AITech Interview with Nicole Helmer, VP of Product Management, Skills & AI at Degreed

Dive into the future of AI-driven skills development with Nicole, VP of Product Management at Degreed. Understand her insights on the essential skills for success, the evolving role of AI, and how companies can create effective upskilling programs to stay agile and innovative in a tech-driven world.

Hello Nicole, welcome to AITech. Could you tell us about your professional background and being VP of Product Management at Degreed?

• My background is a prime example of how taking a skills view opens new opportunities and career mobility. I’ve worked in different roles and industries: defense/public service, consumer advertising, travel, retail, and high-tech. I’ve worked in procurement and operations roles, I’ve been in product and account management roles, and I’ve been a data scientist. Just before joining Degreed, I was leading learning globally for SAP.
• While they may seem unrelated, a skills view of each of these roles shows how similar and overlapping they are.

As someone with experience upskilling and reskilling 50,000 product development and technical colleagues, what key skills do you believe are crucial for future success?

• There are some common skills that everyone will need to develop and hone for future success. These aren’t earth-shattering predictions, but in an age of AI-automated or assisted work, critical thinking, empathy, project management, lean process improvement, and organizational skills will become more critical across nearly all jobs in all industries.
• On a separate note, it’s worth noting that communication skills will increase in importance not only because human interactions will be higher value interactions but also to produce the desired result from an LLM, the ability to communicate questions, challenges, and needs with precision will be similar to being a highly efficient coder today.
• As much as I believe taking a skills view will create a more granular understanding of the labor force, an organization’s workforce, or a person’s career opportunities – I think it’s the unique combinations and permutations of different skills where we’ll see the creative innovation happening in the future. What if a violinist applies their knowledge of vibration to a mechanical engineering problem? Organization design, process management, and team skill composition will be even more critical.

AI is reshaping industries across the world. From your perspective, which industries or job functions are most urgently in need of AI-related skills, and why?

• Every industry and every job that exists today will be reshaped due to AI. The question I contemplate is what percentage of the jobs to be done will be automated, what percentage will be augmented, and which will remain as they are.
• Depending on the answer to those percentages, will the role continue to exist as we know it? Or will the jobs to be done shift so dramatically that the function is eliminated or merged with another adjacent role? I think we’ve only seen the first act of this play – with companies anticipating AI efficiencies conducting mass layoffs. Now, we will see the second act of jobs being reconstructed with ideally more efficient approaches to existing processes – and entirely new jobs to be done will be introduced.
• Job functions may be affected differently – for example, in many cases, we will observe an escalation of complexity where the lower-level work can now be automated. Consider an entry-level customer service call representative. This work will be substantially automated at the lower levels of complexity. Customer service will still be necessary, but the work handled by human reps will be more complex in certain situations. However, in other places, middle-level jobs may be disintermediated; a medical tech may still be required to draw blood, but the work to analyze results may be heavily assisted/automated.
• Rather than pointing out any particular industry, any workers who perform their work primarily at a desk will find a selection of AI skills essential to their future employment, as everything you can do from a computer will be massively reshaped by AI.
• But, I am most excited to see how AI impacts the life sciences industry.

How can companies foster a culture of continuous learning and curiosity when it comes to AI skills, and how do you make sure to apply this at Degreed?

• The ability of companies to motivate their employees to learn AI skills is a potentially great catalyst for creating a culture of learning, but I would put them in that order, not the reverse.
• A culture of learning is one in which individuals know that learning and change go hand-in-hand. To stay agile and resilient as an individual or an organization, the only solution is to sense what potential changes may come your way and invest the time to learn about them.

What metrics or outcomes do you consider when evaluating the success of AI upskilling programs?

• We always work to tie learning objectives to tangible business outcomes, so to give a detailed example of AI upskilling for our product teams, you might aim to deliver new productive “AI use cases” to the market.
• This means the measurement is similar to understanding throughput in a funnel; does learning generate more ideas? Are those ideas feasible, desirable, and viable? Which makes it into development, which makes it to market? And can we compare people who have participated in a learning program vs people who haven’t and their outcomes/contributions to this funnel?

Degreed focuses on making skills the center of all work. How do you envision targeted upskilling programs being designed to address the growing need for AI expertise across all levels of the workforce?

• Targeted upskilling programs for AI need to be both role-specific and person-specific
• On the role-specific side, there will be expectations, which are now more clearly stated in the language of skills to define the expectations of AI skills, proficiency, and competency for your role.
• For example, a non-technical role will need to focus on AI literacy: basic interactions with AI, what it can be trusted for, and anything that is role-specific. For example, marketers will need to know new image or video generation tools. Technical roles will have numerous additional facets for all people these will exist at differing levels of proficiency that they need or come in with.
• So I think of targeted learning along two vectors:
1. Most people need a tailored path that considers their role and their existing skills and helps them learn what they need to meet the expectations for their role.
2. Some people – often in roles that will be substantially transformed or who need to pivot to different roles will need more intensive reskilling programs.
   1. These will need to go beyond general upskilling to prepare employees for entirely new roles and offer intensive training through a more structured program (like a curriculum or academy) designed to take someone from point A to point B.

How do you incorporate ethics and responsible AI use into its upskilling programs, and how do you see this focus growing in importance?

• Ethical AI is only possible with a foundational understanding of how AI works, so learning about AI technology and its ethical implications must go hand-in-hand. The depth of understanding around ethical concerns—such as fairness, transparency, and accountability—should match or even exceed an individual’s proficiency with AI technology.
• o I have the sense that there’s a rush to upskill on the technical and literacy sides of AI, but I worry this isn’t always paired with a focus on the broader implications of AI decisions. Too often, ethics catch up after the fact, sometimes only in response to high-profile failures. For example, incidents that highlight the need for more profound foresight, like Google Gemini’s incident in which it told a kid to ‘please die,’
• o Looking ahead, I see a growing need for upskilling on how to anticipate unintended consequences (which I think is not an oxymoron!) and on how to mitigate the risk of using probabilistic and therefore unpredictable technology vs. what we’re used to with rules-based tech.

What are the ways in which organizational leaders can ensure that learning programs are aligned with both employee development and business goals?

• At Degreed we talk about looking for the intersection of organizational needs and employee aspirations and we focus on three key steps:
  1. Define Business Goals and Identify Gaps: Start by being reasonably clear about organizational priorities and where skill gaps exist. Use available tools and technologies to refine this understanding, but don’t wait for perfect clarity. Leaders can start small, such as organizing roundtable discussions with team leaders to identify common pain points and immediate needs. This creates a focused starting point for alignment.
     1. 1. Provide Visibility to Employees: Share insights about organizational needs and highlight growth paths that align with individual aspirations. Show employees how their existing skills connect to these paths and how they can contribute to organizational success. This transparency helps employees see the value in their development efforts.
     2. 2. Enable Skill Development: Deliver targeted learning programs to help employees build the skills identified in steps one and two. This is where the alignment truly materializes—without actionable learning opportunities, defining needs and aspirations remain unfulfilled. Iterating on these programs based on feedback ensures their continued relevance and impact.
• It is going to take humanity several years to catch up with the 4th Industrial Revolution skill expectations, so there will be a plethora of skills shortages and opportunities over that time. Organizations and individuals will benefit by starting

How will the landscape of AI education change in the coming years, and elaborate on the ways in which organizations can create a sustainable learning ecosystem that continuously adapts to both the evolving demands of AI technology and the personal development needs of employees?

• AI is going to revolutionize learning in two key ways.
  • First, AI will allow organizations to pinpoint needs with incredible accuracy, hyper-personalize learning for individuals, and measure change more meaningfully.
  • Second, AI itself will become a new way to engage with learning, such as through AI coaches, like Degreed Maestro. AI coaches can provide skill check-ins, career coaching, adaptive learning pathways, real-time guidance, and feedback.
• Sustainability is partly a function of these two significant changes but incorporates the additional aspect of constant change; the company is growing and changing, employees are growing and evolving, skills they have are developing or decaying, and skills and technologies are trending up or down.
• A sustainable learning ecosystem must be treated like a muscle that requires consistent effort to build and maintain.
  • This means setting clear goals for upskilling aligned with future needs, integrating continuous learning opportunities directly into workflows, and holding leaders accountable for fostering growth. A strong learning culture isn’t static—it’s a deliberate, ongoing investment in keeping the workforce and the organization agile and resilient.

As a leader in driving Degreed’s upskilling strategy, what advice would you give to other organizations looking to implement their own AI upskilling and reskilling programs?
I don’t think that AI upskilling is notably different from upskilling on any other skill topic (DevOps, Security, Negotiation), so my advice is the same for all:

• Start with Clear Goals, Not Perfection: Define your business objectives and identify key skill gaps, even if your understanding isn’t perfect. Use tools and technologies to refine this over time, but don’t delay action while waiting for a flawless plan.
• Align Organizational Needs with Employee Aspirations: Focus on the intersection of what the business needs and what employees aspire to learn. Be transparent about these needs with the organization, ideally showing employees how those skills can advance their careers and organizational priorities.
• Make Learning Accessible and Relevant: Use AI to personalize learning paths, delivering the right content at the right time based on an individual’s existing skills and growth potential in your industry.

QUOTE FROM AUTHOR:

My dad used to say, “Sometimes good enough is good enough,” but the trick is to figure out when. I think about this one a lot.  We want that relentless drive for excellence, but we also need to celebrate the wins and progress along the way. I also live by “if you think something nice, say something nice” – it costs you absolutely nothing to do it, it makes you and the other person feel good, and as a bonus, people who feel appreciated do more than expected.

Nicole joined Degreed from SAP where she most recently served as the VP of Learning and Development globally. In this role she was responsible for the functional skill development for all 107,000 employees at SAP reporting to the Chief People Officer. Additionally, for the past three years Nicole steered SAP’s own journey to become a skills-based organization starting by establishing a common taxonomy, building consistent skill measurement, and evolving into a fully skills-based job architecture which sets the foundation for the full suite of skills use cases across the enterprise. SAP was recognized as a lighthouse company for their skills transformation program by the World Economic Forum in early 2024.

Nicole holds a master’s in data science from the Institute for Advanced Analytics at NC State, and bachelor’s in business from the University of Maryland, College Park. She currently lives in Atlanta, GA, with her husband and two young daughters.