On AI, Steph Curry and Developing a "Positionless" Workforce
Our weekly round-up of news, perspectives, predictions, and provocations as we travel the world of AI-augmented work.
🎤 This Week’s AIX Factor Podcast: HR.com's State of People Analytics. Mark Vickers, HR.com’s Chief Research Analyst & Data Wrangler - aka the Hardest Working Man in HR Research (TM) - joins us to discuss their new report, “HR.com's State of People Analytics.” The first sentence in the report’s executive summary wastes no time dropping the hammer: “HR has an ongoing analytics problem.” Two stats that bear this out: Only 22% of organizations feel effective in HR analytics; twice as many (44%) say their organizations are only somewhat effective or not effective at all in this area. Mark suggested strategies to reverse this trend, including:
✔️ Start small with meaningful metrics
✔️ Leverage existing technologies (AI) to derive actionable insight
✔️ Use these technologies and other resources to train HR staff
✔️ Hold workshops on storytelling with data to effectively communicate insights to other parts of the organization
An enlightening, free-wheeling conversation. Check it out!
Next Week’s Guest/s: Alain Verstandig and Laurette Bennhold-Samaan of NetExpat join my co-host Michael Pinker and me to discuss AI and Cultural Agility. Alain and Laurette are experts in Global Workforce Deployment, which is fundamentally about establishing and deploying talent - in the right location, culture, and context. The conversation will center on how to navigate today’s “hyper interconnected” world, and what organizations ought to do to help people develop soft skills - communication, EQ, cultural literacy - things that we do better than our automated colleagues, that will ensure that humans are at the forefront driving innovation and growth.
Goals Before Roles: Developing the Positionless Workforce in the Age of Generative AI
AIX’s Charles Epstein
I’m not sure what a Venn diagram of people with interests in HR, AI, and the NBA would look like (actually, I do), so it’s an admitted risk suggesting that organizations can learn a lot from Golden State Warriors point guard Steph Curry, a once-in-a-generation talent who transformed the game, ushing in what’s referred to as “positionless basketball.”
"Positionless basketball" refers to a style of play where traditional basketball positions (point guard, shooting guard, small forward, power forward, and center) are less rigidly defined, and players are expected to perform multiple roles on the court. Instead of sticking to specific tasks based on their position (like a center only playing near the basket or a point guard primarily handling the ball), players in a positionless system are versatile and seamlessly switch between scoring, defending, ball-handling, and playmaking.
A recent report from McKinsey and Company titled “Upskilling and reskilling priorities for the gen AI era,” makes the case for prioritizing “goals before roles,” advocating for the development of “durable” skills, leading to a versatile, cross-functional - or positionless - workforce.
To realize the opportunity of generative AI, organizations should take a collaborative, scaled approach to upskilling and reskilling. Reimagining their learning and development (L&D) can help organizations meet the demands of gen AI and elevate L&D functions to be stronger strategic partners for business leaders. Here are three considerations: Goals before roles. While tempting to rush into building gen AI literacy across all roles at once, start with business outcomes and how gen AI investments can enable or accelerate them instead. Define the skills required to deliver these outcomes and identify groups within the organization that need to build those skills. This is important, as gen AI has led to more rapid reshaping and creation of roles; skills—especially durable ones—are a clearer, longer-lasting currency.
The ability to develop durable skills—such as critical thinking, adaptability, collaboration, and problem-solving—has never been more crucial. As AI moves us toward a more versatile, skills-driven workforce, job titles and traditional roles are becoming less important than having the ability to shoot, defend, and play multiple positions regardless of size…by which I mean, using new tools and technologies to develop/acquire skills that add value and drive innovation.
Goals Before Roles
The expression "goals before roles" means prioritizing outcomes and objectives over the specific titles, positions, or responsibilities that individuals hold. It emphasizes focusing on the larger purpose or the result an organization or individual wants to achieve before getting caught up in defining specific roles or assigning tasks.
In practice, this approach helps ensure that roles are created or adapted to achieve key goals, rather than rigidly adhering to predefined roles that may not align with the organization's or team’s objectives. By starting with the goals, organizations or teams can:
Identify the skills and capabilities needed to reach those outcomes.
Build or assign roles that are designed to meet these goals.
Ensure flexibility and adaptability as the goals evolve over time.
This method ensures that AI investments not only accelerate business objectives but also foster long-term adaptability at all levels of the workforce.
Reimagining Learning & Development (L&D) in the Age of Generative AI
Traditionally, learning and development (L&D) has been seen as a support function, often siloed and disconnected from broader business strategies. In the era of generative AI, L&D must evolve into a strategic partner, working closely with business leaders to ensure the workforce is equipped to meet emerging challenges. This shift requires aligning L&D initiatives with business outcomes—enabling AI to fuel skill acquisition in ways that are both targeted and scalable.
Example 1: Identifying Business Outcomes and Targeted Skills Consider a financial services firm seeking to improve customer service efficiency through generative AI. Rather than focusing on training all employees in AI technologies at once, the firm should first identify its core business outcome—improving customer response times and accuracy.
With this goal in mind, the firm can define the skills needed to support this outcome, such as:
Data literacy to interpret and use AI-generated insights.
Advanced communication skills to interact effectively with AI-driven customer service platforms.
Adaptability in learning new AI-powered tools and processes.
By identifying these targeted skills, the firm can strategically upskill its customer service teams while preparing data and IT teams for backend AI management. This approach creates a streamlined path to achieving the desired business outcome without overwhelming employees with unnecessary AI training.
Example 2: Creating Reskilling Pathways Based on Business Needs In manufacturing, AI is revolutionizing supply chain management through predictive analytics and automated processes. A manufacturing company looking to optimize its supply chain might set a business goal of reducing downtime and improving operational efficiency.
To achieve this, the company can reskill its operations teams by focusing on:
Predictive maintenance techniques that leverage AI insights.
Problem-solving skills to address real-time data-driven challenges.
Collaboration between teams to ensure seamless AI integration across supply chain operations.
By tailoring reskilling programs to the specific skills needed to meet their business objectives, the company not only meets immediate operational goals but also cultivates a more resilient, AI-literate workforce prepared for future challenges.
How Gen AI Promotes the Development and Acquisition of Skills
Generative AI is more than just a tool for automating tasks—it is a catalyst for workforce transformation. AI’s ability to analyze data at scale, provide personalized learning experiences, and streamline tasks can accelerate the development of critical skills.
Personalized Learning Experiences
AI can offer personalized learning pathways for employees, adapting to their individual skill levels and learning styles. For instance, AI-driven platforms can deliver targeted training content based on an employee’s current skill gaps, ensuring faster and more effective skill acquisition. A marketing team member could be provided with personalized training on AI-driven customer insights and data interpretation, while an HR manager may receive tailored training on AI-powered recruitment tools.On-the-Job Learning Through AI-Integrated Tools
AI can also facilitate real-time learning as employees use AI-powered tools. For example, a project manager might leverage generative AI to automate routine project tasks, such as timeline tracking and resource allocation, freeing them up to focus on more strategic work while learning new AI functionalities. This type of experiential learning enables employees to grow their skills organically as they adapt to new AI-enhanced workflows.Fostering Cross-Functional Collaboration
AI is uniquely positioned to promote collaboration across departments, as it requires different teams to work together to maximize its potential. A cross-collaborative approach encourages employees to develop soft skills—such as communication, teamwork, and problem-solving—that are increasingly critical in AI-augmented work environments. For example, data scientists and marketing teams might collaborate to analyze AI-generated customer insights, fostering a culture of knowledge-sharing and innovation.Accelerating Skills Transfer and Adaptation
Generative AI also supports the rapid transfer of skills across roles. As organizations continuously evolve, employees may need to transition into entirely new roles that didn’t exist before AI was integrated. AI-driven systems can help identify employees with transferable skills, such as critical thinking or technical aptitude, and create tailored reskilling pathways that help them adapt to new roles quickly. For instance, a sales team member with strong data interpretation skills could be reskilled into an AI-driven customer insights role, ensuring that the organization retains talent while meeting evolving business needs.
Focus on Durable Skills for Long-Term Resilience
In a world where roles are constantly evolving, durable skills—such as critical thinking, adaptability, and emotional intelligence—are becoming more important than ever. These skills are not only transferable across roles but also remain valuable even as AI technologies continue to advance. AI may automate many technical tasks, but it is unlikely to replace the human capacity for strategic decision-making, creativity, and leadership.
Example 3: Building Durable Skills for AI-Augmented Leadership
A tech company aiming to integrate AI into product development processes could focus on developing leadership skills among mid-level managers. By prioritizing durable skills like empathy, decision-making under uncertainty, and fostering innovation, the company equips its managers to lead AI-integrated teams effectively. While AI may streamline certain aspects of product development, leadership rooted in human-centric skills will continue to drive creativity and team cohesion.
A New Playbook: a positionless workforce positions you for long-term success
A 2020 study by research firm Linton Media found that 83% of NBA coaches said positionless basketball increased their team’s creativity on offense. I don’t want to overplay the parallels - I’d feel terrible if instead of figuring out what skills to focus on you took this as motivation to work on your three-point shot. But taking a cue from Steph, Draymond, and the boys, breaking free from traditional positional constraints - or roles - can promote a more fluid, versatile, adaptable, and ultimately more successful organization. Instead of focusing on rigidly defined roles that may quickly become obsolete, companies should prioritize the development of adaptable skills aligned with overarching business goals. Generative AI can then be leveraged to develop and acquire these skills, creating a workforce that is prepared for today’s challenges and can seize the opportunities that lie ahead.
AIX Files Poll
AI Gone Rogue
McDonald's is killing its AI drive-through experiment. Customers had reported a slew of AI ordering blunders: One posted video of the system incorrectly believing she'd ordered hundreds of dollars of chicken McNuggets. In another case, a customer was given an ice cream cone topped with bacon.
Disturbing AI-Generated Breaking Bad and Toy Story Mash-Up is a Childhood Crusher
AIX-emplary Links
(I think this is typically referred to as overconfidence) Workers have AI confidence — but no training to back it up, survey shows. On top of training concerns, leaders and workers also have vastly different perceptions of AI implementation in their organizations. Nearly two-thirds of leaders said AI is “fully implemented” across their organizations, while only 36% of workers agreed. Additionally, 60% of leaders say they are ahead of their competitors in AI maturity, while only 46% of workers agreed.
Can AI Do Performance Reviews? Rippling Says Yes. (FWIW, Josh Bersin agrees, with caveats.) Rippling introduced a product called Talent Signal. This product reads work output data from Github (for engineers), Salesforce and Gong (for salespeople), and Zendesk (for service reps) and uses AI to analyze their performance. It measures performance, behaviors, and evaluates performance based on company values.
Newsom Vetoes SB 1047. “This is indeed the key question. Do you target the future more capable frontier models that enable catastrophic and existential harm and require they be developed safely? Or do you let such systems be developed unsafely, and then put restrictions on what you tell people you can do with such systems, with no way to enforce that on users let alone on the systems themselves? I’ve explained over and over why it must be the first one, and focusing on the second is the path of madness that is bad for everyone. Yet here we are.”
AI begins its ominous split away from human thinking. But o1 is still primarily trained on human language. That's very different from truth – language is a crude and low-res representation of reality. Put it this way: you can describe a biscuit to me all day long, but I won't have tasted it.
So what happens when you stop describing the truth of the physical world, and let the AIs go and eat some biscuits? We'll soon begin to find out, because AIs embedded in robot bodies are now starting to build their own ground-up understanding of how the physical world works.
AI and the Human Mind: Uncovering the Machine "Unconscious."The creators of ChatGPT did not intend to mimic the functions of the human mind, yet ChatGPT does that.
Moral Turing test. When presented with responses to ethical questions, most people rated the answers from an AI program more favorably than those coming from fellow humans, suggests research in Scientific Reports.
About
The AIX Files is a weekly newsletter providing news, perspectives, predictions, and provocations on the challenges of navigating the world of AI-augmented work. It’s a big topic and there’s a lot to cover. Our goal with this, the AIX Factor, and the broader AIX community (in partnership with HR.com) is to promote - and, if necessary, provoke - illuminating conversations with a cross-section of business and technology leaders, as well as practitioners and people from diverse fields, on the ways AI intersects with leadership, culture, and learning.