Ignis AI blog

From Hidden Traits to Observable Capabilities | Ignis AI

yigal@ignisai.ai (Yigal Rosen) — Wed, 27 May 2026 20:48:50 GMT

Last week at the Association for Talent Development (ATD) Conference in Los Angeles, I had the opportunity to speak with senior Learning & Development leaders from across industries. From fast-growing SMBs to Fortune 500 enterprises the conversations surfaced one question again and again:

“How do we make human power skills visible in a way that is measurable, developable, and tied to real business outcomes?”

The conversations were remarkably consistent. L&D leaders and CHROs are under increasing pressure to demonstrate measurable ROI from leadership development, training, and workforce transformation initiatives. Yet many of the capabilities organizations care about most, creativity, judgment, communication, adaptability, leadership, and collaboration, remain difficult to measure with traditional approaches.

At the same time, these capabilities have never been more important.

As generative AI rapidly reshapes work, organizations are discovering something profound: technical AI fluency alone is not enough. The future belongs to people who can combine AI with human judgment, creativity, ethical reasoning, adaptability, and collaborative leadership.

And yet this combination of skills is precisely what traditional talent systems struggle to see.

A Visit to Griffith Observatory

While in Los Angeles, I made my annual visit to the Griffith Observatory. The Observatory never fails to inspire reflection on science, discovery, and the power of making the invisible observable.

Earlier in my career, as a PhD student, I worked at another observatory where I had the privilege of helping students, families and visitors experience that same sense of wonder: transforming distant, abstract phenomena into something directly observable and deeply human. Standing again beneath the Griffith Observatory dome, I was reminded how profoundly visibility changes understanding and how similar that mission feels to the work we are doing today at Ignis AI.

Standing there overlooking the city, watching visitors line up to peer through the Observatory’s historic Zeiss refracting telescope, I kept thinking about the Observatory’s mission: transforming the “visitor into observer.”

That phrase stayed with me.

For decades, organizations have treated human capabilities largely as inferred traits rather than directly observable behaviors. We infer leadership from job titles. We infer communication from résumés. We infer creativity from educational pedigree or reputation.

But inference is not measurement.

The Griffith Observatory changed humanity’s relationship with the sky by making observation accessible at scale. More people have looked through its telescope than any other telescope on Earth. Suddenly, what once felt distant and abstract became visible and shared.

I believe we are at a similar moment in workforce transformation.

Essential human capabilities such as creativity, leadership, communication, and collaboration are no longer hidden traits.

They are observable behaviors that can be measured and developed.

That realization sits at the core of what we are building at Ignis AI.

Power Skills Practice and Proof

At Ignis AI, we call this approach Power Skills Practice and Proof.

The idea is simple but transformative:

Organizations should not have to guess whether employees are developing the capabilities required for the future of work. They should be able to observe growth, measure progress, and connect development efforts to meaningful outcomes.

Historically, that has been difficult because power skills are fundamentally different from technical skills.

You can test whether someone knows a software platform or a compliance process with a traditional assessment. But how do you measure creativity under ambiguity? Judgment under pressure? Adaptability when circumstances change? Leadership when there is no obvious right answer?

The challenge becomes even more complex in the age of AI.

Today, AI can help generate polished outputs, presentations, strategies, analyses, and recommendations. Surface-level quality is no longer enough to tell us where the human contribution ends and the AI contribution begins.

That changes everything about assessment and talent development.

The most important question is no longer:

“Can someone produce a good answer?”

It is:

“How does someone think, adapt, collaborate, evaluate tradeoffs, and generate differentiated judgment in partnership with AI?”

That is where the most interesting work lies ahead.

Moving Beyond Inference

One of the themes I heard repeatedly at ATD was frustration with talent systems built primarily on inference.

Resumes, job histories, credentials, engagement metrics, and AI-generated skill profiles can provide useful signals. But they often tell us more about exposure than actual capability.

Two people can have identical résumés and dramatically different levels of creative thinking, leadership effectiveness, or adaptability.

This distinction between inferred skills and measured skills is becoming increasingly important in AI-enabled work environments - and it is one worth understanding in depth. It also explains why the data feeding your talent systems matters as much as the systems themselves.

Organizations need ways to observe how people actually reason, communicate, and solve problems in realistic contexts.

That is why we believe the future of assessment and development is performance-based, contextual, and embedded in authentic work activity.

Not static tests.

Not one-time snapshots.

Not personality proxies.

But continuous signals of capability emerging through real-world practice.

Observable Human Capability in the Age of AI

Our recent research at Ignis AI explores how advances in agentic AI, computational psychometrics, and scenario-based assessment can make these capabilities visible at scale.

In our work, creativity is not treated as an abstract personality trait. It is operationalized as observable behavior: the ability to generate non-obvious alternatives, reframe problems, adapt under changing constraints, and sustain differentiated thinking in complex environments. Our research on measuring creativity in the age of generative AI - presented at BIGAI@MIT - explores exactly how to distinguish genuine human originality from AI-assisted output.

This matters deeply for workforce transformation.

As AI systems become increasingly capable, organizations face a growing risk of homogenization. AI can improve fluency and productivity, but it can also push individuals and teams toward increasingly similar outputs and patterns of thinking.

The differentiator will not simply be who uses AI.

It will be who can think differently with AI.

Who can challenge assumptions.

Who can synthesize across domains.

Who can adapt under ambiguity.

Who can bring human creativity and judgment into partnership with intelligent systems.

Rethinking the System Itself

What excites me most is that this moment requires more than simply building new assessments or adding another leadership training program.

It requires rethinking the system itself.

The future of talent development will not be built around episodic training disconnected from work. It will increasingly revolve around continuous readiness, embedded development, and observable evidence of capability growth in context.

In other words:

Practice and proof embedded in the flow of work.
Continuous, contextual, defensible signals of skill.
Human capability inferred not from credentials alone, but from authentic behavior over time.
Development systems that distinguish AI fluency from human judgment and creativity.

The science behind this approach is detailed in our white paper on advances in AI and psychometrics for Power Skills measurement, and validated through our peer-reviewed study with Arizona State University and LERN, which demonstrated measurable improvements in decision-making quality and leadership effectiveness in real-world deployments.

This is just the tip of the iceberg.

The conversations at ATD reinforced that organizations across industries are beginning to recognize the same emerging reality:

The future of workforce advantage will depend not only on AI adoption, but on our ability to develop and measure the uniquely human capabilities that allow people to use AI wisely, creatively, and adaptively.

And perhaps most importantly:

To make those capabilities visible.

I’m grateful for the thoughtful conversations, questions, and challenges shared by leaders throughout the week at ATD.

The future of human capability development is becoming observable.

About the Author Dr. Yigal Rosen is Co-Founder and Chief Product Officer at Ignis AI, and an internationally recognized leader in human capability assessment at the intersection of AI, computational psychometrics, and learning sciences. Before founding Ignis AI, he held senior roles at Harvard University, BrainPOP, Pearson, and ACT, and led two landmark OECD/PISA global assessments - the 2015 Collaborative Problem Solving Assessment and the 2022 Creative Thinking Assessment - that transformed how education systems in more than 70 countries measure human capability. He holds a Ph.D. from the University of Haifa, completed postdoctoral fellowships at Tel Aviv University and Harvard, and is currently pursuing his Executive MBA at MIT Sloan School of Management.

Why Scoring ‘Soft’ Skills Is a Hard Problem | Ignis AI

Ilia Rushkin — Wed, 01 Apr 2026 13:04:20 GMT

If I asked you to rate your own communication skills, most people reading this would say they’re above average, which is, of course, mathematically impossible. This isn’t vanity; it’s that these skills are genuinely hard to see in ourselves. A musician knows instantly when they hit a wrong note. But when did a colleague last pull you aside and say, “You’re not actually listening in meetings - you’re just waiting to talk”?

This is the problem my team at Ignis AI is solving: how do you measure collaboration or communication in a way that is fair, consistent, and actually useful - not with a vague rating scale or a manager’s gut feeling, but with real evidence?

There’s No Answer Key for “Be a Good Collaborator”

Most tests have one right answer. That model falls apart when you’re assessing human interaction skills. In our Ignis AI PowerSkillsAssessment™, a person watches a short video of a virtual team meeting where two colleagues are at odds, then responds to an open-ended question: as the team lead, how would you handle this?

There’s no single correct answer - but there are clearly better and worse ones. Someone who says “I’d just make a decision and move on” is demonstrating something very different from someone who recognizes the underlying tension, creates space for both perspectives, and thinks about preserving trust across the team. Both might sound reasonable on the surface. One reflects a much deeper understanding of how teams actually work.

Defining What ‘Good’ Actually Looks Like

Before AI touches a single response, we do the hard human work of defining what skill proficiency looks like at each level of someone’s career. For communication, someone early in their development might communicate clearly enough most of the time, but tend to miss what’s not being said - the anxiety or political tension underneath a disagreement. Someone more advanced does something qualitatively different: they listen in a way that surfaces what people aren’t saying, they reframe charged situations in ways that move the whole group forward, and they stay composed under real pressure.

These descriptions are written and validated by assessment scientists and subject matter experts. They define the target. Without this foundation, you’re not really measuring a skill - you’re measuring something that feels like one.

How the AI Scores an Open-Ended Response

Different methods are used for different skills. As a fundamental method, the AI is given two things: a detailed rubric describing what each score level means, and a set of real, previously scored responses as reference points. Language is ambiguous, and rubrics alone leave room for interpretation. Showing the AI concrete examples - “this response scored a 3, here’s why; this one scored a 1, here’s why” - grounds its scoring in actual human judgment rather than abstract description.

We also run multiple independent scoring models on each response and compare their answers to reduce variance and increase confidence. Every score comes with a written rationale, which makes human review faster and builds a growing library of explained examples that continuously improves the system. Early results show our AI scores agree with human expert scores at a level consistent with high-quality professional assessment.

Creative thinking is a uniquely important skill, and in many respects a special one. In our assessment, we measure several distinct types of creativity, some requiring specialized approaches. Using machine learning techniques, we can quantify the degree to which a person synthesizes multiple ideas and generates genuinely novel solutions. In a sense, we can assign a number to their ability to process information flexibly and think outside the box — making what was once considered intangible, measurable.

AI Scales Human Expertise - It Doesn’t Replace It

Human expertise guides and calibrates AI. We keep people in the loop to ensure models stay aligned with what actually matters. Otherwise, models can learn shortcuts. For instance, it’s easy for a system to start rewarding longer answers or answers with more sophisticated vocabulary regardless of whether the thinking is any good.

From Scores to Signal: Estimating True Skill

So far, we’ve been talking about how individual responses get scored. But a score on a single task isn’t the same thing as someone’s underlying skill. In practice, people produce a range of responses—even within the same skill area. Context matters, prompts differ, and performance can fluctuate. Treating any one score as “the truth” would be both noisy and misleading.

To bridge that gap, we use latent-variable modeling. Instead of taking scores at face value, we treat them as observations generated by an underlying, unobserved proficiency. The model aggregates evidence across multiple tasks and takes into account relationships among skills, and infers that hidden variable—what the person is actually capable of, not just what they showed in one moment. This approach gives us more than a single point estimate. It produces a full probabilistic picture of someone’s skill. In other words, we move from a single number to a structured understanding.

That richer representation becomes especially valuable downstream. Whether you’re matching someone to a role, building a balanced team, or identifying development opportunities, it’s not just about “how good” someone is—it’s about how confidently you know it, and in what contexts that assessment holds.

Does It Actually Work?

We’ve run two validation studies. The first, with 353 U.S. adults, showed strong consistency across all seven skills we measure. Human reviewers found clear, meaningful differences between high and low scorers - especially in originality, adaptability, and integrating competing perspectives. The second study, with employees at two technology companies, reinforced those results. One finding I find particularly telling: our seven skills turned out to be largely independent of each other. Being strong analytically doesn’t predict much about your collaboration skills. The one exception was leadership and communication, which were more tightly linked - because so much of effective leadership comes down to how well you communicate.

The Bottom Line

Collaboration, communication, leadership, creative thinking - these are the skills that matter most in an AI-driven world, and exactly the ones that have been written off as too fuzzy to measure. The result: talent decisions about these skills get left to interview intuitions and manager impressions, both of which carry well-documented biases.

Over more than a decade of research with the OECD, Harvard, and Microsoft - including a communication assessment that reached over 100,000 employees globally and predicted job performance better than traditional certification tests - my colleagues and I have helped establish that these skills are measurable. Not easy to measure. Not perfectly measurable. But measurable with real rigor, at scale, in ways that give people and organizations something they can actually act on.

People deserve to have their real strengths seen. We’re committed to building the tools that make that possible.

About the Author Dr. Ilia Rushkin is VP of AI & Data Science at Ignis AI, where he leads the AI/ML and data science systems powering the PowerSkillsAssessment™. His prior work includes adaptive learning research at Harvard University with Microsoft and HarvardX, and Principal AI/ML Engineer at BrainPOP, where he holds multiple U.S. patents in AI-enabled assessment.

Why Haven't Skills Kept Up With the Workforce? | Ignis AI

juli@ignisai.ai (Juli Masi) — Mon, 23 Mar 2026 14:45:44 GMT

For 25 years, I’ve had a front-row seat to one of the most profound evolutions in business: the transformation of the workforce.

I’ve seen HR move from administrative support to strategic driver. I’ve watched HCM systems evolve from record-keeping tools into intelligent platforms. And through it all, I’ve had the privilege of working alongside leaders, solving real problems, and helping organizations build workplaces where people and businesses thrive.

But what’s happening now feels different.

We’re not just evolving anymore—we’re redefining what it means to work.

The Shift No One Can Ignore

AI is no longer on the horizon—it’s embedded in how we hire, develop, and manage talent. Roles are changing faster than job descriptions can keep up. And organizations are realizing something critical:

Technical skills alone are no longer enough.

What differentiates high-performing individuals and organizations today are what we call power skills:

Leadership
Creativity
AI fluency
Communication
Analytical thinking

These aren’t “soft skills.” They’re business-critical capabilities.

The Rise of Measurable Human Capability

For years, HR has struggled with one persistent challenge: how do you measure what truly matters?

We’ve gotten very good at tracking performance metrics, engagement scores, and productivity. But the skills that actually drive innovation, adaptability, and resilience? Those have often been left to intuition.

That’s changing.

Organizations are now prioritizing the ability to assess, develop, and scale power skills across their workforce. Not as a nice-to-have—but as a competitive advantage.

Where HCM Is Going

The future of HCM isn’t just about better systems. It’s about better insight into people.

We’re moving toward:

Skills-based organizations instead of role-based ones
Continuous development instead of periodic training
Data-driven people decisions grounded in human capability

And at the center of it all is a simple truth:

The workforce of the future will be defined not just by what people know—but by how they think, adapt, and lead.

A Personal Reflection

After 25 years in this field, what stands out most isn’t the technology.

It’s the people.

The customers who trusted us to solve their most pressing challenges. The colleagues who became lifelong friends. The shared mission to make work better—for individuals and for organizations.

That mission hasn’t changed.

But the way we achieve it has.

The Opportunity Ahead

We have a chance right now to redefine work in a way that is more human, more intelligent, and more impactful than ever before.

The organizations that succeed will be the ones that:

Invest in human capability as much as technology
Measure what truly drives performance
Build cultures that value adaptability and growth

Because in the end, the future of work isn’t just about systems.

It’s about people—and the skills that power them.

Boardroom Thinking, Not Boiler Room Thinking | Ignis AI

dustin@ignisai.ai (Dustin Clinard) — Thu, 05 Mar 2026 22:00:24 GMT

For decades, the dominant model of skill development has been what I would call boiler room thinking.

It focuses on the technical mechanics of a job: the tools you use, the processes you follow, and the domain knowledge you accumulate over time. The assumption is simple: if someone masters the operational details, they will succeed.

But increasingly, that assumption no longer holds.

The most valuable work happening inside organizations today is not about executing predefined tasks. It’s about figuring out what work actually needs to be done in the first place.

That shift requires a very different set of capabilities.

Instead of asking:

Do you know how to do this job?

Leaders increasingly need to ask:

Can you frame the right problem?
Can you communicate complex ideas clearly?
Can you collaborate across disciplines?
Can you think creatively about solutions that don’t yet exist?

This is the difference between boiler room thinking and boardroom thinking.

Boiler room thinking is execution-oriented.

Boardroom thinking is sense-making oriented.

In modern organizations, most meaningful work happens in environments that are ambiguous, collaborative, and dynamic. Projects rarely sit neatly inside one role or function. Problems evolve as you work on them. Solutions require input from people with very different expertise.

Success, therefore, depends on human power skills: creative thinking, communication, collaboration, leadership, and analytical reasoning.

Research increasingly reinforces this shift. The World Economic Forum’s Future of Jobs Report projects that nearly 40% of current skills will be disrupted or transformed by 2030, with employers placing increasing value on human-centered capabilities such as creativity, leadership, and social influence.

In other words, the future of work is not just about technical expertise. It is about how humans think together.

But this realization exposes an uncomfortable truth.

Even when organizations agree that these skills matter most, they often lack a clear way to understand them.

Technical skills are relatively easy to measure.

You can test coding ability, financial modeling, or regulatory knowledge.

Human skills are different.

How do you measure someone’s ability to frame problems creatively?

How effectively they listen in a difficult conversation?

How well they build alignment across competing priorities?

For most of modern business history, these capabilities were treated as intangible qualities — something you might infer through interviews or performance reviews but rarely measure with precision.

That assumption is beginning to change.

Advances in cognitive science, psychometrics, and artificial intelligence are making it possible to evaluate the very skills that define effective leadership and collaboration. Capabilities like communication, creative thinking, and teamwork can now be assessed through real-world scenarios that reveal how people actually think and respond in complex situations.

This matters because when something becomes measurable, it becomes developable.

Organizations can move beyond vague ideas of “soft skills” and start treating human capabilities with the same rigor they apply to technical expertise.

And individuals can begin to understand something even more important:

Not just what they know - but how they think, communicate, and create value with others.

In a world increasingly shaped by intelligent machines, the ultimate differentiator will not be who can execute tasks the fastest.

It will be who can ask better questions, frame better problems, and bring people together to solve them.

That is boardroom thinking.

And it may be the most important skill of all.

Early-Career Talent Development in the Age of AI | Ignis AI

dustin@ignisai.ai (Dustin Clinard) — Tue, 16 Dec 2025 16:57:17 GMT

Discover how AI-driven solutions are transforming early-career talent development in financial services, equipping organizations to future-proof their workforce and empower emerging professionals.

Navigating the AI Revolution: What Early-Career Talent Needs Now

The landscape for early-career professionals in financial services has changed rapidly with the rise of artificial intelligence (AI). Just a few years ago, candidates were primarily evaluated on academic performance and work experience. Today, AI-driven platforms assess technical proficiency and, increasingly, the power skills that define adaptability in a tech-evolving market.

According to the World Economic Forum’s 2023 Future of Jobs Report, 44% of workers’ core skills are expected to change within the next five years as a result of technology and automation. Early-career professionals must now blend technical know-how with creativity, critical thinking, and collaborative abilities to remain competitive.

Building Power Skills and Technical Proficiency for Tomorrow’s Financial Leaders

Financial services organizations are seeking talent with both foundational technical skills and human-centric power skills. While AI can automate data analysis and repetitive tasks, it cannot replicate creativity, nuanced communication, and leadership. Emerging professionals who hone these skills are better positioned to drive innovation and navigate complex, ambiguous challenges.

Ignis AI’s PowerSkillsAssessment solution is designed to help employers identify candidates who demonstrate the blend of adaptability, problem-solving, and leadership necessary for future success. By combining technical upskilling with intentional power skills development, early-career talent can accelerate their path to leadership.

Leveraging AI-Driven Assessments for Smarter, Bias-Resistant Hiring

Traditional hiring methods are often limited by resume-based screening and unconscious biases. AI-driven assessments now offer a more predictive, objective, and bias-reducing approach to evaluating early-career candidates. These systems can evaluate not only technical potential but also critical power skills, resulting in smarter, more equitable hiring decisions.

Research cited by the Harvard Business Review highlights that AI-driven hiring tools, when designed responsibly, can reduce bias and improve the accuracy of candidate-job fit. The shift to data-driven talent decisions empowers organizations to discover high-potential individuals who may have been overlooked by legacy processes.

Integrating Human-Centered AI into Talent Development Strategies

To maximize the impact of AI in early-career development, organizations must take a human-centered approach. This means leveraging AI to inform—not replace—talent decisions, ensuring that the unique strengths of each individual are recognized and developed. Platforms like Ignis AI integrate responsible AI models that keep people at the heart of workforce transformation.

By providing actionable insights on both technical and power skills, human-centered AI enables tailored learning and growth pathways. This approach supports a culture of continuous feedback, mentorship, and collaborative learning, helping early-career professionals build the resilience and adaptability required in a rapidly changing environment.

Future-Proofing Financial Services through Continuous Upskilling and Data-Driven Growth

The financial services sector is evolving rapidly, and continuous upskilling has become a non-negotiable for both individuals and organizations. AI-driven insights help identify skills gaps and growth opportunities, empowering early-career professionals to take charge of their development. Data-driven talent management solutions, like those from Ignis AI, enable organizations to align their workforce capabilities with future strategic needs.

While AI will continue to reshape the early-career landscape, the enduring value of human skills—creativity, leadership, collaboration, and critical thinking—remains clear. By investing in these areas, financial services can future-proof their organizations and unlock the full potential of their emerging talent. Stay tuned for our next post, which will explore how managers can best support and lead early-career professionals in the age of AI.

Why Skills-Based Hiring Is the Future | Ignis AI

yigal@ignisai.ai (Yigal Rosen) — Tue, 16 Dec 2025 16:57:16 GMT

The New Hiring Landscape: Rapid Change and Unpredictable Needs

As technology advances, industries are being reshaped almost overnight. Skills that were in high demand five years ago are becoming obsolete, while new skills — particularly those required to work alongside AI and automation — are emerging fast.

Organizations need talent that is not only technically proficient but also adaptable, creative, collaborative, and emotionally intelligent. These are the Power Skills — once considered “soft skills” — that now make the difference between teams that thrive in change and those that struggle.

Unfortunately, traditional hiring methods aren’t built to measure Power Skills effectively. They rely too heavily on keyword-matching, past experience, and unconscious biases, often leading to poor hiring decisions, inflated costs, and longer time-to-hire.

Ignis AI: Transforming Talent Acquisition with Predictive, Human-Centered AI

This is where Ignis AI’s next-generation hiring platform comes in. Powered by proprietary human-centered AI and assessment technologies, Ignis AI delivers a predictive hiring approach that goes beyond resumes and technical skills.

What Makes Ignis AI Different?

Deep Evaluation of Power Skills: Ignis AI assesses candidates on critical Power Skills such as creativity, communication, problem-solving, and adaptability — the skills most predictive of future success in dynamic econtexts.
Bias-Reducing, Human-Centered AI: Ignis AI is designed with ethical AI principles, minimizing unconscious bias and giving hiring managers deeper, objective insights into candidate potential.

Proven Business Impact: Why Customers Choose Ignis AI

Customers leveraging Ignis AI see measurable improvements across critical hiring and business metrics:

Time-to-Hire — Accelerated hiring cycles by focusing on true capability match, not endless resume reviews.

Cost-per-Hire — Reduced hiring costs through precision targeting of the right talent.

Quality of Hire — Improved retention and performance by selecting candidates with the right combination of Power Skills and technical expertise.

By focusing on what truly matters — the skills and potential that drive business success — Ignis AI helps organizations future-proof their workforce in the age of AI and continuous change.

Skills-Based Hiring is No Longer Optional — It’s Mission-Critical

In a world where job roles evolve faster than ever, hiring for skills and potential is the smartest way forward. Organizations that embrace skills-based, predictive hiring gain a competitive edge by building agile, capable teams ready to tackle tomorrow’s challenges.

Ignis AI is more than a hiring tool — it’s a strategic talent acquisition platform designed for the future of work. By integrating deep Power Skills assessment with technical evaluation, Ignis AI enables companies to hire the right people faster, more cost-effectively, and more accurately than ever before.

Ready to Transform Your Hiring?

If you’re ready to leave behind outdated hiring models and adopt a next-generation, AI-powered, skills-based hiring platform, it’s time to explore what Ignis AI can do for your organization.

Discover how Ignis AI can help you hire best-fit talent with real impact, real ROI, and undeniable evidence of success.

It’s Not Workday’s Fault. It’s Your Hiring Data | Ignis AI

juli@ignisai.ai (Juli Masi) — Tue, 16 Dec 2025 16:57:14 GMT

Why the Workday Discrimination Lawsuit Should Be a Wake-Up Call for HR Leaders.

When the news broke about a class-action lawsuit accusing Workday of enabling discrimination through its AI hiring tools, it sent shockwaves through the HR and tech community. And it is positioned to set a major precedent with AI in Human Capital Management.

As someone who has spent over twenty-five years in talent strategy, technology, and inclusion, I had two immediate reactions:

This was inevitable.
Workday isn’t the problem. Your data is.

AI Doesn’t Create Bias. It Inherits It.

Let’s be clear: AI does not have a propensity for bias. Humans do. This is a well-established concept in psychology and cognitive science.

Algorithms, especially those in talent platforms, are only as good—or as flawed—as the data we feed them.

In this case, what most people don’t realize is that tools like Workday rely heavily on inputs such as:

Job descriptions
Historical hiring decisions
Resumes of past “successful” candidates
Compa-defined filters and preferences

So, when bias shows up in the outcomes, we shouldn’t just blame the system—we should ask: What patterns and priorities have we hard-coded into our hiring culture without even realizing it? AI is NOT the problem. And once you understand what IS the problem, AI can provide several solutions.

The Real Culprit: Bad, Biased Job Descriptions

In my 25 years of working in the Human Capital Management outsourcing industry, I’ve had well over a hundred customers of all sizes and across all of the major industries. And one theme is constant: job descriptions are broken. I know, I’ve seen them.

They’re:

Vague and bloated with outdated requirements
Coded with gendered or exclusionary language
Trained on the subjective profiles of people who are already in the job

If you’ve ever written a job description that says “must have a degree from a top university” or “minimum 10 years of experience in a startup” … congratulations, you’ve just unintentionally biased the algorithm.

I know a little something about outsourcing to companies like Workday – but that does not mean you can outsource responsibility to them.

Blaming Workday or any HCM tech vendor for algorithmic bias is like blaming a spreadsheet for a bad budget. These tools amplify what’s already in your system—they don’t invent it.

Shift the Focus: From Blame to Better Design

Here’s how companies can respond constructively:

Audit Your Hiring Data

Analyze past hiring patterns: who gets through, who doesn’t—and why.
Clean up job descriptions to reflect real, inclusive skill requirements (use AI to help)
Job Description clean up not a viable solution at this time? Use AI to measure your bias, then give the model instruction to minimize/ eliminate

Stop Using "Success Profiles" Based on Yesterday’s Leaders

If your current leadership team is 90% white and male of a certain age, do you really want your AI optimizing for that?

Use Validated, Skills-Based Assessments in hiring

Tools like PowerSkillsAssessment™ (used in platforms like Ignis AI) provide structured, bias-resistant ways to evaluate what really matters: capability, not pedigree.

Train Your Teams, Not Just Your Tools

Hiring managers and recruiters still make final calls. Let’s make sure they understand where bias hides—and how to fight it.
Provide your hiring managers and recruiters the tools and insights to make science-backed, data driven decisions around hiring, not just how well algorithms match resumes to job descriptions (like what Ignis AI is building to address the future of the workforce).

The Workday Lawsuit Is a Wake-Up Call—Not a Warning Label against AI

This moment should push us not to fear AI, but to understand it better, and get serious about the quality of what we feed into it.

These technologies provide so many solutions and they keep getting better. But until you understand how to work with your own data—hiring histories, job specs, implicit definitions of “fit”—you’ll keep getting the same results from different tools.

It’s not Workday’s fault. It’s your hiring data. Fix that—and the tools will work just fine.

Final Thought

Of course, I must caveat that we do not know all the details of the Workday lawsuit yet; more information could be brought to light. But that does not change anything mentioned here. Having been in this business for as long as I have, I’ve seen fads come and go, I’ve seen other companies propose solutions, and I’ve seen how poor, outdated, inaccurate data is the cause of many issues and headaches in the industry.

Here at Ignis AI we are poised to deliver real solutions to existing workforce problems, including this one. If you’re looking for help in building a people-first, future-ready workforce, let’s talk.

Unlock potential. Transform careers. Elevate the future of work.