AI/Gen AI discovery workshop

An AI discovery workshop is a structured, facilitated session—typically 3–4 hours—where your team identifies, scores, and prioritizes the AI and Gen AI opportunities most relevant to your workflows. The format is convergent by design: it moves from broad context (your operations, your pain points) to a short, qualified list your organization can execute.

Two inputs anchor every session. The first is your internal context—the workflows, project patterns, operational frictions, and strategic priorities your team brings. The second is curated real-world Gen AI examples from comparable organizations, selected by the facilitators and calibrated to your industry. Together, they let participants project realistically into what would and wouldn’t work in their environment.

From there, the session runs through four stages: surfacing problems and matching them to AI/Gen AI patterns; clustering and scoring ideas against business value, feasibility, data readiness, security fit, and adoption effort; refining the top candidates into qualified opportunities; and placing those opportunities on an effort/impact matrix with a recommended next step for each.

You leave with a session summary, a prioritized shortlist with scoring rationale, and qualification canvases for the top 2–3 opportunities, each with a recommended next engagement.

The standard AI/Gen AI discovery workshop runs for 4 hours. A condensed version covers the same flow and produces the same outputs in 3 hours, with tighter timeboxes and fewer workflows examined in depth. The right choice depends on how many business areas you want to cover, how complex your compliance and security constraints are, and how much time your team can commit. For executive AI workshops focused on strategic prioritization rather than workflow mapping, the 3-hour format is usually sufficient. For corporate AI workshops covering multiple functions or teams, the full 4-hour session gives more room. The session is online by default; in-person is available when requested.

The cost of an AI/Gen AI discovery workshop depends on the scope of preparation, the number of workflows covered, the format (3h vs. 4h), and the depth of the post-session deliverables. Preparation includes stakeholder interviews and customizing the use-case cards and facilitation assets for your industry and challenge areas—this is where most of the work happens before the session itself. To get a scoped estimate for your organization, the clearest starting point is a short introductory call. Contact the ITRex team to discuss your situation and what a workshop engagement would involve.

The most productive AI workshop sessions happen when the people who understand the business problem are in the same room as the people who understand the constraints. A balanced group of 6–12 participants typically includes a business or delivery sponsor, operations and product owners, engineering or architecture representatives, and at least one person from data, IT, or security.

Mix matters more than seniority. If only business stakeholders participate, ideas tend to be ambitious but technically ungrounded. If only technical people are present, the session risks anchoring on familiar solutions rather than high-value business problems. For executive AI workshops, the group is typically smaller and more senior, focused on portfolio-level prioritization and organizational readiness rather than granular workflow mapping.

You receive four things after the session. First, a session summary covering all ideas generated, grouped by theme and unfiltered. Second, a prioritized shortlist with multi-criteria scoring (business value, feasibility, data readiness, security fit, and adoption effort) and written rationale for each item—including what was explicitly set aside and why. Third, a qualification canvas for each of the top 2–3 opportunities, covering the problem, who benefits, the proposed AI/Gen AI implementation pattern, required data and systems, security and compliance fit, integration dependencies, adoption effort, success metrics, key risks, and guardrails. Fourth, a recommended next engagement for each opportunity: what the step is, what question or risk it addresses, and what decision it unlocks.

The workshop produces a qualified shortlist and a recommended next engagement for each opportunity. From there, the typical path is a feasibility sprint or proof of concept for the highest-priority item—a 2–6 week engagement that confirms data access, shapes the security architecture, and builds a thin working prototype on your real inputs to test output quality. If the PoC validates the opportunity, the next stage is a pilot: building the use case for one real team or workflow, integrating it with the systems it needs, and testing it with a defined user group. ITRex can support you at every stage from discovery through to production and scale. There’s no obligation to continue beyond the workshop and no single-vendor path—the qualification canvases are written in a way that lets you take them to any implementation partner.

A typical session generates 15–30 ideas across the workflows covered, depending on the number of participants and the breadth of the challenge areas explored. Not all of them will be Gen AI opportunities—some will be better served by traditional ML or rule-based automation, and those will be addressed separately rather than forced into a Gen AI shape.

From the full idea inventory, the prioritization process narrows the list to around 5–8 scored candidates. The final output is 2–3 fully qualified opportunities with enough detail to evaluate seriously and take forward. The goal is not to collect as many ideas as possible. It’s to identify the ones that are most relevant, most realistic, and most worth exploring next.

AI and Gen AI workshops for business are a focused starting point, not a full AI strategy or roadmap engagement. They give you a qualified shortlist of opportunities, a recommended next step for each, and a clear view of the effort, risk, and data readiness involved. That output feeds directly into a broader AI adoption roadmap—but it’s scoped to the use cases your team surfaced in the session, not your entire organization.

If you need a full AI readiness assessment covering infrastructure, data maturity, governance, talent, and a company-wide roadmap, ITRex offers that as a separate engagement. Many clients start with an AI discovery workshop to validate where the most tractable opportunities are, then commission a broader AI strategy consulting engagement once they have a clearer picture.

No. The workshop is designed to work with a mixed group—participants who understand the business operations and those who understand the technical and security constraints. The facilitation translates between both perspectives. What helps most is having participants who can speak concretely about where work slows down, where decisions get made with incomplete information, and where the highest-cost manual processes sit. The technical feasibility assessment is handled by the ITRex facilitators, not left to participants to figure out.

Each idea generated during the AI/Gen AI workshop is scored against five criteria: business value/impact (1–3), technical feasibility (1–3), data or knowledge readiness (1–3), security and compliance fit (1–3), and adoption or change effort (1–3). Higher scores are better on all criteria. The scoring is done as a group using dot voting, with the ITRex facilitators providing a read on feasibility based on their production experience.

For the top candidates, a full qualification canvas is completed covering the data path, integration dependencies, security posture, and key risks and guardrails. If data access is unclear or a security constraint is unresolved, that gets flagged explicitly rather than assumed away. The output for each top opportunity includes a recommended next engagement—often a short feasibility sprint—designed specifically to answer the questions the workshop couldn’t fully resolve.

An AI consultation is typically a conversation between your team and an advisor—useful for getting a read on a specific question, a build-vs-embed-vs-buy decision, or a technology choice. An AI discovery workshop is a structured group session designed to surface and qualify multiple opportunities across your workflows. It involves a preparation phase (stakeholder interviews, customized use-case cards), a facilitated in-session process with your team, and a set of post-session deliverables. The outputs are more operational than advisory: a scored shortlist, qualification canvases, and recommended next steps—not a report.

If you have a specific, well-defined technical question to resolve, an advisory session is the more efficient path. If you need to find where AI/Gen AI is worth applying across your business and build internal alignment around those decisions, a workshop is the right format.

Yes. The preparation phase for every AI/Gen AI discovery workshop includes customizing the use-case inspiration cards for your industry and challenge areas. The examples shown during the session are drawn from real AI and Gen AI deployments at comparable organizations—not generic technology overviews.

ITRex has delivered AI and Gen AI projects across digital health (HIPAA-compliant RAG pipelines, clinical workflow automation), financial services (anti-money laundering, KYC automation), logistics and supply chain (demand forecasting, route optimization), manufacturing (predictive maintenance, production analytics), retail and eCommerce (personalization, demand forecasting), and biotech and life sciences. For regulated industries, security and compliance fit is given explicit weight in the scoring and qualification process.

The use cases that surface most frequently in AI workshops for business fall into a few recurring categories: internal knowledge access (RAG-based assistants that let employees query internal documentation without IT tickets), customer-facing support automation (agents that handle routine inquiries, deflect support volume, and escalate edge cases to humans), content and document processing (drafting, summarization, classification, and extraction workflows that currently depend on manual effort), onboarding and training (Gen AI-based tools that reduce ramp-up time for new employees or sales teams), and operational decision support (assistants that surface relevant data at the point of a decision rather than requiring analysts to pull it). The specific opportunities that score highest in any given session depend on where the most friction exists in your workflows and what your data environment can realistically support.

Security and compliance fit is one of the five scoring criteria used to prioritize ideas during the session. For every top candidate, the qualification canvas includes a section covering data sensitivity, deployment posture (cloud, on-premises, or hybrid), the relevant compliance regime, and the guardrails required.

ITRex’s facilitation team includes practitioners with experience building HIPAA-compliant, GDPR-aligned, and financially regulated AI systems—so data privacy constraints are assessed against real architectural patterns, not generic recommendations. If a use case carries significant data sensitivity or a compliance path that isn’t clear, that’s flagged in the qualification canvas and factored into the recommended next engagement.