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Forbes
26-06-2025
- Business
- Forbes
The Case For Moving Capability Building From HR To PMO
Peter Beven, CEO of iEC Professional and a leader at the intersection of education, technology innovation and organizational transformation. This may upset some human resources HR leaders, but I believe that for too long, capability building has been miscategorized as an HR function. Because of this, it often ends up sitting quietly in the corner of the organization, far from the action. It gets packaged as employee retention, training plans, learning management system (LMS) completions—training programs that tick boxes but don't shift performance. But here's the uncomfortable truth I have come to realize: In project-based organizations where execution speed, delivery performance and adaptability are everything, this model of HR-run capability building isn't just outdated—it may be holding you back. Shifting From HR To PMO If capability is the engine of delivery, why isn't it embedded where delivery actually happens—the project management office (PMO)? My underlying rationale for this argument is that HR is designed for consistency, whereas the PMO is designed for performance. This isn't a dig at HR. HR plays a vital role in managing workforce systems, policies and employee life cycle processes that support organizational stability, compliance and culture. But let's be honest: HR is geared for consistency and control. It's designed to support the employee life cycle, not the delivery life cycle. The PMO, on the other hand, is designed for movement, for execution, for impact. It works at the sharp edge of strategy: turning ambition into reality through coordinated delivery. It sees firsthand where the capability bottlenecks are, which tools are underutilized and which teams are underperforming. Capability As Both A Delivery And A Development Issue Projects fail when skills don't match the work to be done. In complex project environments, this issue can become even more profound. In my experience, many missed milestones and blown budgets come down to one thing: The team didn't have the right capability at the right time. And yet we still treat capability building as something separate from delivery. Teams wait for annual training plans, generic courses or slow approval processes. Meanwhile, the work keeps moving. I believe capability building needs to be in the hands of the PMO because they are already embedded in project planning, performance tracking, milestone reviews and delivery risk management. PMO typically finds out, faster and more clearly than HR, where capability gaps are emerging and what's needed to close them. Shifting capability building from HR to PMO may not work for everyone, but I highly recommend it if your organization is heavily tech-centric or has high digital maturity. I have found through working with my own company that embedding capability building into the PMO of a project-based organization can lead to teams being more agile, responsive and equipped with the vital skills needed for productivity transformation—particularly in increasingly complex environments. This can help you consistently deliver projects and services on time, on budget and with outcomes that matter. Five Steps To Make The Shift For Project-Based Organizations 1. Reframe capability as a delivery asset. Stop talking about training plans and start talking about delivery enablement. Position capability as a lever for delivery excellence and not as an HR 'initiative.' 2. Relocate or co-locate the capability function. Move the capability function into the PMO. Create a dual operating model where the PMO drives delivery-focused capability while HR supports foundational and enterprise-wide needs. 3. Measure what matters. Put performance over participation: Adopt a capability measurement model that links skill growth to project performance and productivity metrics, including timelines, quality, cost and customer outcomes. 4. Build just-in-time, embedded learning infrastructure. Replace generic training with project-embedded, microcredential learning that is aligned to priority competencies. Build content libraries, enable expert coaching and create systems that deliver learning in the flow of work. 5. Build a competency-based workforce. Redesign your jobs based on the skills needed for each role, and align all roles, skills and development to clearly defined capabilities. Utilizing AI For Just-In-Time Capability Building AI can be one of the biggest enablers for shifting capability-building duties from HR to PMO. Forget the narrative of self-styled gurus that say AI is just the copilot to jobs; it can do so much more. It stands to totally transform jobs, not just at the periphery but across the board. For example, earlier this year, Moderna announced its move to merge technology and HR into a single function under the CIO office to align with changes brought on by AI technology. This is just one of the latest signs that artificial intelligence is bringing big changes to the workforce. Here are several ways you can utilize AI to help your PMO deliver in capability building: • Use project delivery analytics for real-time capability gap detection. • Develop and deliver role-specific expert learning content, on demand and derived from industry best practices. • Verify and validate skills in all training and by all providers. • Enable dynamic skills mapping and personalized learning-pathway generation, especially for capability uplift. • Embed AI coaching within project management tools. • Use project metrics and performance data to drive learning. • Implement workforce capability forecasting and delivery-readiness simulation. Final Thoughts Let's be clear: This shift may ruffle feathers. Traditional HR and L&D leaders may see this as a land grab. It's not. It's about recognizing that transformational capability building shouldn't be managed as a support function anymore—not when delivery is on the line. This is a call to elevate capability, not sideline HR. I believe the future is collaborative, but it's also urgent. In project-based environments, capability should be owned by those closest to the work, the performance and the results. This shift is about repositioning capability as a strategic, embedded and responsive engine of delivery excellence. And that requires bold change. The PMO has evolved from a governance body to a strategic powerhouse. It's time to go further and make it the beating heart of organizational capability. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
Forbes
16-06-2025
- Business
- Forbes
How SaaS Companies Can Reduce AI Model Bias
As businesses realize the high value of artificial intelligence in improving operations, understanding customers, setting and meeting strategic goals, and more, embedding AI into their products is moving from a 'nice to have' feature to a competitive necessity for software as a service companies. However, it's essential to tread carefully; SaaS companies must be aware of the risk that both implicit and explicit bias can be introduced into their products and services through AI. Below, members of Forbes Business Council share strategies to help better detect and minimize bias in AI tools. Read on to learn how SaaS companies can ensure fairness and inclusivity within their products and services—and protect their customers and brand reputation. To build AI tools that people trust, businesses must embed ethical AI principles into the core of product development. That starts with taking responsibility for training data. Many AI products rely on open, Web-scraped content, which may contain inaccurate, unverified or biased information. Companies can reduce exposure to this risk by using closed, curated content stored in vector databases. - Peter Beven, iEC Professional Pty Ltd It is impossible to make AI unbiased, as humans are biased in the way we feed it with data. AI only sees patterns in our choices, whether they are commonly frowned upon patterns, like race and location, or not-so-obvious patterns, like request time and habits. Like humans, different AI models may come to different conclusions depending on their training. SaaS companies should test AI models with their preferred datasets. - Ozan Bilgen, Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify? You can't spot bias if your test users all look and think the same. Diverse testers help catch real harms, but trying to scrub every point of view just creates new blind spots. GenAI's power is in producing unexpected insights, not sanitized outputs. Inclusivity comes from broadening inputs, not narrowing outcomes. - Jeff Berkowitz, Delve Evaluations are key. SaaS businesses cannot afford expensive teams to validate every change when change is happening at a breakneck speed. Just like QA in software engineering has become key, every business must implement publicly available evaluations to validate bias. This is the most thorough and cost-effective solution out there. - Shivam Shorewala, Rimble Using third-party AI tools for independent audits is key to spotting and correcting bias. This approach helps SaaS companies stay competitive and maintain strong client trust by ensuring fairness, transparency and accountability in their AI-driven services. - Roman Gagloev, PROPAMPAI, INC. SaaS companies need to extend prelaunch audits with real-time bias monitoring to monitor live interactions. For example, one fintech customer reduced approval gaps by 40% by allowing users to flag biases within the app, dynamically retraining models. Ethical AI requires continuous learning and fairness built up through user collaboration, not solely code. - Adnan Ghaffar, LLC SaaS companies can reduce bias by diversifying their training data and using interdisciplinary teams when developing an AI model. They should also implement routine audits to verify that algorithms are fair and transparent, ensuring their AI is inclusive and equitable. This is essential to mitigate alienating customers and damaging brand equity, as biased AI systems lead to inequity. - Maneesh Sharma, LambdaTest Bias starts with who's at the table. If your team doesn't reflect the people you're building for, neither will your model. Audit your data before you code. Fairness isn't a feature you add later, but one that should be baked into the build. If you get that wrong, the harm done is on you. Inclusivity is a strategy, not charity. If your strategy's biased, so is your bottom line. - Aleesha Webb, Pioneer Bank We embed fairness audits at each stage of model development—data curation, training and output testing—using diverse datasets and human-in-the-loop validation. For SaaS, where scale meets intimacy, unchecked bias can harm thousands invisibly. Building trust starts with building responsibly. - Manoj Balraj, Experion Technologies In the age of social media, the best way to minimize bias is to let the users tell you about it. Collecting user-generated opinions through testing, MVPs and feedback forms is the best way to ensure your product is free from developer or even marketer biases. Just make sure you have a good number of users to test your AI product. - Zaheer Dodhia, One powerful way SaaS companies can tackle bias in AI models is by rigorously testing them against open-source and indigenous datasets curated specifically to spotlight underrepresented groups. These datasets act like a mirror, reflecting how inclusive or exclusive your AI really is. By stepping outside the echo chamber of standard data, companies gain a reality check. - Khurram Akhtar, Programmers Force Most teams focus on fixing bias at the data level, but the real signs often surface through day-to-day product use. I tell SaaS companies to loop in support and success teams early. They're closest to the users and usually flag issues first. Their feedback should feed directly into model reviews to catch blind spots that don't show up in training data. - Zain Jaffer, Zain Ventures SaaS companies should simulate edge-case users, including small sellers, niche markets, nonnative speakers and more, to test how AI performs for them. Real inclusivity means optimizing for the exceptions, not just the averages. If your product works for those on the edges, it'll work for everyone. - Lior Pozin, AutoDS Integrate diverse voices at every stage, from design and data to deployment. Uncovering bias begins with owning our blind spots, so use honesty as a guide. Inclusive AI isn't just ethical—it's also essential for relevance, reach and trust in today's diverse world. - Paige Williams, AudPop SaaS companies should establish a continuous feedback loop with external experts, such as ethicists and sociologists, to review AI model outcomes. These experts can identify unintended consequences that technical teams might miss, ensuring the AI model serves all communities fairly. This proactive approach helps avoid costly mistakes, improves user satisfaction and strengthens long-term brand credibility. - Michael Shribman, APS Global Partners Inc. Treat bias like a security bug by documenting it, learning from it and making spotting it everyone's job rather than just the AI team's responsibility. Build bias reports into internal processes and reward early detection. Building operational systems around bias detection keeps products fair, inclusive and trusted. - Ahva Sadeghi, Symba What finally shifted things for us was bringing real users from underserved communities into our QA process. We stopped pretending to know what fairness looks like for everyone. It turns out, when you ask the people most likely to be excluded, they'll tell you exactly how to fix it. - Ran Ronen, Equally AI One way SaaS companies can detect and minimize bias in their AI models is by conducting equity-focused impact assessments. These assessments can evaluate whether the model produces better, worse or neutral outcomes for each user group. This is important, because equity ensures that users from different backgrounds receive fair and appropriate outcomes, promoting true inclusivity and preventing systemic disadvantage. - Ahsan Khaliq, Saad Ahsan - Residency and Citizenship One way SaaS companies can better detect and minimize bias in their AI models is by actively inputting their own unique ideas and diverse perspectives into the system. In this way, the AI can be guided to develop solutions that reflect true inclusivity, ensuring that the outcomes are fair and representative of a wide range of users. - Jekaterina Beljankova, WALLACE s.r.o SaaS companies must shift from a 'software as a service' mindset to a 'service as software' mindset to recognize AI as a dynamic, evolving system. This mindset encourages continuous bias audits, inclusive datasets and real-world feedback loops, which are essential for fairness, trust and long-term relevance in diverse markets. - Kushal Chordia, VaaS - Visibility as a Service
