The AI Integration Workshop: who it's for, what it costs, what a team walks away with

This is a direct service breakdown. If you are evaluating the AI Integration Workshop, this is what you need to know.

The workshop is for teams that already use AI and can feel the gap between individual usage and shared capability. The outputs may be technically adequate, but generic. Different people may get different results from similar tasks. The initial enthusiasm may have settled into a more honest feeling: AI is useful for certain things, but it is not becoming the operating advantage the team expected.

It is also for teams about to formalize AI use and wanting to build the foundation properly before the habits become messy, and it is not a tool-training session. I am not teaching your team how to use ChatGPT or Claude in isolation. The work is the organizational layer underneath the tools: the workflow, knowledge structure, evaluation model, and decision logic that make any useful tool perform better in your context.

Who it fits

The workshop makes sense when the team has enough AI usage to know where the friction is. Someone has tried to draft tickets with AI. Someone has asked it to summarize research, critique flows, write copy, or produce first-pass analysis. The team has seen enough promise to keep using it, and enough inconsistency to know the current approach will not scale by itself.

It is less useful for a team starting from absolute zero, where the first problem is exposure and habit formation rather than integration.

The best-fit team usually has three symptoms:

• Useful AI outputs depend too heavily on the person prompting.
• The same product context keeps getting re-explained.
• Nobody has a shared standard for what good AI-assisted work looks like.

That last point matters more than most teams expect. AI adoption fails quietly when each person builds a private method and the organization never turns those methods into a shared operating system.

The six-week structure

The current workshop is a six-week engagement, not a one-off training day.

Week 1: Discovery. I map the business, team, and real operating problem. Where do decisions get made? Where does knowledge actually live: Notion, people’s heads, Slack threads, product meetings, support history, or a folder nobody opens? Where is AI currently failing the team, and is the failure really an AI problem or a workflow problem?

Week 2: Mapping. We separate AI-suited work from workflow problems and problems that need neither. This is where the team stops treating AI as a universal answer and starts seeing where it belongs in the work.

Week 3: Prioritization. We rank opportunities, choose the right interventions, and align on the workflows worth improving first. The goal is not to generate a long AI wish list; it is to choose the few use cases where better context and better process will actually change output quality.

Weeks 4 and 5: Guided implementation. Your team builds with guidance. This is where the workflow, knowledge structure, prompts, standards, and evaluation habits start becoming real. The work should leave the team with operating muscle, not just a deck.

Week 6: Handover. We codify what was built, transfer the evaluation model, and decide what the team should maintain, expand, or stop doing.

What the team walks away with

The output is not a prompt library by itself. A prompt library can be useful, but only when the context around it is maintained.

The team should leave with a clearer map of the workflows where AI actually helps, a ranked view of AI opportunities versus workflow problems, a knowledge-transfer model for maintaining context, and a working implementation path that the team understands because they helped build it.

The most valuable outcome is usually diagnostic instinct. The team gets better at telling the difference between a weak prompt, a stale context layer, an unclear workflow, and a use case that should not be automated in the first place. Those problems look similar from the outside, but they require different fixes.

What it costs

The service page is the source of truth: the AI Integration Workshop starts from AED 36.7K for a single-team baseline.

That baseline covers a focused team, a focused problem, the six-week structure, workflow mapping, AI opportunity ranking, guided implementation planning, and knowledge transfer. Multi-stream or enterprise versions are scoped separately because the work changes when the context crosses departments, geographies, governance needs, or multiple workflows.

Pricing depends on team size, number of workflows, implementation depth, stakeholder access, existing AI maturity, governance needs, and how much knowledge transfer has to be built into the engagement.

How to tell if it is the right fit

A scoping call should answer three questions quickly: what AI is already being used for, where the team feels the most friction, and whether the real problem is AI capability, workflow clarity, or both.

Scattered AI use and inconsistent output usually point toward the workshop. A deeper product operating problem may need a Product Systems Audit first, while newsroom-specific transformation may fit the Newsroom AI Transformation Sprint better.

Book a scoping call if you want to find out where your team sits.