Two fictional technology advisers discuss work at a shared workstation

For managed service providers

Turn AI licenses
into work worth
renewing.

Some customers working with our MSP partners already require training before they issue an AI license. They want people ready to use what they are buying.

Help your customers frame better work, improve it with AI and judge the result. Give renewal and expansion conversations something more useful than a seat count.

Talk to usPlan a customer cohort

MSP partners in the US and Canada are running this model with their customers today.

The renewal conversation

Your customer bought access.
What changed in the work?

A rollout gives people a tool. It does not, by itself, give them a reason to return to it. When use stays thin, the renewal conversation can collapse into seat count and price.

A real customer response

Training first.
Then the license.

Some customers have already made readiness a condition of provisioning.

Those customers want people to know how to use the access they receive. That gives the MSP a concrete way to help before the next license is issued.

Agree on what completion and readiness mean. Keep approved practice tools available during learning. A training requirement should prepare people to work, not become a box to check.

Customer adoption and partner challenges

Where is AI getting stuck
in your customer conversations?

Choose the challenge closest to your experience. Explore the customer capability, the partner opportunity and a practical way to recognize progress.

Give the license a useful job to do.

Access has arrived, but a repeatable way to use it has not. Start with recurring work the customer actually needs to complete.

The partner opportunityA stronger basis for sustained use and right-sized license growth

What your customer develops Frame a worthwhile task, provide appropriate context, improve the result and check it against a clear standard.

Programs to exploreAI Agility

How to recognize progress Follow weekly learning activity. Separately review authorized software usage and a customer-owned measure such as checking time, rework or turnaround.

Which team has the clearest reason to use the seats it already has?

Talk about this priority

Bring the work into the renewal conversation.

A provisioned-user count says what was purchased, not what improved. Connect a recurring task, the people doing it and evidence the customer would trust.

The partner opportunityRenewal and expansion conversations grounded in customer value

What your customer develops Define the starting point, compare usable outputs and name who owns the outcome and its measurement.

Programs to exploreAI Agility

How to recognize progress Compare the customer-agreed baseline with current performance, including review effort and quality. Keep participation, usage and outcomes distinct.

What would your customer need to see to justify the next renewal?

Talk about this priority

Add capability building without becoming a training company.

Your account team identifies the opportunity. humanskills.ai supplies the curriculum, facilitation, delivery and learner support.

The partner opportunityA practical customer offer with limited training-delivery lift

What your customer develops Customers build transferable skills through guided practice. Your team stays focused on the relationship and the opportunity.

Programs to exploreAI Agility

How to recognize progress Review weekly learner activity with the customer stakeholder. Agree on the MSP’s follow-up role without creating a new training support desk.

Which customer is asking for help your team cannot deliver alone?

Talk about this priority

Make responsible use a skill, not just a policy.

An approved platform can still leave people unsure what information to use, which answers to trust or when to stop. Practice those decisions.

The partner opportunityMore consistent AI use within the customer’s approved boundaries

What your customer develops Recognize sensitive information, check evidence and bias, and accept, revise, reject or escalate within assigned authority.

Programs to exploreResponsible & Ethical AI

How to recognize progress Review decisions in realistic, nonsensitive scenarios and how questions reach the right owner. Training complements controls; it does not establish compliance.

What decision are your customer’s employees currently having to guess at?

Talk about this priority

Be the partner who helped the work improve.

Move beyond the price comparison. Help the customer develop useful work and a process they can demonstrate, not another promise of AI value.

The partner opportunityA stronger advisory relationship and a clearer expansion discussion

What your customer develops Build a repeatable workflow with clear context, human review, ownership and an exception route.

Programs to exploreAI Workflows + Agents

How to recognize progress Follow the whole workflow. The customer chooses quality, handoff and turnaround measures. Validate financial claims separately.

Where could you help the customer improve the work, not just buy the tool?

Talk about this priority

Make training-first a useful starting point.

Some customers working with our MSP partners require training before issuing a license. They want people prepared, rather than hoping access creates a habit.

The partner opportunityA deliberate path from learner readiness to purposeful access

What your customer develops Build AI Literacy and AI Fluency within AI Agility, then practice framing, iterating and judging work.

Programs to exploreAI Agility

How to recognize progress Agree on completion and readiness criteria. Keep approved practice tools available during learning. The customer decides when to provision access.

What does this customer expect someone to demonstrate before receiving a license?

Talk about this priority

Human skills, beyond the feature tour

The menus will change.
The work still needs judgment.

Feature training has a place. People still need to know the approved tool and its controls. But knowing where to click is different from knowing what work to hand to AI, how to improve the result and when the answer is not good enough.

humanskills.ai develops the human side of that collaboration. We call it Directing Intelligence: giving AI purpose and context, evaluating its work and remaining responsible for the result.

A fictional customer and adviser discuss a task around a laptop
Start with the work someone needs to do, not a tour of the tool.
01 / Frame the problem

Give AI a job
worth doing.

Choose a task where assistance could help. Define the audience, the decision, the relevant context and what a useful result must contain. Keep sensitive information within the customer’s approved boundaries.

Illustrative customer task

Instead of:
“Summarize this meeting.”

Frame the need:
“From these synthetic notes, prepare a handoff for the project owner. Separate decisions, open questions and next actions. Flag missing owners instead of inventing them.”

02 / Iterate toward usefulness

Work with the draft.
Do not just accept it.

Notice what is missing. Ask for a different structure, make assumptions explicit and add the context the model lacks. Test a revision against the task, rather than asking for something vaguely “better.”

Keep improving the same task

Inspect the first attempt:
The summary sounds finished, but mixes a suggestion with an agreed action.

Direct a revision:
Ask it to distinguish confirmed decisions from proposals, point to the supporting notes and mark uncertainty.

03 / Judge the result

Know what is
fit to use.

Check the work against its sources, purpose and constraints. Look for unsupported claims, omissions and consequences for the people affected. Decide what to accept, change, reject or escalate.

Keep the human decision

Check before sharing:
Are the decisions accurate? Are responsibilities supported? Is anything important missing?

Own the next step:
The person responsible corrects the handoff, confirms ownership and decides whether it is ready to use.

Practice, not a prompt library.

With Virgil, our Agentic Learning Guide, participants work through exercises connected to their work and goals. They create something they can inspect, explain their decisions and improve the result. Use synthetic or approved nonsensitive material.

Make the change visible

From an available seat
to a dependable way of working.

The aim is not more prompts. It is a customer team that can use AI for worthwhile work, explain its checks and improve a method others can use.

Three different kinds of evidence. Keep them separate.

Learning participation

Weekly activity reports from humanskills.ai go to the customer stakeholder and, where wanted, the MSP. Use them to follow participation and discuss support.

Software usage

The customer or an authorized administrator reviews the tool’s available usage reporting. A learning report is not a Copilot, ChatGPT or Claude usage report.

Business results

The customer owns the baseline and outcome measure. Review work quality, checking effort, rework or turnaround before attributing value to the change.

These diagrams describe an intended change in working practice. They are not a measured before-and-after result. Training participation does not establish license utilization, ROI or renewal impact.

Why this matters to your MSP

Grow the value around the license.
Not just the training revenue.

Help customers get value from the access they have and decide where to expand. Training revenue is an additional benefit.

The primary business opportunity

A better reason to renew.
A better case to expand.

Help the customer build working practices worth continuing. Use those practices, authorized usage evidence and customer-owned measures to discuss which seats are useful, where support is needed and which team should be next.

That is a more durable conversation than defending an invoice with another feature demo. Expansion follows a credible use case, not an assumption that everyone needs a paid seat.

An additional revenue line

Earn a share of training revenue.

Your MSP earns a percentage of training revenue, while humanskills.ai handles facilitation, delivery and learner support. We will discuss the partner arrangement together.

A stronger advisory role

Help the customer make AI useful.

Bring a capability-building path alongside the tools and services you already discuss. Stay in the conversation about work, decisions and what should improve next.

Useful adoption may mean expansion, a changed approach or right-sizing seats. Renewal, license-growth and financial outcomes are not guaranteed.

A straightforward partner model

You bring the opportunity.
We deliver the learning.

Start with the customer, the participants and the work. Your team does not become the training support desk.

  1. Identify the customer

    Your MSP identifies the customer and the opportunity.

  2. Share the enrollment list

    Provide the names and email addresses of the people enrolling through the agreed enrollment process.

  3. We deliver and support

    humanskills.ai handles all facilitation, delivery and learner support.

  4. Follow weekly activity

    Reports go to the customer stakeholder and, where wanted, your MSP.

  5. Earn revenue share

    Your MSP earns a percentage of training revenue. We discuss the arrangement together.

Start small. Keep room to grow.

From a handful of people
to thousands.

A cohort of two is deliverable when those people matter enough to the opportunity. Most MSPs find the economics work better with a group.

Delivery scales to thousands, using the same delivery model we use with our higher education partners. Programs are delivered through our higher education partner network, with humanskills.ai providing facilitation, delivery and learner support.

Programs available through this route

Match the learning
to the customer’s starting point.

Start with practical capability. Add responsible-use judgment or workflow design where the customer needs it.

AI Agility

Includes AI Literacy and AI Fluency

Frame useful tasks, provide context, iterate toward better results and evaluate the work. Build approaches that colleagues can repeat and improve.

Prepare and check a recurring project handoff, meeting brief or customer update using synthetic or approved nonsensitive material.

Explore AI Agility

Responsible & Ethical AI

Turn principles into everyday judgment

Practice decisions about sensitive information, evidence, fairness and accountability. Apply the customer’s policies and know when to stop or escalate.

Work through an unapproved-tool request or an unsupported answer. Explain what to protect, verify or take to the responsible owner.

responsible AI learning

AI Workflows + Agents

Design the work with human ownership

Map tasks, context, decisions and handoffs. Specify permissions, human review, ownership and exception routes before delegating work to AI.

Design a customer-intake or internal approval workflow with defined inputs, review points and a way to handle incomplete information.

Explore AI Workflows + Agents

External research, not program outcomes

The adoption gap
deserves a careful reading.

The useful lesson is not a dramatic failure rate. It is the distinction between providing technology, helping people use it and demonstrating a meaningful change in the work.

Deloitte / State of AI, fourth edition

37%

Significant investment
in helping people adapt

Deloitte reported that 37% of respondents significantly invested in change management, incentives or training to help people integrate new technology into their work.

Plan for the people as well as the platform.Read the evidence

Recon Analytics / February 2026

Access

Does not settle
which tool people choose

Recon’s workplace analysis distinguishes tool availability from preference, including differences between workplaces that provide one platform and those that provide several.

Investigate the customer’s actual usage.Read the evidence

RAND / August 2024

Purpose

Comes before
the latest technology

Interviews with experienced AI practitioners identified misunderstanding the problem and prioritizing technology over user needs among recurring causes of project failure.

Define the work before selecting the solution.Read the evidence

These external sources do not evaluate humanskills.ai or establish an effect on license utilization, renewals or revenue.

A partner conversation

Start with one customer.
Build a useful starting point.

Tell us where the customer is getting stuck. We can discuss the people, programs and partner arrangement.

A useful first conversation

  1. Your customer opportunity. A rollout, renewal, readiness requirement or team asking for help.
  2. Your priority. The work to improve and the evidence the customer would value.
  3. Your starting point. The participants, program, higher education delivery and partner arrangement.

No finished plan is needed. Please leave customer-confidential information and enrollment lists out of this form.

Let’s make it concrete

Plan a customer cohort

Start with one customer and a priority that matters to their people and your business.

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The evidence behind the page

Research you can inspect.

DeloittePeople, change management and technology adoptionRecon AnalyticsAvailability, preference and workplace adoptionRANDProblem definition and implementation pitfallsBCGBusiness outcomes beyond individual task speed
Explore all sources, findings and context

Reviewed September 23, 2026. Sources do not imply partnership or endorsement. Historical findings, surveys and practitioner interviews answer different questions. None is presented as a humanskills.ai program outcome.

  1. Deloitte: How to build an AI-ready culture

    Fourth-edition research, not the 2026 survey. Source of the 37% change-management, incentives or training finding.

  2. Recon Analytics: AI Choice 2026

    February 2026, drawing on more than 150,000 respondents. Availability, subscriptions and primary-tool preference are not interchangeable with paid-license utilization.

  3. RAND: The root causes of failure for AI projects

    August 13, 2024. Interviews with 65 experienced data scientists and engineers. Supports the importance of defining the right problem; it is not a measurement of training effectiveness or a 2,400-project failure-rate study.

  4. BCG: Look past productivity to get real value from AI

    August 31, 2026. Practitioner analysis of process redesign and business outcomes. Useful context for looking beyond task speed; not causal evidence that training increases license renewals.

Workplace scenes are AI-generated illustrations of fictional people, not photographs of customers or partners. Practice examples and diagrams are illustrative, not customer testimonials or measured results.

Please do not include Personally Identifiable Information or other sensitive data.

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