a tool instead of a target outcome is one of the most common reasons AI pilots never reach production.
We can't tell if it's AI, or automation with a new label.
This confusion runs in both directions. Internally, leadership teams often use “AI” and “automation” interchangeably when deciding where either one applies. Externally, the market hasn’t helped, vendors routinely market basic rules-based automation as AI-powered, a pattern significant enough that regulators now pursue it as a form of securities or consumer fraud. If a team can’t draw that line internally, evaluating a vendor’s claims is close to impossible.
We're not sure our data is ready.
This instinct is usually correct, and it’s worth surfacing before a contract gets signed rather than after. Recent mid-market data readiness research found only a small minority of organizations report their data is genuinely AI-ready, and data quality consistently ranks among the top barriers cited by mid-market IT leaders trying to scale past a pilot.
Knowledge gap.
This is broader than “has the team used ChatGPT, Claude, Gemini or CoPilot.” Studies of professional workforces consistently find a meaningful share of employees have received contradictory guidance, or no formal guidance at all from leadership on how, or whether, to use AI in their work. That’s a leadership communication gap as much as a skills gap, and a generic training video doesn’t close it.
Alignment gap.
One we’d add from the research, not your list: surveys of executives and individual contributors at the same companies routinely show a wide gap in how each group perceives the organization’s AI progress and intent, leadership tends to overestimate both adoption and enthusiasm relative to what frontline staff actually report. Left unaddressed, that gap quietly undercuts adoption regardless of how sound the strategy is.
We don't have a phased path from pilot to payoff.
Having the right use case isn’t enough to get it funded. Most stalled AI initiatives don’t fail because the idea was bad, they fail because the ROI was a guess instead of a real model, and there was no small first step before the big ask. That’s why big requests either get turned down, or get approved and then quietly shelved at the next budget review. The fix isn’t a bigger pitch, it’s a smaller first move: start with a proof-of-concept, measure it, improve it, then earn the case to go bigger.
A Pre-Assessment Survey and
Systems Map That Shape the Agenda
Every participant completes a short, confidential readiness survey before workshop day. It covers current AI literacy by role, tools already in informal or “shadow” use, the blockers people actually believe are holding adoption back, and what each person hopes the organization gets out of AI. Responses are aggregated and anonymized, the goal is an honest baseline, not a performance review.
Alongside the survey, we take the time to understand your business operations and build a department-level view of the applications that run your business and where the data lives. We take the time to understand your business, your digital maturity, and your application landscape, so we walk in with context and a baseline understanding of your business, not a blank slate.
Together, the survey and this organizational background serve as meaningful context to ensure the agenda is structured to your needs and every discussion is informed, not generic.
REPORTING
Aggregated and anonymized, individual responses are never shared. Includes an Enterprise Resource Map of the software applications in use across the organization.
USED FOR
Creating Phase 1 and 2 of the workshop agenda, so we're not reteaching what the participants already know.
Workshop Tailored to
your organization's AI journey
Built for mid-market leadership teams: four to six short sessions, from “where do we start?” to a prioritized AI plan.
Each workshop is tailored to your organization. Sessions typically run 2–4 hours, spaced 1–2 weeks apart..
Session 1
Assess & Learn
Assess: Find out where your team stands on AI knowledge today, so everyone works from the same facts.
Learn: Get a vendor-neutral primer: how AI is used in your industry, what it can and can’t reliably do, and learn the difference AI applications: between generative AI, predictive analytics and agentic AI.
Shared knowledge
Session 2
Use Case Exploration
Explore: A collaborative working session where your team identifies use cases using a framework we give you.
Analyze: In parallel, we review your enterprise architecture and the state of your business applications to help spark potential use cases and analyze data readiness to support AI application.
A list of use caes
Session 3
Evaluate with Discipline
Framework: Fit AI into your existing IT strategy using the framework businesses already use for technical and process change: ROI, cross-functional impact, technical feasibility, data readiness, risk and change management.
Score: Every idea is scored the same way you assess your other projects.
A ranked short-list
Session 4
Vision & Roadmap
We combine what you’ve learned, your prioritized use cases and what’s possible to help craft a realistic AI vision and roadmap.
A practical roadmap
Built for Leaders & Cross-Functional Teams
CEO and Founders
You’ve set the AI direction. Six months later the team is still debating where to start. This session gets your leadership team aligned and moving.
IT
Leaders
You’re fielding vendor proposals and internal requests with no neutral framework to evaluate them. The workshop gives you a structured way to assess options and set clear priorities.
Finance
Teams
You’re being asked to approve AI investments without a clear ROI framework. This session gives you the tools to evaluate proposals, stress-test assumptions, and set guardrails before budget is committed.
Functional Leaders
Your team is buried in manual work and asking for AI tools. You need a practical way to assess what’s worth pursuing, what isn’t, and how to sequence it without disrupting what’s already working.