Steven George, Author at Skills Base

Garbage in, AI-enhanced garbage out: Pitfalls in Skills Intelligence

Steven George — Wed, 16 Apr 2025 03:54:45 +0000

Organizations are increasingly finding ways to leverage Artificial Intelligence (AI) to automate processes, supplement capability, and gain insights from data. The skills management space is no exception, with AI-driven Skills Intelligence being a hot topic. One area that has gained interest is the use of AI in the identification and measurement of skills within a workforce. However, the approach taken to leverage AI in this context can impact the ultimate quality of the insights generated. This article explores the pitfalls of taking a hands-off approach to AI-driven Skills Intelligence and contrasts it with a tried and proven methodology.

The problem that sparks the journey

A business executive or departmental head is speaking with stakeholders and has a sudden moment of realization: they don’t understand the skills of their workforce in any substantive way. There is no system, report, or even spreadsheet that can tell them the skill strengths and weaknesses across teams, what gaps exist, or which skills people are actually interested in developing.

Meanwhile, competing businesses seem to be ahead of the game, using skills intelligence to tailor their offerings, engage talent, and drive innovation. Panic sets in. Measuring skills at a granular level feels like an overwhelming task — there’s no abundance of spare resources, and the time to value feels distant.

Then comes the allure of a solution that promises to do it all.

The promise of a hands-off solution

In recent years, vendors offering AI-powered skills intelligence platforms have made bold (and occasionally ridiculous) claims such as “Instantly understand the skills of your entire workforce” and “No manual input needed”. These promises have resonated with organizations desperate for answers — but reality has often proven more complex.

Some vendors have had to walk back their claims after customers discovered that AI-generated skill profiles were inaccurate, lacked context, or were based on outdated or irrelevant data. In one notable case, a vendor suggested that its AI could analyze internal communications and infer employee skills from email tone and word choice. The approach sparked privacy concerns and was ultimately abandoned. Others promised auto-generated skills taxonomies tailored to each organization — only for customers to realize that these taxonomies bore little resemblance to their actual workforce needs.

These examples illustrate a broader point: while AI is a powerful tool, using it blindly or too passively in the skills space can lead to false confidence, misinformed decisions, and wasted effort.

Garbage in, garbage out

The old adage rings true in the age of AI: garbage in, garbage out — or worse, AI-enhanced garbage out. When the data used to train, feed, or inform an AI model is unreliable or biased, the insights it generates will inevitably mirror those flaws.

Consider a common scenario: a company uses historical resumes to populate skill profiles. These resumes were often written years ago, optimized to land a job rather than reflect current capabilities, and rarely updated. They were never designed for skills analysis — yet are treated as credible sources. The result? Misleading insights that shape real decisions about talent development, hiring, or workforce planning.

Contextual relevance

AI lacks context unless it’s explicitly provided. If an organization can’t clearly articulate its goals, priorities, or expectations as they relate to skills, then any insights generated are likely to be generic at best — or counterproductive at worst.

This becomes a particular challenge for organizations that are new to skills management. If the organization hasn’t built a foundational understanding of its skills landscape, where can even the most advanced AI expect to find skills data of any quality or reliability? In the absence of data, the AI will make its own assumptions (a phenomenon called “AI hallucination”) – often influenced by “filling in the blanks” with generalized market data, internet sources, or biased historical patterns.

When this happens, the organization may not realize the misalignment until it’s too late — typically when the outputs are exposed to employees. At that point, the damage is done: employees feel misunderstood, misrepresented, or even threatened. The organization may then be forced to backpedal and re-do the exercise, this time with proper guidance — but now under scrutiny and with lower trust.

Cultural change impact

Skills are personal. They represent an employee’s sense of identity, value, and future. If an AI system — perceived as a “black box” — starts making statements about someone’s capabilities without their involvement, resistance is inevitable. This is especially the case when employees don’t understand how it arrived at the result, where it got its information from, or what decisions it made in the process.

A hands-off approach not only risks technical failure, but cultural rejection. Employees may feel sidelined, judged, or reduced to data points in a system they don’t understand and didn’t help build. This can erode trust and engagement at a time when the organization needs buy-in the most.

To become a skills-based organization, cultural alignment is essential. That requires transparency, collaboration, and active participation — not automation in isolation.

Using AI for the strengths it offers

So, what’s the alternative? It’s not to avoid AI — far from it. The opportunity lies in using AI for what it does best, while combining it with human input, experience, and oversight.

Here’s a better approach:

Keep the business in the driver’s seat. AI should support the organization’s strategy — not control it. Ensure that business leaders define the goals, guide the process, and retain control over decisions. This alignment is essential to keep AI outputs relevant, trusted, and actionable.
Start small. Don’t try to boil the ocean. Begin with a pilot group, a targeted skill set, or a focused initiative. This keeps risk low and learning high.
Put in just enough effort. There’s no need to manually manage everything — but some strategic effort is essential to get the foundation right.
Learn as you go. Early mistakes are valuable. Make them fast, correct them quickly, and use those lessons to shape future stages.
Partner with the right vendor. Look for partners who see themselves as partners, not just software providers. They should help you shape the process, and provide solid advice from their extensive experience – not just sell the product.
Build internal buy-in. As stakeholders see that the process has integrity and is grounded in reality, support will grow. That makes future rollouts increasingly easier, faster, and more effective.
Focus on long-term value. Skills intelligence isn’t a one-off project — it’s a journey. Success lies in sustainable, repeatable progress.

Final thoughts

AI-driven skills intelligence can offer tremendous value — but only when used with intention, transparency, and care. A hands-off approach may sound attractive in the short term, but it often leads to disappointing outcomes, rework, and disengagement.

Instead, organizations should treat AI as a powerful assistant, not an autonomous driver. With thoughtful implementation and human-centered design, AI can enhance — not replace — the wisdom, experience, and vision required to build a truly skilled workforce.

The post Garbage in, AI-enhanced garbage out: Pitfalls in Skills Intelligence appeared first on Skills Base.

A skills-based approach to Workforce Training

Steven George — Thu, 06 May 2021 00:37:40 +0000

The benefits of an effectively executed organizational training program are well documented; improved employee performance, increased productivity, improved employee engagement and advocacy, reduced employee turnover, improved morale, and more.

Workforce development has also become increasingly essential in today’s economy and competitive landscape.

Throwing money at training is easy, however achieving a return on that investment can be difficult. Which training is actually required? Which training will produce the biggest “bang for buck”? Which training can be deferred? More broadly, which training actually moves the needle on helping the business achieve its objectives?

Organizations that employ a skills-based approach are able to more efficiently and effectively deliver training programs that produce genuine and measurable impact, greater ROI, and strategic alignment.

A skills-based approach

To understand training requirements using a skills-based approach, it is first necessary to understand the skills of the workforce at a granular level. This provides an organization or department with a precise picture of exactly which employees have which skills, and makes gaps and strengths transparent. Using this information, organizations can map targeted training to gaps, factoring target skill levels assigned to employees’ roles.

The objective is to optimize training expenditure by targeting the right training to the right people at the right time.

The importance of skills management

For organizations that rely on highly technical skills, processes or products, effective skills management is the only way to make sense of hundreds or thousands of skills across hundreds or thousands of employees. Having current and reliable skill information available in a timely manner is paramount in effective decision making for many of today’s global businesses. Without it organizations risk “flying blind”, making decisions based on assumptions, incomplete information, or gut-feel, risking inefficient and ineffective expenditure of the organization’s scarce resources such as money and time.

However, this may still be viewed as progress in reporting and metrics. After all, any improvement is an improvement. But without proper attribution, improvement may not be reliable or predictable. In today’s competitive landscape organizations are increasingly needing to act smarter and more efficiently in order to retain competitive advantage.

Finding the gold: Filling the key skill gaps

When an organization sets up any capability, for example Software Development, it generally has objectives it is attempting to achieve. These objectives could be to create a new software solution, enhance an existing solution in line with a roadmap, or to simply maintain business-critical solutions. In any case, these objectives will generally manifest as strategy documents, annual plans, and notably employee position descriptions, since any new hires for the new capability need to be able to positively contribute to the objectives of the organization.

But position descriptions are deliberately broad, industry-oriented, and as such usually non-specific to an organization’s unique processes. Otherwise, the only suitable candidates would be ex-employees. Whilst broad position descriptions fill the need for recruitment activities, they are not specific or detailed enough in a skills management context to be able to add reliable and real value to decision making, particularly regarding workforce training.

A deeper dive is needed, firstly by identifying the granular skills that make up the specific abilities used daily by an employee that directly contribute towards achieving the objectives of the department and organization.

What constitutes a Skill?

The term “skill” can have many definitions as it is broadly used, however in the context of skills management, a skill is a granular learned ability possessed by a person, and especially the proficiency in performing or executing that ability. We say “granular”, because when it comes to something like optimizing training, being specific really counts.

Filling the gap

Once an organization has defined its granular skills, it is necessary to determine the current (actual) skill level for each skill and each employee. This is where things can get daunting, since organizations can easily identify hundreds or thousands of skills, and have hundreds or thousands of employees. A spreadsheet won’t do the job here, however quality Skills Management software makes the task easy.

With current skill levels ascertained, target skill levels should also be set for each skill and each person (generally by role). The resulting difference between the current (actual) and target is the “Skill gap”.

Targeting these precise gaps with specific training is where the advantage of using a skills-based approach to workforce training really excels. It ensures that training is not wasted:

Where proficiency exists and is already assessed and validated, and
Where training is excessive for the target skill level assigned to an employee (or role)

In short, training budgets are optimized and maximized by assigning training only to high-value targets that directly align with organizational objectives by strategically evolving the workforce accordingly.

The proof is in the results

Organizations that use a skills-based approach to training report a more strategically aligned workforce, with measurable improvements in skill areas that directly address organizational objectives. These organizations enjoy a more efficient training spend, as well as the benefits of a more transparent and objective approach to training delivery.

References

The Importance of Training and Development in the Workplace Via 2020 Project Management
How to Create Better Workforce Development Training Via Vector Solutions

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