Licenses, models and enterprise tools.
Applied AI adoption for business teams
Turn AI licenses into workflows your team actually uses.
Corso finds where AI belongs in the work, trains employees live in the tools your company approved, and leaves you with reusable skills, agents, workflow playbooks and custom online learning.
The capability gap
Tool access is only the starting pointRandom prompts, uneven quality and shadow AI.
Trained people, repeatable workflows and reusable assets.
You already paid for the technology.
We build the capability around it.
Real workflows ↗
Role-based training ↗
Reusable agents and skills ↗
Safer daily use ↗
Capability that stays ↗
Real workflows ↗
Role-based training ↗
Reusable agents and skills ↗
Safer daily use ↗
Capability that stays ↗
The adoption problem
The tool is live. The new way of working is not.
Giving employees access to AI does not tell them where it helps, how to use company knowledge safely, or what good output looks like. Without a working system, adoption becomes improvised.
Usage is broad but shallow.
Employees draft messages and summaries, but the high-value workflows remain unchanged.
What leadership seesActivity without a clear business result.
Everyone invents their own method.
Prompts, sources and review standards vary by person, so output quality varies too.
What leadership seesInconsistent work and no repeatable process.
Unsafe work moves into the shadows.
When approved tools feel difficult, people find easier routes without clear data or review boundaries.
What leadership seesGovernance risk that policy alone cannot solve.
The learning disappears after the workshop.
One inspiring session creates interest, but not the resources employees need on Monday morning.
What leadership seesA temporary lift instead of lasting capability.
Your AI problem is rarely an access problem.
It is a workflow, capability and adoption problem.Why the capability layer matters
AI value compounds when people move from occasional use to structured workflows.
growth in weekly users of structured enterprise AI workflows in 2025
OpenAI enterprise report ↗of surveyed employees reported using AI tools not supplied by their employer
SAP / WalkMe survey ↗reported receiving extensive training on how to use AI effectively
SAP / WalkMe survey ↗of surveyed organizations reported advanced AI integration in 2026
The Conference Board ↗Industry evidence, not Corso client results. Outcomes depend on the workflow, tools, participation and implementation.
The Corso model
Train the people. Build the workflows. Keep the capability.
We do not teach AI in the abstract. We work inside your approved environment, with anonymized or approved examples of the work your employees already do.
See the 4-6 week sprint ↓
Live practice
Role-based training on real work
Employees practise with their own use cases, tools and quality standards.
Working assets
Skills, agents and repeatable workflows
We configure the reusable building blocks instead of leaving people with a blank chat window.
Learning that stays
Custom online learning and playbooks
Your team can revisit the method, onboard new colleagues and keep practice consistent.
Safe scaling
Sources, review and escalation rules
Every workflow defines what AI can do, what a human must check and when not to use it.
Not a generic prompt course
Start with work that happens every week.
Choose a team to see the difference between isolated AI use and a designed workflow.
Example workflow
Turn a meeting into an approved action plan Operations / recurring delivery meetings
Before
Someone copies notes into a general chat and rewrites the result by hand.
- No shared input standard
- Important decisions get lost
- Output depends on the individual
After
A defined agent uses the approved template, extracts owners and flags missing decisions for review.
- Approved source and prompt structure
- Named human review step
- Reusable output every week
The team keeps
Meeting agent
Action-plan template
5-minute lesson
Review checklist
The first engagement
AI Adoption Sprint
One team. Two to four workflows. A working capability you can evaluate before scaling.
Typical duration: 4-6 weeks-
01
Diagnose the workFind the workflows worth changing.
We interview the sponsor and team, map recurring work, confirm approved tools and establish a practical baseline.
-
02
Build the assetsTurn the method into something usable.
We configure workflow instructions, sources, skills, agents, templates and the human review path.
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03
Train livePractise on real, role-specific tasks.
The team learns by doing, receives feedback and understands both the value and the boundaries.
-
04
Produce learningMake the new capability repeatable.
We create short online learning, demonstrations, playbooks and quality checks for continued use.
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05
Measure and hand offDecide what is ready to scale.
We compare the workflow to its baseline, document ownership and create a backlog for the next use cases.
What you keep
The workshop ends. The capability does not.
The sprint is designed around assets your team can use, review and improve after Corso leaves. Delivery can live in your LMS or in a dedicated Ludus LMS environment.
Employees who can apply the workflows, review the output and help colleagues use them correctly.
Defined inputs, sources, instructions, outputs, ownership and review standards.
Skills, agents, GPTs, Gems, Projects or templates matched to the approved environment.
Short lessons, demonstrations and job aids people can revisit when the work happens.
Baseline findings, ownership, update rules and a prioritized backlog of the next workflows.
Measure the work, not the hype
A login is not an outcome.
We agree the useful signal before the sprint and measure the workflow at the point where value should appear.
Do not stop at
Seats activatedPrompts sentWorkshop attendance
Measure instead
Cycle timeRework and qualityWorkflow adoption
Example: how long it takes to turn an approved input into a reviewed client-ready output.
Where Corso fits
Best for companies that have the tools and need the working behaviour.
- Your company already has an approved AI environment.
- One team has recurring work worth redesigning.
- You want live practice and reusable learning.
- A sponsor can own adoption after the sprint.
- You still need to select the enterprise AI platform.
- The priority is data architecture or systems integration.
- You only want an inspirational keynote.
- You expect one workshop to transform the whole company.
Questions before the first sprint
Practical by design.
Start with one team and a small set of workflows. Expand only when the method works.
What is AI adoption consulting?+
It is the work between buying an AI tool and seeing it improve real work. Corso maps the use cases, builds repeatable workflows, trains the team and creates the resources needed to keep using them.
Which AI tools can you train our team on?+
We design around the environment your company has approved, including ChatGPT, Microsoft Copilot, Claude or Gemini. The exact assets depend on what the platform supports.
Do employees use real company work?+
Yes, but only within agreed privacy and data boundaries. We use approved, anonymized or synthetic examples where sensitive material should not enter the training environment.
How is this different from prompt training?+
A prompt is one component. A working workflow also needs approved sources, a repeatable input, a useful output format, ownership, human review and a place in the existing process.
What happens after the live training?+
Your team keeps the configured assets, online learning, demonstrations, playbooks and review standards. We also document what to improve or scale next.
Can the learning live in our LMS?+
Yes. We can deliver for your existing LMS or provide a dedicated learning environment through Ludus LMS.
Plan the first workflow
You already bought the AI. Let us make it useful at work.
Tell us which team has access, what tool is approved and where the work still feels manual. We will identify whether an AI Adoption Sprint is the right first step.