01
Activity without evidence
People use AI, but the company cannot connect that activity to a measurable change in a real workflow.
Teams are using AI. Results are still unclear.
Plenty of AI activity,
No measurable business impact
People are already trying AI at work. But no one owns implementation, and promising experiments depend on a few enthusiasts. The team cannot show what changed, measure the effect, or repeat the process.
Meanwhile, pressure to show progress keeps growing. Another general AI course may create activity, but not a working process the team can improve.
02
Enthusiasts become the system
A few motivated people carry the experiments. Their learning never becomes a shared team capability.
03
Implementation becomes extra work
Without protected time and a clear process, day-to-day work wins and promising ideas stay unfinished.
AI Workflow Lab
Your team creates measurable AI impact with our guidance
A guided 4-5 week program that helps your team turn scattered AI experiments into one measurable workflow - and prepares them to build the next
This is not a general AI course and it is not an outside team arriving with a finished automation. Training and implementation happen together. Your team sees the full cycle happen and leaves able to run it again.
How the Lab works
Use AI agents professionally
The Lab runs in Claude Cowork or ChatGPT Work, depending on your company’s approved environment. We teach the professional practices behind agent work: structuring tasks, providing context, setting boundaries and review points, and turning one-off prompts into workflows.
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01
Build a practical foundation
The team develops a shared, practical understanding of AI agents: what they can do, where human review belongs, and how to think in workflows rather than isolated prompts.
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02
Choose the process and the measure
We apply the learning to your team’s real work, select one bounded process, define the pilot, and agree on the baseline and the evidence that will matter.
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03
Build the pilot together
The team builds a working AI workflow around the selected process. We guide the design, boundaries, human checkpoints, testing, and the decisions needed to make it usable in real work.
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04
Measure what changed
The pilot is used in real conditions and compared with the baseline. The team looks at time, quality, risk, and the practical trade-offs - not just whether the tool produced an output.
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05
Prepare the next cycle
We review the evidence, decide what to improve, scale, or stop, appoint an internal owner, prioritise the next processes, and turn the first pilot into a repeatable way of working.
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06
Check that it continues
We review the 30-day plan, check whether measurement and ownership continued, and agree on the next practical step.
Tangible outcomes
The real outcome:
a team that can deliver measurable AI improvements
again and again
The first working pilot is the proof: your team has taken a real process from hypothesis to evidence in its own environment. Beyond the pilot, the team leaves with the method, ownership, and plan to run the cycle again.
01
A working AI pilot
One bounded workflow used in real work - proof that the team can move from process to measurable result.
02
A repeatable method
A shared cycle for finding, prioritising, implementing, and measuring the next AI improvement.
03
Clear evidence of impact
A scorecard recording the baseline, result, quality, risks, and the decision that follows.
04
A prioritised process backlog
The next opportunities ranked by expected value and feasibility.
05
A named internal owner
One participant takes responsibility for keeping the next cycle moving.
06
A 30-day implementation plan
Clear actions and protected time for the next cycle, reviewed together 30 days later.
The people behind the Lab
Built on 15 years of production leadership
Egor and Kate built the AI Workflow Lab from first-hand experience running production businesses, leading client work and teams, and introducing AI tools into their own operations.
Kate Chernova
Process and delivery
Kate brings 15 years of CG production experience, including as Head of Production, delivering work for Disney, Discovery, and Allianz and leading teams of more than 20 people.
LinkedInPractical questions
What teams usually want to know before we start
Is this a general AI course?
No. The team learns practical AI-agent skills, but the program is organised around a real company process, a working pilot, and evidence from real use.
Do we need to choose the pilot before the program starts?
No. After the practical foundation, we run a dedicated process-to-pilot workshop to select the process, scope the pilot, and define the baseline. Access to real work and the people who understand it is still essential.
Which AI tools do you use?
We work in Claude Cowork or ChatGPT Work, depending on the company’s approved environment. The Lab teaches professional use of these agentic workspaces while keeping access, data boundaries, and human review aligned with company policy.
How much time does the team need?
The team should reserve approximately 16-20 hours in its working schedule. The full process takes 4-5 weeks.
When will we see a result?
With protected team time and a suitable process, the first working pilot can be ready by the third week. The evidence window and final review bring the measured result and a clear next-step plan together in approximately 4-5 weeks.
What happens after the first pilot?
The team leaves with an internal owner, an impact scorecard, a prioritised backlog, a 30-day implementation plan, and a practical way to run the next cycle. We review the continuation in the 30-day follow-up.
Can the Lab be delivered onsite?
Yes. Live remote is the standard format; onsite delivery is available separately.
How many people can participate?
The Lab is designed for one working group of 5-12 participants.
Start with the work
Bring us the process your team wants to improve
We will discuss where AI is already showing up, what could be measured, and whether the AI Workflow Lab is the right next step.
Discuss the Lab