Defining "High Potential" - CURIOSITY

I have spent countless hours on every continent working with high-potential leaders to recognize something important: early indicators of future success are often less about what someone knows and more about how they relate to what they don't yet know.

The pattern I’ve seen in my own career suggests there are SIX early signals to spot high potential talent. This is the first of a six-part series examining those qualities, beginning with…

Curiosity: They ask questions that are not required of them

This is probably the most obvious one, but also the easiest to misunderstand. Lots of people ask questions because they need information.

High-potential people ask questions because they are trying to understand how the system works.

Early in their careers, they tend to ask things like:

• Why do we do it this way?
• How does this decision affect the rest of the organization?
• What happened before I got here?
• Who else is affected by this?
• What would happen if we changed it?
• How does the business actually make money?

The distinction is important.

Ordinary curiosity is informational.
High-potential curiosity is connective.

They don't simply accumulate knowledge. They look for patterns. They do not remain confined to one discipline. My trajectory moved across learning, executive development, organizational effectiveness, talent, global leadership, technology, and now AI enablement. That kind of career usually reflects a person who keeps asking:

What else is connected to this problem?

That is a powerful early signal. 

Become a Transformation Translator

 

The most valuable Talent leaders will become “transformation translators”.

The biggest gap I see emerging inside companies is between:

Technology leaders who understand what AI can do

and

Business leaders who understand what needs to change

and

Employees who must actually change how they work.

The organizations that succeed will need people who can translate across all three groups.

That is a new leadership category. I would describe it as:

The Human Transformation Leader

Someone who understands:

• AI and emerging technology
• Workforce strategy
• Leadership
• Organizational behavior
• Learning science
• Change management
• Culture
• Executive influence

I’ve spent my career as an instructional designer, learning leader, talent strategist, HR generalist, or technology leader. That experience and background crosses several of the disciplines that will need to converge.

I believe that the learning leaders and talent development leaders of today will need to evolve into Global Talent and Workforce Transformation Executives

with a specialization in:

Building the leadership, talent, and organizational capabilities required for the AI-enabled enterprise.

That positioning incorporates:

• Global experience
• Executive development
• Organizational effectiveness
• Talent strategy
• Workforce transformation
• AI enablement
• Large-scale learning transformation
• Leadership of international teams

The defining question for our profession:

How do organizations transform their workforce fast enough to take advantage of AI without destroying trust, capability, culture, and leadership capacity in the process?

The answer will not primarily come from technology departments. It will require leaders who understand humans, organizations, leadership, and transformation simultaneously.

That is the intersection where I believe our greatest opportunity lies over the next decade.

Talent strategy: Build, buy, automate, augment or eliminate?

The skills-based organization will evolve into something more sophisticated.

For the last several years, HR has discussed the "skills-based organization." I believe that idea will survive—but evolve.

The first generation focused heavily on:

• Skills taxonomies
• Skills databases
• Skills assessments
• Skills matching
• Learning recommendations

The next generation will need to answer a more difficult question:

Which capabilities should we build internally, buy externally, automate with AI, augment with AI, or eliminate entirely?

That turns talent strategy into a form of enterprise portfolio management which is a very different approach!  Future CHROs and Chief Talent Officers will increasingly need to understand:

Business strategy + workforce economics + technology + organizational design + capability development.

This is important because it moves people in Talent and Development closer to the strategic center of the business. 

Reinventing Career Development

This may be the most underappreciated issue in talent management. Historically, careers have been relatively predictable:

Entry-level role → increasing responsibility → management → leadership.

But what happens when AI automates many of the tasks that employees traditionally performed to learn their profession? How does a young financial analyst develop judgment if AI performs much of the initial analysis? How does a consultant learn problem-solving if AI produces the first draft? How does a manager learn to manage if organizations eliminate layers of management?

This creates what I would call a potential experience-development crisis. Organizations will have to deliberately create new developmental pathways through:

• Internal gigs
• Project-based assignments
• Rotational experiences
• Human mentoring
• Simulations
• AI-assisted practice
• Cross-functional work
• Deliberately designed stretch experiences

LinkedIn's workplace learning research already points toward the convergence of learning, leadership development, career development, internal mobility, and AI adoption. Organizations with stronger career-development practices appear better positioned for AI transformation, while AI upskilling increasingly varies by role rather than following a one-size-fits-all model. Those of us with backgrounds in learning see this as a major opportunity: Move beyond “Head of Learning” toward “Architect of Talent Capability and Career Systems.”

The Rapidly Shifting World of Enterprise Capability Building (formerly known as Talent and Development)

The title suggests the transformation of Talent and Learning from a “development function” into an enterprise capability-building function for the AI economy.

In other words, the future of our profession is not primarily about better training. It is about helping organizations redesign how work gets done, what capabilities humans need, and how leaders manage increasingly AI-augmented organizations.

My background positions me unusually well for that shift.  Here’s why:

AI will fundamentally change the work—not just the skills—of employees

The first wave of Generative AI was largely about tools: Copilot, ChatGPT, automation, productivity, and experimentation.  I help to lead this work in  highly regulated fields, so “getting it right” was a solid requirement.

The next decade will be about work redesign.

Organizations are now confronting questions such as:

• Which tasks should humans continue to perform?
• Which tasks should be automated?
• Which should be performed collaboratively by humans and AI?
• What decisions should never be delegated entirely to AI?
• How should jobs be redesigned when AI eliminates 20–40% of certain tasks but not the entire job?
• How do you develop employees when AI is doing some of the work through which employees traditionally gained experience?

Recent evidence suggests enterprise AI adoption is spreading across functions and seniority levels, while organizations are still learning how to integrate it into actual workflows rather than simply deploying the technology.

That distinction is enormously important.

Most corporate learning organizations are still built around the old model:

Identify a skill gap → build a program → deliver learning → measure participation.

That model will increasingly be insufficient.

The emerging model will be:

Redesign work → identify future capabilities → redesign jobs and careers → build leadership capability → create learning in the flow of work → continuously measure adaptation.

That is a much more strategic mandate—and it aligns closely with my combination of executive development, organizational effectiveness, talent strategy, and AI enablement.

Talent Management: Chess or Checkers?

I was inspired by a recent conversation with a colleague to examine this question more closely.  We were comparing and contrasting the work of different teams within an organization.  He likened another team’s work to checkers, while suggesting that we were playing chess – and pointed out that there was really no comparison.  He was right!

If you think about it, checkers is largely a game of tactical skills, about blocking your opponent’s next move or preserving yourself.  Checkers is fairly reactionary, a jagged continuum of random movements.  On the other hand, chess is quite strategic.  Rather than simply having red and black pieces (or as the Glossary of Checkers Terminology calls them, “men”), there is an array of pieces that can be used in a myriad of ways.  Chess is far more strategic and analytical; it requires thought , forethought and afterthought.  There is no checklist in chess; you must use your brain.

When it comes to talent management, many companies are still playing checkers.  I prefer chess.   I began to ask myself “what is it that differentiates those organizations playing chess from those still playing checkers?”   I’ve concluded that there are four significant characteristics that differentiate those organizations:  a clear strategy, a holistic approach, the absence of boxes, and an inclusive “what if” mindset.

Strategy is the foundation upon which the function is built, right?  You’d be surprised at how many organizations simply do not have a talent management strategy.  It seems so basic, yet I believe it is the primary reason so many organizations fail miserably at talent management.  A talent management strategy keeps the organization and its leaders “honest”.  It roots out nepotism and favoritism and brings in objectivity.  It levels the playing field, creating equal opportunities for all qualified candidates.

It must be stated that talent management is not a stand-alone activity.  One cannot see “talent review” in isolation; it must be connected to employee engagement, compensation, performance management, and leadership development.  There must be actions, insights and deliverables that come from talent review discussion, and those discussions should involve both supporters of a candidate and those with whom there have been disagreements.  Talent management professionals need to push for a 360 degree view of the subject.  Without intention, the act of managing talent becomes a “check the box” activity.  Sadly, this mindset exists in too many companies.  Stakeholders are sometimes led to believe that there is a strong bench of “ready now” talent and that the talent pipeline is deep when, in fact, there are merely names on a list; names of people who could succeed a leader in the event of a departure.

Then, for organizations who actually do have a formal process for reviewing talent, many ruin it all by insisting on putting names in boxes --- the dreaded, antiquated 9 BOX grid.  Checkers players love the 9 box because checkers requires one to simply put pieces (men) in boxes, move them across the board and viola’, you’re either a winner or a loser.  Chess requires meaningful thought, even internal conversation, about your next move.  Chess requires that you ignite thought from a different part of your brain.  Unlike checkers, chess requires that you consider a wide range of possibilities.

Pursuing the thought that chess requires consideration of a wider range of possibilities, so should talent management.  Talent leaders should constantly be asking themselves “what if?”   In Peter Block’s “The Answer To How Is Yes”, he asserts that when we ask “how”, that we’ve already revealed our bias and that we’re looking for reasons not to act.  By shifting away from “how” to “what if”, we again call on a different part of our brain to explore opportunities.  Who knows, we might just find a possibility that we’d never find playing checkers.

Measuring ROI on Leadership Initiatives

Many leadership and executive development learning managers find themselves at a loss for quantifying a return on investment for development programs and initiatives.  Often, L&D leaders default to qualitative results versus quantitative results, which is understandable since quantitative results require considerably more digging.  Organizations without data scientists or employees trained in knowledge and insights often struggle to tell a story using data.  Below are some tips to make this work easier for leadership and executive development professionals.

Step 1:  Define your purpose.  I recommend a purpose statement like “the purpose of this research is to determine the influence of leadership development programs and executive coaching on these variables:  performance and potential, retention, engagement and job movement.”  It is imperative to have your senior leaders’ buy in before beginning the process.  Here is a place to demonstrate leadership: make recommendations but be sure to incorporate factors that are important to leaders.

Step 2:  Narrow your focus.  Are you measuring ROI on executive coaching, leadership programs or mentoring?  Each discipline will require a different standard for measurement.   For example,
programs may last a week while leadership development experiences may last much longer.  Most executive coaching engagements range from six to twelve months.  Also, it is critical to clearly define your audience.  At this point, many leaders realize that their efforts at developing leaders haven’t been as focused as they should have been.  Often, emerging leaders are placed in signature programs for which they were not properly vetted and ready.  This practice compromises the validity of the executive development experience.  Getting the right people in the right programs for the right reasons is key.  This is the point at which you clearly define your audience; remember: right person, right program, right time.

Step 3:  Decide on the three to five key questions you want to answer.  For example, is there a common profile for each participant?  Obviously, if you’re measuring the impact of a women’s leadership program, being female is a common characteristic of all participants.  Apply that line of reasoning for each program or initiative; for example, male/female, job grade, age, tenure, ethnicity, veteran status, gender identity, etc.  All this data becomes extremely relevant when slicing, dicing and targeting your development initiatives.

Is retention important?  If so, compare retention rates of program participants against those who haven’t participated in a program.  Is there a difference?   

How does participation in a program affect performance and potential?   Programs that hit their target are ones that increase not only the participant’s performance, but also the performance of their team.  Look at the performance of the leader and their team one year prior to the program/event, the year of the program/event and up to three years after the program/event.  There should be a positive correlation between performance/potential and the program.  If there isn’t, then it’s time to reexamine the content of the program.  A redesign may be in order.

Are program participants being promoted at a higher rate than those who haven’t participated in a program?  Again, if the right person was in the right program for the right reason (growth and development), then the participant should be promoted at a faster rate than non-program participants.

Step 4:  Determine and vet your process with learning leaders.   In most cases, these 6 steps will produce consistent results:

1.       Determine the specific research questions

2.       Determine the data requirements

3.       Gather the necessary data

4.       Conduct data analysis

5.       Summarize the findings

6.       Report the findings to key stakeholders.

A few finer points to also consider:

If you are measuring several programs or initiatives, call out and report on each one individually.  As you compare results, you may see misalignments that can be corrected.  The same holds true for measuring executive coaching engagements.  Look at the coaching results from each coach and compare them side by side.  Are you getting the results you expect?  After a deep analysis, you’ll have the quantitative data to make that determination.

If your development programs are “staged” meaning that there is a program for front line leaders, emerging leaders, high potential leaders, etc., is there course overlap?  Is there a pure sequence for the programs?  This may/may not be important in your organization, but if it is important, now you’ll have data to support your decision to continue or course correct.

To better protect your investment, it is important to look at separations from the company, both voluntary and involuntary.  If there are a disproportionate number of voluntary separations, you may be training people to leave your organization for other jobs.  If there are a disproportionate number of involuntary separations, are you properly identifying the right people during your talent management and succession planning discussions?

Does the investment drive employee engagement?  If so, is it appropriate to broaden the offerings to drive higher engagement?   Is feedback provided at the end of a program?  If so, how is that data used to enrich the program? 

Finally, has the concept of “differentiated investment” become a common expression in talent management and succession planning discussions?  If so, are the leaders identified able to articulate what that means and the expectations that come from such investments?