From Assessment to Action: How AI Coaching Closes the Development Gap

Author

Anjana Unni

Created on

May 20, 2026

The development gap is the well-documented distance between leadership assessments and lasting behavior change.

The data on this is concerning. In Gartner's survey, 75% of HR leaders said their managers are overwhelmed by expanding responsibilities, and 69% said managers are not equipped to lead change. Three-quarters of organizations have updated their leadership development programs, more than half have increased spend, and most are not seeing results. Gartner's own conclusion is blunt: traditional seminars and lectures have a negative effect on leader development.

Deloitte's 2025 Global Human Capital Trends report, based on a survey of nearly 10,000 leaders across 93 countries, sharpens the same point: managers spend roughly 40% of their time on administrative work and just 13% developing their people. Over a third (36%) of managers say they are insufficiently prepared for the role. AI, the report argues, can help free managers to do what makes them managers in the first place: coach, develop, and motivate.

McKinsey has been making the same case for over a decade. McKinsey’s analysis of why leadership-development programs fail notes that adults retain about 10% of what they hear in classroom lectures versus roughly two-thirds when they learn by doing, and that behavior change is unlikely without sustained discomfort and reflection. A follow-up McKinsey survey found that organizations whose leaders are self-aware enough to adapt their behavior are four times more prepared to lead change, and successful leadership programs were three times more likely to provide coaching that encourages introspection and self-discovery.

So the gap has three layers:

People do not retain classroom-style content.
Managers do not have time to translate insights into coaching conversations.
Most development happens off the job and is never reinforced in the flow of work.

This is the gap AI coaching is designed to close.

Why most assessment programs do not change behavior

Most assessments still end where development should begin.

An employee completes a personality questionnaire. They receive a PDF feedback report. A facilitator may run a debrief. Then the report goes into a drawer. Six months later, no one can name a behavior the employee has changed.

This pattern is not the assessment's fault. It is the workflow's fault. As Dr. Luke Treglownave Winsborough writes, the legacy industry of 360s, personality reports, and executive coaching was deliberately built to be complex, jargon-heavy, and expensive, accessible mostly to senior executives who could afford a human coach to interpret the results. Everyone else gets the report and nothing else.

That model has two structural failures:

No timing. Coaching happens monthly at best. The moment a behavior actually showed up (the missed feedback, the dropped delegation, the meeting where someone shut down) has already passed.
No translation. Assessment results talk about traits. Day-to-day work demands behavior. Without something to bridge the two, even good data stays inert.

When Gartner predicted the rise of "nudgetech" in its 2025 CHRO predictions, they framed it precisely this way: hyper-personalized nudges with clear explanations for why a change is recommended create a double benefit of improved communication and increased behavior change.

A nudge is only powerful if it is grounded in something the person trusts. That something is the assessment.

What is AI coaching?

AI coaching is the use of an artificial intelligence system, typically a large language model wrapped around a structured psychological or behavioral framework, to deliver personalized development guidance in real time.

It is different from three adjacent things people often confuse it with:

AI coaching is not a chatbot. A chatbot answers questions. An AI coach is goal-directed and grounded in a person's actual assessment data, role, and development objectives.
AI coaching is not an LMS. A learning management system pushes pre-built courses to a calendar. An AI coach surfaces the right content at the right moment for the right person.
AI coaching is not a replacement for human coaches. It is an extension of them. Human coaches handle depth, relationship, and the conversations that require a person on the other end. AI coaches handle reach, repetition, and the in-between moments human coaches will never see.

A randomized controlled trial followed 169 participants over six months and found that the experimental group using an AI chatbot coach (Vici) showed statistically significant improvements in goal attainment relative to the control group, and was comparable to human coaches on that specific dimension. Another study published in Harvard Business Review found that AI-coached learners outperformed those in traditional classroom workshops on applied soft-skills tasks.

The headline from both: AI coaching does not replicate the full depth of human coaching. It does something different and complementary, and on the specific job of helping people set goals and act on them, it works.

How AI coaching closes the development gap

AI coaching works on three mechanisms that traditional development programs do not have.

1. Timing. AI coaches operate continuously. A monthly coaching session is retrospective by design. AI coaching can intervene before the meeting, not after the regret. This matches what McKinsey identifies as the key adult-learning principle: feedback and reflection loops are what move classroom insight into behavior, and the loops have to be close to the actual work.

2. Context. A good AI coach knows your assessment results, your role, your goals, and the situation you are walking into. Sola, for example, will give different advice for "How do I delegate this project?" depending on whether the person asking is Candid or Diplomatic on Core Drivers, and depending on whether their team is mostly Adaptable or mostly Disciplined. Generic AI assistants cannot do this; they have nothing to ground the advice in.

3. Scale. Human coaching costs roughly $500 to $1500 per hour and is reserved for a sliver of the workforce. AI coaching can extend a structured, evidence-based development experience to every individual contributor, not just the future C-suite. Deloitte's 2025 trends report makes a similar point: AI can take administrative load off managers so they spend more of their 87% of "non-developing" time actually developing people.

Meet Sola: Deeper Signals' AI assessment coach

Sola is Deeper Signals' AI assessment assistant. It is the layer that turns a Core Drivers or Core Values result into action.

What that looks like in practice:

For the employee: Sola reads your assessment results and answers questions like "How should I approach my new role given my Driven and Considerate Core Drivers?" or "What should I prepare for a difficult feedback conversation with Mia and Thompson?" The advice is grounded in your data, not in generic management writing.
For the manager: Sola generates 1:1 talking points, feedback prompts, delegation guidance, and onboarding plans calibrated to the direct report's personality and values. The manager does not need to interpret a psychological report. They get specific things to say and try.
For the team leader: Sola pulls together team-level dynamics from individual assessments and generates playbooks for the actual problems leaders face: conflict between two team members with clashing Core Drivers, low engagement among Independent-leaning team members, hiring for a missing strength.
For the recruiter and hiring manager: Sola turns candidate assessment data into structured, role-specific interview guides and side-by-side candidate comparisons. (Detailed in our hiring guide.)

Sola is also constrained in ways that general AI assistants are not. Assessment data stays in the Deeper Signals environment and is not used to train external models. Sola is designed for handling assessment data securely, not repurposing it. That is a deliberate design choice, and an increasingly important one as AI ethics in talent decisions becomes a board-level question (see our write-up on how ethics shapes AI assessment).

The technical name for what Sola does is assessment-aware AI coaching: an AI system whose every recommendation is conditioned on a validated psychometric profile.

From assessment to action: The Sola workflow

Here is how a single assessment becomes ongoing behavior change. We will use a composite, anonymized example: a newly promoted product manager (we will call her Lara) at a mid-sized SaaS company.

Step 1. Assessment. Lara completes the Core Drivers Diagnostic in seven minutes. The diagnostic is built on the Five Factor Model of personality and has been validated on over 300,000 working professionals. Her results show a strong Driven and Disciplined profile with a lower Diplomatic high Candid score. She also completes the Core Values Diagnostic, which surfaces Achievement and Independence as her top motivators.

Step 2. Personalized self-coaching. Within minutes, Lara is in conversation with Sola. She asks: "I just got promoted. What should I focus on in my first 60 days based on my results?" Sola responds with a development plan that is specific to her profile: leverage her natural goal orientation by setting clear quarterly objectives, but invest in stakeholder relationships early because lower Diplomatichigh Candid scores can come across as overly blunt under pressure. The plan links out to short learning content on giving developmental feedback.

Step 3. Manager prompts. Lara's manager, who has access to Lara's permissioned summary, gets a different view. Sola generates 1:1 questions calibrated to Lara's profile: "Ask Lara what stakeholder relationships she is most likely to neglect when under deadline pressure. Discuss specific examples." The manager does not need to be a psychologist. They get a script, and a reason behind it.

Step 4. Continuous nudges. Over the next eight weeks, Sola surfaces small reminders that reflect Lara's actual development priorities: a prompt before a planned cross-functional review to slow down, ask for input, and not steer the meeting too early. A prompt to recognize a peer publicly after a project ships. Each nudge is short, contextual, and tied back to a behavior Lara herself agreed to work on.

Step 5. Team coaching. Lara's manager runs a team workshop using the AI Team Coach Sola, which generates a custom playbook based on the makeup of the team: where the team is likely to lock up under pressure, which combinations of personalities will naturally clash, where to invest in connective tissue. This is not pulled from a generic team-effectiveness book. It is a playbook generated from the actual people in the room.

Five steps. One assessment. Three audiences (individual, manager, team). Continuous reinforcement across weeks, not a one-off debrief.

This is the workflow that closes the development gap.

Want to see what Sola produces for your team? Book a demo. Bring an assessment result and a real development question.

Frequently asked questions

1. How is AI coaching different from a chatbot?

A chatbot answers free-form questions with general advice. An AI coach is goal-directed, conditioned on a person's assessment data and role, and structured around recognized coaching frameworks like goal-attainment theory.

2. Does AI coaching replace human coaches?

No. The current evidence is that AI coaching matches human coaches on goal attainment but underperforms them on deeper outcomes like psychological wellbeing and stress reduction. The most effective programs combine both, using AI for scale and continuity and human coaches for depth.

3. What is Sola?

Sola is the AI assessment assistant inside the Deeper Signals platform. It turns Core Drivers and Core Values results into personalized guidance for employees, managers, team leaders, recruiters, and coaches, in real time.

4. Is my assessment data safe with Sola?

Yes. Deeper Signals is SOC 2 and GDPR compliant. Assessment data stays inside the Deeper Signals ecosystem and is not used to train external AI models.

5. Can AI coaching work for individual contributors, not just leaders?

Yes, and this is where its leverage is highest. Traditional coaching has historically been reserved for senior leaders. AI coaching makes the same kind of structured, personalized development available to anyone with an assessment.

From Assessment to Action: How AI Coaching Closes the Development Gap

Author

Anjana Unni

Created on

May 20, 2026

The development gap is the well-documented distance between leadership assessments and lasting behavior change.

The data on this is concerning. In Gartner's survey, 75% of HR leaders said their managers are overwhelmed by expanding responsibilities, and 69% said managers are not equipped to lead change. Three-quarters of organizations have updated their leadership development programs, more than half have increased spend, and most are not seeing results. Gartner's own conclusion is blunt: traditional seminars and lectures have a negative effect on leader development.

Deloitte's 2025 Global Human Capital Trends report, based on a survey of nearly 10,000 leaders across 93 countries, sharpens the same point: managers spend roughly 40% of their time on administrative work and just 13% developing their people. Over a third (36%) of managers say they are insufficiently prepared for the role. AI, the report argues, can help free managers to do what makes them managers in the first place: coach, develop, and motivate.

McKinsey has been making the same case for over a decade. McKinsey’s analysis of why leadership-development programs fail notes that adults retain about 10% of what they hear in classroom lectures versus roughly two-thirds when they learn by doing, and that behavior change is unlikely without sustained discomfort and reflection. A follow-up McKinsey survey found that organizations whose leaders are self-aware enough to adapt their behavior are four times more prepared to lead change, and successful leadership programs were three times more likely to provide coaching that encourages introspection and self-discovery.

So the gap has three layers:

People do not retain classroom-style content.
Managers do not have time to translate insights into coaching conversations.
Most development happens off the job and is never reinforced in the flow of work.

This is the gap AI coaching is designed to close.

Why most assessment programs do not change behavior

Most assessments still end where development should begin.

An employee completes a personality questionnaire. They receive a PDF feedback report. A facilitator may run a debrief. Then the report goes into a drawer. Six months later, no one can name a behavior the employee has changed.

This pattern is not the assessment's fault. It is the workflow's fault. As Dr. Luke Treglownave Winsborough writes, the legacy industry of 360s, personality reports, and executive coaching was deliberately built to be complex, jargon-heavy, and expensive, accessible mostly to senior executives who could afford a human coach to interpret the results. Everyone else gets the report and nothing else.

That model has two structural failures:

No timing. Coaching happens monthly at best. The moment a behavior actually showed up (the missed feedback, the dropped delegation, the meeting where someone shut down) has already passed.
No translation. Assessment results talk about traits. Day-to-day work demands behavior. Without something to bridge the two, even good data stays inert.

When Gartner predicted the rise of "nudgetech" in its 2025 CHRO predictions, they framed it precisely this way: hyper-personalized nudges with clear explanations for why a change is recommended create a double benefit of improved communication and increased behavior change.

A nudge is only powerful if it is grounded in something the person trusts. That something is the assessment.

What is AI coaching?

AI coaching is the use of an artificial intelligence system, typically a large language model wrapped around a structured psychological or behavioral framework, to deliver personalized development guidance in real time.

It is different from three adjacent things people often confuse it with:

AI coaching is not a chatbot. A chatbot answers questions. An AI coach is goal-directed and grounded in a person's actual assessment data, role, and development objectives.
AI coaching is not an LMS. A learning management system pushes pre-built courses to a calendar. An AI coach surfaces the right content at the right moment for the right person.
AI coaching is not a replacement for human coaches. It is an extension of them. Human coaches handle depth, relationship, and the conversations that require a person on the other end. AI coaches handle reach, repetition, and the in-between moments human coaches will never see.

A randomized controlled trial followed 169 participants over six months and found that the experimental group using an AI chatbot coach (Vici) showed statistically significant improvements in goal attainment relative to the control group, and was comparable to human coaches on that specific dimension. Another study published in Harvard Business Review found that AI-coached learners outperformed those in traditional classroom workshops on applied soft-skills tasks.

The headline from both: AI coaching does not replicate the full depth of human coaching. It does something different and complementary, and on the specific job of helping people set goals and act on them, it works.

How AI coaching closes the development gap

AI coaching works on three mechanisms that traditional development programs do not have.

1. Timing. AI coaches operate continuously. A monthly coaching session is retrospective by design. AI coaching can intervene before the meeting, not after the regret. This matches what McKinsey identifies as the key adult-learning principle: feedback and reflection loops are what move classroom insight into behavior, and the loops have to be close to the actual work.

2. Context. A good AI coach knows your assessment results, your role, your goals, and the situation you are walking into. Sola, for example, will give different advice for "How do I delegate this project?" depending on whether the person asking is Candid or Diplomatic on Core Drivers, and depending on whether their team is mostly Adaptable or mostly Disciplined. Generic AI assistants cannot do this; they have nothing to ground the advice in.

3. Scale. Human coaching costs roughly $500 to $1500 per hour and is reserved for a sliver of the workforce. AI coaching can extend a structured, evidence-based development experience to every individual contributor, not just the future C-suite. Deloitte's 2025 trends report makes a similar point: AI can take administrative load off managers so they spend more of their 87% of "non-developing" time actually developing people.

Meet Sola: Deeper Signals' AI assessment coach

Sola is Deeper Signals' AI assessment assistant. It is the layer that turns a Core Drivers or Core Values result into action.

What that looks like in practice:

For the employee: Sola reads your assessment results and answers questions like "How should I approach my new role given my Driven and Considerate Core Drivers?" or "What should I prepare for a difficult feedback conversation with Mia and Thompson?" The advice is grounded in your data, not in generic management writing.
For the manager: Sola generates 1:1 talking points, feedback prompts, delegation guidance, and onboarding plans calibrated to the direct report's personality and values. The manager does not need to interpret a psychological report. They get specific things to say and try.
For the team leader: Sola pulls together team-level dynamics from individual assessments and generates playbooks for the actual problems leaders face: conflict between two team members with clashing Core Drivers, low engagement among Independent-leaning team members, hiring for a missing strength.
For the recruiter and hiring manager: Sola turns candidate assessment data into structured, role-specific interview guides and side-by-side candidate comparisons. (Detailed in our hiring guide.)

Sola is also constrained in ways that general AI assistants are not. Assessment data stays in the Deeper Signals environment and is not used to train external models. Sola is designed for handling assessment data securely, not repurposing it. That is a deliberate design choice, and an increasingly important one as AI ethics in talent decisions becomes a board-level question (see our write-up on how ethics shapes AI assessment).

The technical name for what Sola does is assessment-aware AI coaching: an AI system whose every recommendation is conditioned on a validated psychometric profile.

From assessment to action: The Sola workflow

Here is how a single assessment becomes ongoing behavior change. We will use a composite, anonymized example: a newly promoted product manager (we will call her Lara) at a mid-sized SaaS company.

Step 1. Assessment. Lara completes the Core Drivers Diagnostic in seven minutes. The diagnostic is built on the Five Factor Model of personality and has been validated on over 300,000 working professionals. Her results show a strong Driven and Disciplined profile with a lower Diplomatic high Candid score. She also completes the Core Values Diagnostic, which surfaces Achievement and Independence as her top motivators.

Step 2. Personalized self-coaching. Within minutes, Lara is in conversation with Sola. She asks: "I just got promoted. What should I focus on in my first 60 days based on my results?" Sola responds with a development plan that is specific to her profile: leverage her natural goal orientation by setting clear quarterly objectives, but invest in stakeholder relationships early because lower Diplomatichigh Candid scores can come across as overly blunt under pressure. The plan links out to short learning content on giving developmental feedback.

Step 3. Manager prompts. Lara's manager, who has access to Lara's permissioned summary, gets a different view. Sola generates 1:1 questions calibrated to Lara's profile: "Ask Lara what stakeholder relationships she is most likely to neglect when under deadline pressure. Discuss specific examples." The manager does not need to be a psychologist. They get a script, and a reason behind it.

Step 4. Continuous nudges. Over the next eight weeks, Sola surfaces small reminders that reflect Lara's actual development priorities: a prompt before a planned cross-functional review to slow down, ask for input, and not steer the meeting too early. A prompt to recognize a peer publicly after a project ships. Each nudge is short, contextual, and tied back to a behavior Lara herself agreed to work on.

Step 5. Team coaching. Lara's manager runs a team workshop using the AI Team Coach Sola, which generates a custom playbook based on the makeup of the team: where the team is likely to lock up under pressure, which combinations of personalities will naturally clash, where to invest in connective tissue. This is not pulled from a generic team-effectiveness book. It is a playbook generated from the actual people in the room.

Five steps. One assessment. Three audiences (individual, manager, team). Continuous reinforcement across weeks, not a one-off debrief.

This is the workflow that closes the development gap.

Want to see what Sola produces for your team? Book a demo. Bring an assessment result and a real development question.

Frequently asked questions

1. How is AI coaching different from a chatbot?

A chatbot answers free-form questions with general advice. An AI coach is goal-directed, conditioned on a person's assessment data and role, and structured around recognized coaching frameworks like goal-attainment theory.

2. Does AI coaching replace human coaches?

No. The current evidence is that AI coaching matches human coaches on goal attainment but underperforms them on deeper outcomes like psychological wellbeing and stress reduction. The most effective programs combine both, using AI for scale and continuity and human coaches for depth.

3. What is Sola?

Sola is the AI assessment assistant inside the Deeper Signals platform. It turns Core Drivers and Core Values results into personalized guidance for employees, managers, team leaders, recruiters, and coaches, in real time.

4. Is my assessment data safe with Sola?

Yes. Deeper Signals is SOC 2 and GDPR compliant. Assessment data stays inside the Deeper Signals ecosystem and is not used to train external AI models.

5. Can AI coaching work for individual contributors, not just leaders?

Yes, and this is where its leverage is highest. Traditional coaching has historically been reserved for senior leaders. AI coaching makes the same kind of structured, personalized development available to anyone with an assessment.

From Assessment to Action: How AI Coaching Closes the Development Gap

Author

Anjana Unni

Created on

May 20, 2026

The development gap is the well-documented distance between leadership assessments and lasting behavior change.

The data on this is concerning. In Gartner's survey, 75% of HR leaders said their managers are overwhelmed by expanding responsibilities, and 69% said managers are not equipped to lead change. Three-quarters of organizations have updated their leadership development programs, more than half have increased spend, and most are not seeing results. Gartner's own conclusion is blunt: traditional seminars and lectures have a negative effect on leader development.

Deloitte's 2025 Global Human Capital Trends report, based on a survey of nearly 10,000 leaders across 93 countries, sharpens the same point: managers spend roughly 40% of their time on administrative work and just 13% developing their people. Over a third (36%) of managers say they are insufficiently prepared for the role. AI, the report argues, can help free managers to do what makes them managers in the first place: coach, develop, and motivate.

McKinsey has been making the same case for over a decade. McKinsey’s analysis of why leadership-development programs fail notes that adults retain about 10% of what they hear in classroom lectures versus roughly two-thirds when they learn by doing, and that behavior change is unlikely without sustained discomfort and reflection. A follow-up McKinsey survey found that organizations whose leaders are self-aware enough to adapt their behavior are four times more prepared to lead change, and successful leadership programs were three times more likely to provide coaching that encourages introspection and self-discovery.

So the gap has three layers:

People do not retain classroom-style content.
Managers do not have time to translate insights into coaching conversations.
Most development happens off the job and is never reinforced in the flow of work.

This is the gap AI coaching is designed to close.

Why most assessment programs do not change behavior

Most assessments still end where development should begin.

An employee completes a personality questionnaire. They receive a PDF feedback report. A facilitator may run a debrief. Then the report goes into a drawer. Six months later, no one can name a behavior the employee has changed.

This pattern is not the assessment's fault. It is the workflow's fault. As Dr. Luke Treglownave Winsborough writes, the legacy industry of 360s, personality reports, and executive coaching was deliberately built to be complex, jargon-heavy, and expensive, accessible mostly to senior executives who could afford a human coach to interpret the results. Everyone else gets the report and nothing else.

That model has two structural failures:

No timing. Coaching happens monthly at best. The moment a behavior actually showed up (the missed feedback, the dropped delegation, the meeting where someone shut down) has already passed.
No translation. Assessment results talk about traits. Day-to-day work demands behavior. Without something to bridge the two, even good data stays inert.

When Gartner predicted the rise of "nudgetech" in its 2025 CHRO predictions, they framed it precisely this way: hyper-personalized nudges with clear explanations for why a change is recommended create a double benefit of improved communication and increased behavior change.

A nudge is only powerful if it is grounded in something the person trusts. That something is the assessment.

What is AI coaching?

AI coaching is the use of an artificial intelligence system, typically a large language model wrapped around a structured psychological or behavioral framework, to deliver personalized development guidance in real time.

It is different from three adjacent things people often confuse it with:

AI coaching is not a chatbot. A chatbot answers questions. An AI coach is goal-directed and grounded in a person's actual assessment data, role, and development objectives.
AI coaching is not an LMS. A learning management system pushes pre-built courses to a calendar. An AI coach surfaces the right content at the right moment for the right person.
AI coaching is not a replacement for human coaches. It is an extension of them. Human coaches handle depth, relationship, and the conversations that require a person on the other end. AI coaches handle reach, repetition, and the in-between moments human coaches will never see.

A randomized controlled trial followed 169 participants over six months and found that the experimental group using an AI chatbot coach (Vici) showed statistically significant improvements in goal attainment relative to the control group, and was comparable to human coaches on that specific dimension. Another study published in Harvard Business Review found that AI-coached learners outperformed those in traditional classroom workshops on applied soft-skills tasks.

The headline from both: AI coaching does not replicate the full depth of human coaching. It does something different and complementary, and on the specific job of helping people set goals and act on them, it works.

How AI coaching closes the development gap

AI coaching works on three mechanisms that traditional development programs do not have.

1. Timing. AI coaches operate continuously. A monthly coaching session is retrospective by design. AI coaching can intervene before the meeting, not after the regret. This matches what McKinsey identifies as the key adult-learning principle: feedback and reflection loops are what move classroom insight into behavior, and the loops have to be close to the actual work.

2. Context. A good AI coach knows your assessment results, your role, your goals, and the situation you are walking into. Sola, for example, will give different advice for "How do I delegate this project?" depending on whether the person asking is Candid or Diplomatic on Core Drivers, and depending on whether their team is mostly Adaptable or mostly Disciplined. Generic AI assistants cannot do this; they have nothing to ground the advice in.

3. Scale. Human coaching costs roughly $500 to $1500 per hour and is reserved for a sliver of the workforce. AI coaching can extend a structured, evidence-based development experience to every individual contributor, not just the future C-suite. Deloitte's 2025 trends report makes a similar point: AI can take administrative load off managers so they spend more of their 87% of "non-developing" time actually developing people.

Meet Sola: Deeper Signals' AI assessment coach

Sola is Deeper Signals' AI assessment assistant. It is the layer that turns a Core Drivers or Core Values result into action.

What that looks like in practice:

For the employee: Sola reads your assessment results and answers questions like "How should I approach my new role given my Driven and Considerate Core Drivers?" or "What should I prepare for a difficult feedback conversation with Mia and Thompson?" The advice is grounded in your data, not in generic management writing.
For the manager: Sola generates 1:1 talking points, feedback prompts, delegation guidance, and onboarding plans calibrated to the direct report's personality and values. The manager does not need to interpret a psychological report. They get specific things to say and try.
For the team leader: Sola pulls together team-level dynamics from individual assessments and generates playbooks for the actual problems leaders face: conflict between two team members with clashing Core Drivers, low engagement among Independent-leaning team members, hiring for a missing strength.
For the recruiter and hiring manager: Sola turns candidate assessment data into structured, role-specific interview guides and side-by-side candidate comparisons. (Detailed in our hiring guide.)

Sola is also constrained in ways that general AI assistants are not. Assessment data stays in the Deeper Signals environment and is not used to train external models. Sola is designed for handling assessment data securely, not repurposing it. That is a deliberate design choice, and an increasingly important one as AI ethics in talent decisions becomes a board-level question (see our write-up on how ethics shapes AI assessment).

The technical name for what Sola does is assessment-aware AI coaching: an AI system whose every recommendation is conditioned on a validated psychometric profile.

From assessment to action: The Sola workflow

Here is how a single assessment becomes ongoing behavior change. We will use a composite, anonymized example: a newly promoted product manager (we will call her Lara) at a mid-sized SaaS company.

Step 1. Assessment. Lara completes the Core Drivers Diagnostic in seven minutes. The diagnostic is built on the Five Factor Model of personality and has been validated on over 300,000 working professionals. Her results show a strong Driven and Disciplined profile with a lower Diplomatic high Candid score. She also completes the Core Values Diagnostic, which surfaces Achievement and Independence as her top motivators.

Step 2. Personalized self-coaching. Within minutes, Lara is in conversation with Sola. She asks: "I just got promoted. What should I focus on in my first 60 days based on my results?" Sola responds with a development plan that is specific to her profile: leverage her natural goal orientation by setting clear quarterly objectives, but invest in stakeholder relationships early because lower Diplomatichigh Candid scores can come across as overly blunt under pressure. The plan links out to short learning content on giving developmental feedback.

Step 3. Manager prompts. Lara's manager, who has access to Lara's permissioned summary, gets a different view. Sola generates 1:1 questions calibrated to Lara's profile: "Ask Lara what stakeholder relationships she is most likely to neglect when under deadline pressure. Discuss specific examples." The manager does not need to be a psychologist. They get a script, and a reason behind it.

Step 4. Continuous nudges. Over the next eight weeks, Sola surfaces small reminders that reflect Lara's actual development priorities: a prompt before a planned cross-functional review to slow down, ask for input, and not steer the meeting too early. A prompt to recognize a peer publicly after a project ships. Each nudge is short, contextual, and tied back to a behavior Lara herself agreed to work on.

Step 5. Team coaching. Lara's manager runs a team workshop using the AI Team Coach Sola, which generates a custom playbook based on the makeup of the team: where the team is likely to lock up under pressure, which combinations of personalities will naturally clash, where to invest in connective tissue. This is not pulled from a generic team-effectiveness book. It is a playbook generated from the actual people in the room.

Five steps. One assessment. Three audiences (individual, manager, team). Continuous reinforcement across weeks, not a one-off debrief.

This is the workflow that closes the development gap.

Want to see what Sola produces for your team? Book a demo. Bring an assessment result and a real development question.

Frequently asked questions

1. How is AI coaching different from a chatbot?

A chatbot answers free-form questions with general advice. An AI coach is goal-directed, conditioned on a person's assessment data and role, and structured around recognized coaching frameworks like goal-attainment theory.

2. Does AI coaching replace human coaches?

No. The current evidence is that AI coaching matches human coaches on goal attainment but underperforms them on deeper outcomes like psychological wellbeing and stress reduction. The most effective programs combine both, using AI for scale and continuity and human coaches for depth.

3. What is Sola?

Sola is the AI assessment assistant inside the Deeper Signals platform. It turns Core Drivers and Core Values results into personalized guidance for employees, managers, team leaders, recruiters, and coaches, in real time.

4. Is my assessment data safe with Sola?

Yes. Deeper Signals is SOC 2 and GDPR compliant. Assessment data stays inside the Deeper Signals ecosystem and is not used to train external AI models.

5. Can AI coaching work for individual contributors, not just leaders?

Yes, and this is where its leverage is highest. Traditional coaching has historically been reserved for senior leaders. AI coaching makes the same kind of structured, personalized development available to anyone with an assessment.

From Assessment to Action: How AI Coaching Closes the Development Gap

Author

Anjana Unni

Created on

May 20, 2026

The development gap is the well-documented distance between leadership assessments and lasting behavior change.

The data on this is concerning. In Gartner's survey, 75% of HR leaders said their managers are overwhelmed by expanding responsibilities, and 69% said managers are not equipped to lead change. Three-quarters of organizations have updated their leadership development programs, more than half have increased spend, and most are not seeing results. Gartner's own conclusion is blunt: traditional seminars and lectures have a negative effect on leader development.

Deloitte's 2025 Global Human Capital Trends report, based on a survey of nearly 10,000 leaders across 93 countries, sharpens the same point: managers spend roughly 40% of their time on administrative work and just 13% developing their people. Over a third (36%) of managers say they are insufficiently prepared for the role. AI, the report argues, can help free managers to do what makes them managers in the first place: coach, develop, and motivate.

McKinsey has been making the same case for over a decade. McKinsey’s analysis of why leadership-development programs fail notes that adults retain about 10% of what they hear in classroom lectures versus roughly two-thirds when they learn by doing, and that behavior change is unlikely without sustained discomfort and reflection. A follow-up McKinsey survey found that organizations whose leaders are self-aware enough to adapt their behavior are four times more prepared to lead change, and successful leadership programs were three times more likely to provide coaching that encourages introspection and self-discovery.

So the gap has three layers:

People do not retain classroom-style content.
Managers do not have time to translate insights into coaching conversations.
Most development happens off the job and is never reinforced in the flow of work.

This is the gap AI coaching is designed to close.

Why most assessment programs do not change behavior

Most assessments still end where development should begin.

An employee completes a personality questionnaire. They receive a PDF feedback report. A facilitator may run a debrief. Then the report goes into a drawer. Six months later, no one can name a behavior the employee has changed.

This pattern is not the assessment's fault. It is the workflow's fault. As Dr. Luke Treglownave Winsborough writes, the legacy industry of 360s, personality reports, and executive coaching was deliberately built to be complex, jargon-heavy, and expensive, accessible mostly to senior executives who could afford a human coach to interpret the results. Everyone else gets the report and nothing else.

That model has two structural failures:

No timing. Coaching happens monthly at best. The moment a behavior actually showed up (the missed feedback, the dropped delegation, the meeting where someone shut down) has already passed.
No translation. Assessment results talk about traits. Day-to-day work demands behavior. Without something to bridge the two, even good data stays inert.

When Gartner predicted the rise of "nudgetech" in its 2025 CHRO predictions, they framed it precisely this way: hyper-personalized nudges with clear explanations for why a change is recommended create a double benefit of improved communication and increased behavior change.

A nudge is only powerful if it is grounded in something the person trusts. That something is the assessment.

What is AI coaching?

AI coaching is the use of an artificial intelligence system, typically a large language model wrapped around a structured psychological or behavioral framework, to deliver personalized development guidance in real time.

It is different from three adjacent things people often confuse it with:

AI coaching is not a chatbot. A chatbot answers questions. An AI coach is goal-directed and grounded in a person's actual assessment data, role, and development objectives.
AI coaching is not an LMS. A learning management system pushes pre-built courses to a calendar. An AI coach surfaces the right content at the right moment for the right person.
AI coaching is not a replacement for human coaches. It is an extension of them. Human coaches handle depth, relationship, and the conversations that require a person on the other end. AI coaches handle reach, repetition, and the in-between moments human coaches will never see.

A randomized controlled trial followed 169 participants over six months and found that the experimental group using an AI chatbot coach (Vici) showed statistically significant improvements in goal attainment relative to the control group, and was comparable to human coaches on that specific dimension. Another study published in Harvard Business Review found that AI-coached learners outperformed those in traditional classroom workshops on applied soft-skills tasks.

The headline from both: AI coaching does not replicate the full depth of human coaching. It does something different and complementary, and on the specific job of helping people set goals and act on them, it works.

How AI coaching closes the development gap

AI coaching works on three mechanisms that traditional development programs do not have.

1. Timing. AI coaches operate continuously. A monthly coaching session is retrospective by design. AI coaching can intervene before the meeting, not after the regret. This matches what McKinsey identifies as the key adult-learning principle: feedback and reflection loops are what move classroom insight into behavior, and the loops have to be close to the actual work.

2. Context. A good AI coach knows your assessment results, your role, your goals, and the situation you are walking into. Sola, for example, will give different advice for "How do I delegate this project?" depending on whether the person asking is Candid or Diplomatic on Core Drivers, and depending on whether their team is mostly Adaptable or mostly Disciplined. Generic AI assistants cannot do this; they have nothing to ground the advice in.

3. Scale. Human coaching costs roughly $500 to $1500 per hour and is reserved for a sliver of the workforce. AI coaching can extend a structured, evidence-based development experience to every individual contributor, not just the future C-suite. Deloitte's 2025 trends report makes a similar point: AI can take administrative load off managers so they spend more of their 87% of "non-developing" time actually developing people.

Meet Sola: Deeper Signals' AI assessment coach

Sola is Deeper Signals' AI assessment assistant. It is the layer that turns a Core Drivers or Core Values result into action.

What that looks like in practice:

For the employee: Sola reads your assessment results and answers questions like "How should I approach my new role given my Driven and Considerate Core Drivers?" or "What should I prepare for a difficult feedback conversation with Mia and Thompson?" The advice is grounded in your data, not in generic management writing.
For the manager: Sola generates 1:1 talking points, feedback prompts, delegation guidance, and onboarding plans calibrated to the direct report's personality and values. The manager does not need to interpret a psychological report. They get specific things to say and try.
For the team leader: Sola pulls together team-level dynamics from individual assessments and generates playbooks for the actual problems leaders face: conflict between two team members with clashing Core Drivers, low engagement among Independent-leaning team members, hiring for a missing strength.
For the recruiter and hiring manager: Sola turns candidate assessment data into structured, role-specific interview guides and side-by-side candidate comparisons. (Detailed in our hiring guide.)

Sola is also constrained in ways that general AI assistants are not. Assessment data stays in the Deeper Signals environment and is not used to train external models. Sola is designed for handling assessment data securely, not repurposing it. That is a deliberate design choice, and an increasingly important one as AI ethics in talent decisions becomes a board-level question (see our write-up on how ethics shapes AI assessment).

The technical name for what Sola does is assessment-aware AI coaching: an AI system whose every recommendation is conditioned on a validated psychometric profile.

From assessment to action: The Sola workflow

Here is how a single assessment becomes ongoing behavior change. We will use a composite, anonymized example: a newly promoted product manager (we will call her Lara) at a mid-sized SaaS company.

Step 1. Assessment. Lara completes the Core Drivers Diagnostic in seven minutes. The diagnostic is built on the Five Factor Model of personality and has been validated on over 300,000 working professionals. Her results show a strong Driven and Disciplined profile with a lower Diplomatic high Candid score. She also completes the Core Values Diagnostic, which surfaces Achievement and Independence as her top motivators.

Step 2. Personalized self-coaching. Within minutes, Lara is in conversation with Sola. She asks: "I just got promoted. What should I focus on in my first 60 days based on my results?" Sola responds with a development plan that is specific to her profile: leverage her natural goal orientation by setting clear quarterly objectives, but invest in stakeholder relationships early because lower Diplomatichigh Candid scores can come across as overly blunt under pressure. The plan links out to short learning content on giving developmental feedback.

Step 3. Manager prompts. Lara's manager, who has access to Lara's permissioned summary, gets a different view. Sola generates 1:1 questions calibrated to Lara's profile: "Ask Lara what stakeholder relationships she is most likely to neglect when under deadline pressure. Discuss specific examples." The manager does not need to be a psychologist. They get a script, and a reason behind it.

Step 4. Continuous nudges. Over the next eight weeks, Sola surfaces small reminders that reflect Lara's actual development priorities: a prompt before a planned cross-functional review to slow down, ask for input, and not steer the meeting too early. A prompt to recognize a peer publicly after a project ships. Each nudge is short, contextual, and tied back to a behavior Lara herself agreed to work on.

Step 5. Team coaching. Lara's manager runs a team workshop using the AI Team Coach Sola, which generates a custom playbook based on the makeup of the team: where the team is likely to lock up under pressure, which combinations of personalities will naturally clash, where to invest in connective tissue. This is not pulled from a generic team-effectiveness book. It is a playbook generated from the actual people in the room.

Five steps. One assessment. Three audiences (individual, manager, team). Continuous reinforcement across weeks, not a one-off debrief.

This is the workflow that closes the development gap.

Want to see what Sola produces for your team? Book a demo. Bring an assessment result and a real development question.

Frequently asked questions

1. How is AI coaching different from a chatbot?

A chatbot answers free-form questions with general advice. An AI coach is goal-directed, conditioned on a person's assessment data and role, and structured around recognized coaching frameworks like goal-attainment theory.

2. Does AI coaching replace human coaches?

No. The current evidence is that AI coaching matches human coaches on goal attainment but underperforms them on deeper outcomes like psychological wellbeing and stress reduction. The most effective programs combine both, using AI for scale and continuity and human coaches for depth.

3. What is Sola?

Sola is the AI assessment assistant inside the Deeper Signals platform. It turns Core Drivers and Core Values results into personalized guidance for employees, managers, team leaders, recruiters, and coaches, in real time.

4. Is my assessment data safe with Sola?

Yes. Deeper Signals is SOC 2 and GDPR compliant. Assessment data stays inside the Deeper Signals ecosystem and is not used to train external AI models.

5. Can AI coaching work for individual contributors, not just leaders?

Yes, and this is where its leverage is highest. Traditional coaching has historically been reserved for senior leaders. AI coaching makes the same kind of structured, personalized development available to anyone with an assessment.

From Assessment to Action: How AI Coaching Closes the Development Gap

Customer

Job Title

The development gap is the well-documented distance between leadership assessments and lasting behavior change.

The data on this is concerning. In Gartner's survey, 75% of HR leaders said their managers are overwhelmed by expanding responsibilities, and 69% said managers are not equipped to lead change. Three-quarters of organizations have updated their leadership development programs, more than half have increased spend, and most are not seeing results. Gartner's own conclusion is blunt: traditional seminars and lectures have a negative effect on leader development.

Deloitte's 2025 Global Human Capital Trends report, based on a survey of nearly 10,000 leaders across 93 countries, sharpens the same point: managers spend roughly 40% of their time on administrative work and just 13% developing their people. Over a third (36%) of managers say they are insufficiently prepared for the role. AI, the report argues, can help free managers to do what makes them managers in the first place: coach, develop, and motivate.

McKinsey has been making the same case for over a decade. McKinsey’s analysis of why leadership-development programs fail notes that adults retain about 10% of what they hear in classroom lectures versus roughly two-thirds when they learn by doing, and that behavior change is unlikely without sustained discomfort and reflection. A follow-up McKinsey survey found that organizations whose leaders are self-aware enough to adapt their behavior are four times more prepared to lead change, and successful leadership programs were three times more likely to provide coaching that encourages introspection and self-discovery.

So the gap has three layers:

People do not retain classroom-style content.
Managers do not have time to translate insights into coaching conversations.
Most development happens off the job and is never reinforced in the flow of work.

This is the gap AI coaching is designed to close.

Why most assessment programs do not change behavior

Most assessments still end where development should begin.

An employee completes a personality questionnaire. They receive a PDF feedback report. A facilitator may run a debrief. Then the report goes into a drawer. Six months later, no one can name a behavior the employee has changed.

This pattern is not the assessment's fault. It is the workflow's fault. As Dr. Luke Treglownave Winsborough writes, the legacy industry of 360s, personality reports, and executive coaching was deliberately built to be complex, jargon-heavy, and expensive, accessible mostly to senior executives who could afford a human coach to interpret the results. Everyone else gets the report and nothing else.

That model has two structural failures:

No timing. Coaching happens monthly at best. The moment a behavior actually showed up (the missed feedback, the dropped delegation, the meeting where someone shut down) has already passed.
No translation. Assessment results talk about traits. Day-to-day work demands behavior. Without something to bridge the two, even good data stays inert.

When Gartner predicted the rise of "nudgetech" in its 2025 CHRO predictions, they framed it precisely this way: hyper-personalized nudges with clear explanations for why a change is recommended create a double benefit of improved communication and increased behavior change.

A nudge is only powerful if it is grounded in something the person trusts. That something is the assessment.

What is AI coaching?

AI coaching is the use of an artificial intelligence system, typically a large language model wrapped around a structured psychological or behavioral framework, to deliver personalized development guidance in real time.

It is different from three adjacent things people often confuse it with:

AI coaching is not a chatbot. A chatbot answers questions. An AI coach is goal-directed and grounded in a person's actual assessment data, role, and development objectives.
AI coaching is not an LMS. A learning management system pushes pre-built courses to a calendar. An AI coach surfaces the right content at the right moment for the right person.
AI coaching is not a replacement for human coaches. It is an extension of them. Human coaches handle depth, relationship, and the conversations that require a person on the other end. AI coaches handle reach, repetition, and the in-between moments human coaches will never see.

A randomized controlled trial followed 169 participants over six months and found that the experimental group using an AI chatbot coach (Vici) showed statistically significant improvements in goal attainment relative to the control group, and was comparable to human coaches on that specific dimension. Another study published in Harvard Business Review found that AI-coached learners outperformed those in traditional classroom workshops on applied soft-skills tasks.

The headline from both: AI coaching does not replicate the full depth of human coaching. It does something different and complementary, and on the specific job of helping people set goals and act on them, it works.

How AI coaching closes the development gap

AI coaching works on three mechanisms that traditional development programs do not have.

1. Timing. AI coaches operate continuously. A monthly coaching session is retrospective by design. AI coaching can intervene before the meeting, not after the regret. This matches what McKinsey identifies as the key adult-learning principle: feedback and reflection loops are what move classroom insight into behavior, and the loops have to be close to the actual work.

2. Context. A good AI coach knows your assessment results, your role, your goals, and the situation you are walking into. Sola, for example, will give different advice for "How do I delegate this project?" depending on whether the person asking is Candid or Diplomatic on Core Drivers, and depending on whether their team is mostly Adaptable or mostly Disciplined. Generic AI assistants cannot do this; they have nothing to ground the advice in.

3. Scale. Human coaching costs roughly $500 to $1500 per hour and is reserved for a sliver of the workforce. AI coaching can extend a structured, evidence-based development experience to every individual contributor, not just the future C-suite. Deloitte's 2025 trends report makes a similar point: AI can take administrative load off managers so they spend more of their 87% of "non-developing" time actually developing people.

Meet Sola: Deeper Signals' AI assessment coach

Sola is Deeper Signals' AI assessment assistant. It is the layer that turns a Core Drivers or Core Values result into action.

What that looks like in practice:

For the employee: Sola reads your assessment results and answers questions like "How should I approach my new role given my Driven and Considerate Core Drivers?" or "What should I prepare for a difficult feedback conversation with Mia and Thompson?" The advice is grounded in your data, not in generic management writing.
For the manager: Sola generates 1:1 talking points, feedback prompts, delegation guidance, and onboarding plans calibrated to the direct report's personality and values. The manager does not need to interpret a psychological report. They get specific things to say and try.
For the team leader: Sola pulls together team-level dynamics from individual assessments and generates playbooks for the actual problems leaders face: conflict between two team members with clashing Core Drivers, low engagement among Independent-leaning team members, hiring for a missing strength.
For the recruiter and hiring manager: Sola turns candidate assessment data into structured, role-specific interview guides and side-by-side candidate comparisons. (Detailed in our hiring guide.)

Sola is also constrained in ways that general AI assistants are not. Assessment data stays in the Deeper Signals environment and is not used to train external models. Sola is designed for handling assessment data securely, not repurposing it. That is a deliberate design choice, and an increasingly important one as AI ethics in talent decisions becomes a board-level question (see our write-up on how ethics shapes AI assessment).

The technical name for what Sola does is assessment-aware AI coaching: an AI system whose every recommendation is conditioned on a validated psychometric profile.

From assessment to action: The Sola workflow

Here is how a single assessment becomes ongoing behavior change. We will use a composite, anonymized example: a newly promoted product manager (we will call her Lara) at a mid-sized SaaS company.

Step 1. Assessment. Lara completes the Core Drivers Diagnostic in seven minutes. The diagnostic is built on the Five Factor Model of personality and has been validated on over 300,000 working professionals. Her results show a strong Driven and Disciplined profile with a lower Diplomatic high Candid score. She also completes the Core Values Diagnostic, which surfaces Achievement and Independence as her top motivators.

Step 2. Personalized self-coaching. Within minutes, Lara is in conversation with Sola. She asks: "I just got promoted. What should I focus on in my first 60 days based on my results?" Sola responds with a development plan that is specific to her profile: leverage her natural goal orientation by setting clear quarterly objectives, but invest in stakeholder relationships early because lower Diplomatichigh Candid scores can come across as overly blunt under pressure. The plan links out to short learning content on giving developmental feedback.

Step 3. Manager prompts. Lara's manager, who has access to Lara's permissioned summary, gets a different view. Sola generates 1:1 questions calibrated to Lara's profile: "Ask Lara what stakeholder relationships she is most likely to neglect when under deadline pressure. Discuss specific examples." The manager does not need to be a psychologist. They get a script, and a reason behind it.

Step 4. Continuous nudges. Over the next eight weeks, Sola surfaces small reminders that reflect Lara's actual development priorities: a prompt before a planned cross-functional review to slow down, ask for input, and not steer the meeting too early. A prompt to recognize a peer publicly after a project ships. Each nudge is short, contextual, and tied back to a behavior Lara herself agreed to work on.

Step 5. Team coaching. Lara's manager runs a team workshop using the AI Team Coach Sola, which generates a custom playbook based on the makeup of the team: where the team is likely to lock up under pressure, which combinations of personalities will naturally clash, where to invest in connective tissue. This is not pulled from a generic team-effectiveness book. It is a playbook generated from the actual people in the room.

Five steps. One assessment. Three audiences (individual, manager, team). Continuous reinforcement across weeks, not a one-off debrief.

This is the workflow that closes the development gap.

Want to see what Sola produces for your team? Book a demo. Bring an assessment result and a real development question.

Frequently asked questions

1. How is AI coaching different from a chatbot?

A chatbot answers free-form questions with general advice. An AI coach is goal-directed, conditioned on a person's assessment data and role, and structured around recognized coaching frameworks like goal-attainment theory.

2. Does AI coaching replace human coaches?

No. The current evidence is that AI coaching matches human coaches on goal attainment but underperforms them on deeper outcomes like psychological wellbeing and stress reduction. The most effective programs combine both, using AI for scale and continuity and human coaches for depth.

3. What is Sola?

Sola is the AI assessment assistant inside the Deeper Signals platform. It turns Core Drivers and Core Values results into personalized guidance for employees, managers, team leaders, recruiters, and coaches, in real time.

4. Is my assessment data safe with Sola?

Yes. Deeper Signals is SOC 2 and GDPR compliant. Assessment data stays inside the Deeper Signals ecosystem and is not used to train external AI models.

5. Can AI coaching work for individual contributors, not just leaders?

Yes, and this is where its leverage is highest. Traditional coaching has historically been reserved for senior leaders. AI coaching makes the same kind of structured, personalized development available to anyone with an assessment.

Ready for your Spotlight?

Contact us to book your Customer Spotlight and showcase your work to an extensive, global audience!

Start your free trial today

Free access to Deeper Signals’ quick, scientific assessments, feedback tools, and more.

Start Free Trial

Articles

The Hidden Cost of Low Team Visibility: A Case for Modern Team Diagnostics

The HR leaders fixing their pipelines aren't running another personality offsite - they're treating team data like product teams treat user data. A guide to closing the gap between team visibility and psychological safety, succession, and effectiveness.

Articles

Soft Skills Data That Moves the Needle: Connecting Assessments to Business Outcomes

The CHROs seeing measurable returns aren't running more assessments; they're using talent data the way a CFO uses pipeline data. A guide to connecting soft skills measurement to attrition, quota attainment, and internal mobility.

Articles

Before You Replace Your Team with AI

The real risk of AI adoption isn't the technology. It's making permanent workforce decisions based on temporary pricing. Here's the calculation most organisations are skipping.

Articles

Predict Potential, Reduce Regretted Hires: A Framework for High-Stakes Talent Decisions

Most hiring evaluates the past, not future potential. Learn how structured assessments and soft skills intelligence help reduce mis-hires and improve talent decisions.