Build vs. buy: should we build our own AI coach?

Key takeaways

• A demo is the easy part. The real work is employee context, privacy, guardrails, testing, admin tools, adoption and upkeep as models change.
• Building can make sense when coaching is part of what you sell, or you have a long-term AI product team and a need no vendor covers.
• Buying usually makes sense when you want the whole workforce covered soon, with enterprise security and HR integrations from day one.
• Compare total cost of ownership, not the model bill.
• Hold both options to the same bar: the coach knows the employee, coaches rather than lectures, protects privacy and shows results.

Should we build our own AI coach? The short answer

For most organizations, buying is the faster and lower-risk path. Building makes sense mainly when an AI coach is a core product you’re prepared to staff and maintain for years. The model is the smallest part of the work: a coach employees trust also needs their context, a coaching method, privacy controls, guardrails, testing, admin tools and steady upkeep.

Outside research points the same way. A 2025 report from MIT’s NANDA initiative, reported by Fortune, found that buying AI tools from specialized vendors and building partnerships succeeded about 67% of the time, while internal builds succeeded only one-third as often. It covered generative AI broadly, not coaching, but the pattern is worth weighing.

What does building an AI coach really involve?

Building an AI coach means building and running nine things, not one. A prototype that answers coaching questions covers only the first.

1. Model access

Choose models, decide whether to use a provider’s service or host your own, and manage cost per conversation, response time and outages.

2. Prompts and a coaching method

Decide how the coach coaches: when it asks, when it advises, how it sets goals and follows up. Making that reliable takes many rounds of prompt design and testing. Skip it and you have a chatbot with a new name; see how an AI coach differs from a chatbot.

3. Employee context and HR integration

A coach is only as good as what it knows. Connect your HR system, learning records, goals and calendar, choose which fields to sync and keep them current as those systems change.

4. Privacy and security reviews

Coaching conversations are some of the most sensitive data you’ll hold. Expect security, privacy and legal reviews covering retention, access, data residency and whether a model provider may train on the data.

The EU AI Act (2024) lists among high-risk uses AI systems intended to make decisions on promotion or termination, to allocate tasks, or to monitor and evaluate workers’ performance and behavior. Whether a coach is in scope depends on how its outputs are used, so involve legal counsel early.

5. Guardrails and escalation

The coach must recognize topics it shouldn’t handle alone, such as health, harassment or a crisis, respond safely and point people to HR, a manager or your employee assistance program. Test this hardest; it is where mistakes hurt most.

6. Evaluation and quality

You need realistic test conversations, rubrics for coaching quality, review by experienced coaches, bias checks and re-testing after every change. The NIST AI Risk Management Framework (2023) and its Generative AI Profile (2024) are useful public references.

7. Admin tools and reporting

HR needs to set up programs, choose data, assign practice and report participation and progress to leadership. Without these tools, every change becomes an engineering ticket.

8. Adoption

Employees have to choose to come back. That depends on where the coach lives, onboarding, timely nudges and clear communication about privacy.

9. Upkeep as models change

Providers release new models and retire old ones, so prompts, guardrails and tests need regular rework, along with security patches, integration fixes and support.

What are the hidden costs of building an AI coach?

The hidden costs are mostly people and time, not model fees, and most arrive after launch.

In 2024, Gartner predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs or unclear business value. Its analyst added that generative AI costs “aren’t as predictable as other technologies.”

• A standing team across product, engineering, data, coaching expertise and support, for as long as the coach runs.
• Reviews that repeat whenever the design, data or model provider changes.
• Evaluation: test sets, expert review and re-testing.
• Integration upkeep as your HR, learning and identity systems change.
• Usage that grows with success, especially for voice practice.
• The cost of a mistake: a privacy incident or a bad answer on a sensitive topic can set back trust in the whole program.

How to estimate the real cost

1. List the roles you’d need and each person’s share of time, for the build and each year of upkeep.
2. Add model and hosting costs at full-rollout usage, not pilot volume, plus review, evaluation and support time.
3. Total it over three years and compare it with vendor quotes for the same scope.

When does building your own AI coach make sense?

Building can make sense when the coach is strategic to your business and you’re ready to run it as a product, with a team and budget that outlast the launch. Consider it if:

• Coaching is part of what you sell, so the coach is your product.
• You have an AI product team with capacity and a multi-year budget.
• Your need is narrow and unusual, such as one specialized skill no vendor covers well.
• You have data or policy constraints no vendor can meet after a real search.
• You want to learn first: a small, time-boxed pilot can sharpen your requirements before you buy.

When does buying an AI coach make sense?

Buying makes sense when you want coaching for many employees soon, with enterprise controls in place, and you’d rather your team spend its time on people than on software. It fits when several of these are true:

• You want to reach the whole workforce, not just a pilot group.
• You need security certifications, single sign-on and data controls from day one.
• You need connections to the HR, learning and collaboration systems you already run.
• HR and L&D want to run programs and build practice scenarios without engineering help.
• You want AI coaching linked to mentoring and other development programs.
• You’d rather the vendor carry the upkeep as models change.

Buying has trade-offs: less control over the roadmap, a vendor to manage and subscription costs. A pilot with clear success measures, and contract terms on data use and deletion, reduce the risk.

Build vs. buy scorecard

Mark which column fits your organization on each row. If most rows point to buy, buy. If several point strongly to build, write a full business case and test it against vendor quotes.

Questions to ask any AI coaching vendor

Use these questions to compare vendors, and hold an internal build to the same bar. Ask to see each answer in a live demo.

1. What does the coach know about an employee before the first conversation, and from which systems?
2. Which coaching method does it follow, and how do you test that it sticks to it?
3. How does it handle sensitive topics, and when does it hand off to a person?
4. What can HR, managers and admins see? Is our data used to train models?
5. Which security certifications do you hold, and where is data hosted?
6. Who builds and maintains the connections to our HR and learning systems?
7. Can our L&D team build practice scenarios and score them on our own rubrics?
8. How do you protect coaching quality when you change models?
9. What does implementation ask of our IT team, and what happens to our data if we leave?

The ICF’s AI Coaching Framework and Standards (2024) is a neutral reference for these conversations.

An example of buying: Rumi by Chronus

If you decide to buy, Rumi by Chronus is one option to evaluate. It brings AI coaching, mentoring and communities into one platform and builds on 20 years of talent development expertise.

• Context: Rumi Coach knows each employee’s role, skills and goals, and your structure, teams and programs, drawn from the systems you already run.
• Practice: employees rehearse high-stakes conversations in Roleplays that L&D and enablement teams build in minutes and assess on their own training frameworks.
• Privacy: conversations are private by default, administrators choose which organizational data Rumi Coach can access, and your data is never used to train AI models.
• Fit: Rumi connects to your HR systems, with single sign-on and the access controls your IT team expects. See integrations and security.

Frequently asked questions