Skills for the AI Workforce

𝗔𝗜 𝘀𝗸𝗶𝗹𝗹𝘀 𝗮𝗿𝗲𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗳𝗼𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀. 𝗧𝗵𝗲𝘆’𝗿𝗲 𝗳𝗼𝗿 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲. If your execs can’t articulate AI’s value — you’re stuck. If your experts can’t translate use cases — you’re stalled. If your employees don’t trust the tools — adoption fails. This is the AI literacy gap — and it’s killing transformation before it even begins. 𝗪𝗵𝘆 𝗔𝗜 𝗹𝗶𝘁𝗲𝗿𝗮𝗰𝘆 𝗺𝗮𝘁𝘁𝗲𝗿𝘀? It’s not just about new roles or flashy tools. It’s about enabling everyone to understand, trust, and challenge AI. Gartner calls AI literacy a major trend for 2026 — and here’s why: → It’s tied to regulation (like the EU AI Act) → It drives responsible, real-world adoption → It prevents the two biggest risks: blind trust and blind rejection The idea is simple: The more people understand AI, the better they use it. That includes non-technical teams too. AI literacy means: → Knowing where AI fails (hallucinations, misuse) → Navigating compliance, ethics, and governance → Cutting through hype to focus on business value Gartner’s framework breaks it down into four key level: 𝗟𝗲𝘃𝗲𝗹 𝟭 – 𝗡𝗼𝗻𝗲: → No clue how AI works. Still far too common. 𝗟𝗲𝘃𝗲𝗹 𝟮 – 𝗕𝗮𝘀𝗶𝗰: → Understands AI concepts. Can follow, not lead. 𝗟𝗲𝘃𝗲𝗹 𝟯 – 𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲: → Applies AI meaningfully in their work. The SME sweet spot. 𝗟𝗲𝘃𝗲𝗹 𝟰 – 𝗦𝘁𝗿𝗼𝗻𝗴: → Leads AI strategy. Evaluates trade-offs. Connects models to mission-critical goals. There’s no one-size-fits-all training when it comes to AI literacy. A tailored approach is essential. Technical teams need different training than executives or middle management. So what’s needed? Targeted upskilling — by role, by depth, by design. Because AI success isn’t just about smarter models. It’s about smarter people. 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E

When a company deploys an AI transformation, everyone fixates on the technology but here’s what is even more important. It's about the people. Over the years, I've developed a simple but powerful tool to evaluate teams for AI readiness. I call it my Will-Skill Matrix for AI! It’s taking a pre-existing model and customizing it for AI deployments based on 13 years of deployment experience. This framework is copyrighted: © 2025 Sol Rashidi. All rights reserved. 𝗛𝗶𝗴𝗵 𝗦𝗸𝗶𝗹𝗹, 𝗛𝗶𝗴𝗵 𝗪𝗶𝗹𝗹: These are your champions - they have the technical capabilities and the hunger to drive AI adoption forward. 𝗛𝗶𝗴𝗵 𝗦𝗸𝗶𝗹𝗹, 𝗟𝗼𝘄 𝗪𝗶𝗹𝗹: Often your most technically brilliant people who resist change. They've mastered existing systems and see AI as either a threat or unnecessary complexity. 𝗟𝗼𝘄 𝗦𝗸𝗶𝗹𝗹, 𝗛𝗶𝗴𝗵 𝗪𝗶𝗹𝗹: Your enthusiastic learners. They may not understand neural networks, but they're eager to embrace AI-driven solutions. 𝗟𝗼𝘄 𝗦𝗸𝗶𝗹𝗹, 𝗟𝗼𝘄 𝗪𝗶𝗹𝗹: These team members neither understand AI nor want to adapt to it. They're comfortable in their current roles and see no reason to change. Here's the counterintuitive insight most leaders miss: The "Low Skill, High Will" group is your hidden treasure in AI transformation. I discovered this at one of my employers during a massive data overhaul. My most valuable contributors weren't always the data scientists with impressive credentials. Often, they were business analysts who couldn't code complex algorithms but brought boundless curiosity and deep business knowledge and a will to figure it out. Why does this matter? Because AI implementation isn't just a technical challenge - it's fundamentally a human change management project. In one particularly tough transformation, I spent months trying to convince resistant technical experts to embrace new methods. Meanwhile, I overlooked enthusiastic business teams eager to learn and adapt. The breakthrough came when I finally shifted focus. By empowering the "High Will" groups and pairing them with technical mentors, our implementation timeline was shortened by nearly 40%. This completely changed my approach to building AI teams. The most successful AI implementations don't just depend on having the best algorithms or the most data engineers. They depend on having people throughout your organization who are willing to reimagine what's possible - and who bring others along with them.

Check out these AI Careers and the Skills You Need to Succeed AI is reshaping industries, but breaking into this field means knowing exactly which skills matter for each career path. When working in the AI space, you can choose to analyze data, build models, or design autonomous AI agents. Building an AI skill foundation makes all the difference. 🔹 Data Science: A data scientist blends math, programming, and experimentation. From machine learning algorithms and SQL to big data tools like Spark, the focus is on building predictive models, cleaning complex datasets, and deploying solutions that drive business impact. 🔹 Data Analytics: Data analysts transform raw information into actionable insights. Mastery of Excel, SQL, and data cleaning paired with dashboards (Power BI, Tableau) and data storytelling makes them vital for decision-making and trend analysis in organizations. 🔹 AI Engineering: AI engineers bridge research and production. They work with neural networks, deep learning frameworks (TensorFlow, PyTorch), and advanced fields like NLP, computer vision, and reinforcement learning. Their expertise extends to cloud AI services, pipelines, and scaling models for real-world applications. 🔹 Agentic AI: The newest career track, Agentic AI specialists design autonomous systems. Core skills include prompt engineering, role and agent design, context memory, multi-agent coordination, and tool/API integration. Using frameworks like LangChain and orchestration tools (Make, n8n, Zapier AI), they build AI agents that think, plan, and act. The takeaway you may ask: each AI career path may demands a unique toolkit, however they will most likely remain essential for the next wave of AI innovation. #AI #careers

McKinsey has 40,000 employees and 25,000 AI agents. Now it is adjusting remuneration to AI. An entire industry is being disrupted by AI. And it is not the only one. Less than 2 years ago McKinsey had just 3,000 AI agents. Its CEO originally expected to reach one AI agent per employee by 2030. Now it might be months away. 𝗕𝘂𝘁 𝘄𝗵𝗮𝘁 𝗱𝗼 𝗮𝗴𝗲𝗻𝘁𝘀 𝗱𝗼 𝗶𝗻 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗶𝗻𝗴? • Consulting is full of work that is structured, repeatable, research-heavy, and analysis-driven. Exactly the type AI can replace. • Agents can help consultants search internal knowledge, summarize documents, compare markets, draft first versions, structure analyses, test hypotheses, build models, prepare client materials, and accelerate the kind of linear problem-solving that used to consume large amounts of junior consultant time. This does not mean McKinsey no longer needs consultants. It means consulting is changing. If AI can produce the first draft, the benchmark, the synthesis, the model, or the analysis, humans have to become better at the parts AI cannot reliably do: • setting the right ambition • applying judgment • challenging answers • managing the client • connecting politics with strategy • turning analysis into decisions This is much bigger than automation. Consulting firms are now redesigning the economics of consulting around a new execution layer. 𝗟𝗲𝘁’𝘀 𝘁𝗮𝗸𝗲 𝗼𝗻𝗲 𝘀𝘁𝗲𝗽 𝗯𝗮𝗰𝗸. For decades, the consulting model was built around senior partners selling the work, large teams delivering it, and clients paying for expertise, time, and execution capacity. If now AI agents are doing an increasing part of this work, clients will ask why they should pay the same way for work that now takes less human effort. That means consulting firms need to adjust their business model: from selling hours and advice to selling outcomes. Savings, cost reduction, productivity improvement, revenue increase, real transformation. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗠𝗰𝗞𝗶𝗻𝘀𝗲𝘆 𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗻𝗼𝘄: Partners will receive a smaller share of profits in cash and a larger share in equity. In practice, part of the money that would have been paid out immediately stays inside the firm. 𝗪𝗵𝘆? • Because consulting cash flows may become more volatile. If more projects are tied to savings or performance improvements, the firm may only get fully paid once the client actually delivers the result. • McKinsey needs more capital inside the business: to absorb delayed payments, take more outcome risk, and invest in the technology needed to deliver work differently. Consulting companies are adopting 𝗼𝘂𝘁𝗰𝗼𝗺𝗲-𝗯𝗮𝘀𝗲𝗱 𝗽𝗿𝗶𝗰𝗶𝗻𝗴. Any industry built on expensive expert work, repeatable analysis, and billable hours will face the same pressure: to move from selling activity to selling outcomes. Opinions: my own, Graphic source: CB Insights Subscribe to my newsletter: https://lnkd.in/dkqhnxdg

🚀 𝐓𝐡𝐞 𝐍𝐞𝐱𝐭 𝐅𝐫𝐨𝐧𝐭𝐢𝐞𝐫 𝐨𝐟 𝐓𝐚𝐥𝐞𝐧𝐭 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: 𝐖𝐡𝐞𝐫𝐞 𝐀𝐈 𝐚𝐧𝐝 𝐇𝐮𝐦𝐚𝐧 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐂𝐨𝐧𝐯𝐞𝐫𝐠𝐞 If your organization is automating just to cut costs, you're missing the transformation—you're optimizing yesterday's work, not creating tomorrow's value. Across industries, leaders are racing to deploy AI, yet most are missing the real prize: value creation through talent amplification. 🔹 McKinsey estimates that generative AI could automate up to 30% of current tasks by 2030 — but history shows that automation rarely eliminates work; it reshapes it. 🔹 World Economic Forum data tells us that while 83 million roles may be displaced, 69 million new ones will emerge, demanding entirely new skill architectures. 🔹 Whether in Boston or Berlin, the pattern is clear: organizations that invest in workforce development alongside AI see significantly higher returns than those focused on technology alone. 🔹 And yet, only 24% of organizations have a concrete reskilling strategy aligned with their AI investments. That's the real gap. Not a "skills gap," but a 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐢𝐦𝐚𝐠𝐢𝐧𝐚𝐭𝐢𝐨𝐧 𝐠𝐚𝐩 — where technology races ahead and people strategy lags behind. The future winners will not be those who automate fastest, but those who redeploy, reskill, and reinvent talent as quickly as they retool technology. 𝐁𝐞𝐜𝐚𝐮𝐬𝐞 𝐞𝐯𝐞𝐫𝐲 𝐟𝐫𝐞𝐞𝐝 𝐡𝐨𝐮𝐫 𝐟𝐫𝐨𝐦 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐚𝐧 𝐨𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲 𝐭𝐨:  ✅ Reallocate human capacity to innovation and customer impact ✅ Build adaptive skills in data literacy, problem-solving, and leadership ✅ Rewire learning systems to evolve at the speed of AI 𝐅𝐢𝐧𝐚𝐥 𝐰𝐨𝐫𝐝: AI provides the capability. Your people determine how that capability creates value. The organizations that make AI and human capability equal partners in growth will turn the "AI vs. talent" paradox into their unmatched business advantage. #FutureOfWork #TalentStrategy #AITransformation #WorkforceDevelopment #CEO #CHRO #TalentLeadership #StrategicHR #LeadershipDevelopment #BusinessTransformation #GenerativeAI #PeopleStrategy

I've done 127 AI readiness assessments in the past two years. Only three actually measured what matters. The others focused on beautiful dashboards. Impressive tech scores. Data cleanliness metrics. Automation percentages. All the wrong things. They miss the critical factor. Whether your team trusts this is happening for them, not to them. A healthcare company with ninety million in revenue had a perfect readiness score on paper last quarter. Clean data. Solid infrastructure. Two successful pilots. Six months after rollout, adoption sat at nine percent. I asked the operations manager what happened. She said nobody explained why they were doing this. Just that they had to. A manufacturing client I'm working with now has messy data. Their systems aren't integrated. But their teams know exactly what problems the AI is solving for them. Ninety days in, sixty-eight percent usage rate. The difference isn't the technology. It's whether you asked your people what they actually need before you started building. Most companies treat AI readiness like a technical assessment. Infrastructure check. Data quality check. Security protocols check. They're auditing the wrong thing. AI readiness isn't a tech audit. It's a trust audit. #AIReadiness #AIAdoption #DigitalTransformation #FutureOfWork #HumanCenteredAI #ChangeManagement #AIBusiness #TrustInTech #AICulture #LeadershipInAI

Everyone’s talking about AI adoption. But not enough are asking: “Are people actually ready to use it—well?” 72% of employees now say they’re using AI regularly. But when you zoom in on the frontline? Just 51%. That's what BCG calls the "Silicon Ceiling"— Interest is high, tools exist… But value? Still stuck at the surface. Here’s why AI often flops after launch: 🧱 36% feel untrained 🧰 37% don’t have the right tools 🧭 Only 25% feel supported by leadership What happens next? Over half turn to unauthorized AI tools (54%) to get their work done. That’s a compliance nightmare waiting to happen. What actually works? ✅ Coaching. ✅ Real training. ✅ Leadership that leads. Just 5 hours of focused support drives real ROI: 📈 +19pp in AI adoption 📈 +14pp with access to a coach 📈 +12pp from in-person learning And the biggest unlock? When leaders actively back AI: Confidence jumps from 15% → 55% If you're a leader, this is your roadmap: 1. Measure what matters – Track AI impact on productivity, quality, satisfaction. 2. Don’t DIY your training – Build formal pathways, not one-off workshops. 3. Support your people – Tools are useless without workflows and skills. 4. Experiment fast, fail safe – A/B test AI agents to learn quicker, with less risk. Here’s the real truth: AI won’t transform your business. Your people will. But only if they’re trained, guided, and empowered. If you’re done chasing AI hype and ready to build real capability— Let’s talk. 👉 This post is inspired by recent insights from BCG, June 2025. ♻️ Share this with your network if it resonates. ☝️ And follow Stuart Andrews for more insights like this.

Every customer and government leader I meet is asking, “How can we make AI a force for good for our people, and not a threat?” 92% of jobs are expected to undergo some level of transformation due to advancements in AI. The work begins with identifying and enabling the new skills and training needed for AI preparedness. That’s why I’m honored to share the insights from the AI-Enabled ICT Workforce Consortium's inaugural report, “The Transformational Opportunity of AI on ICT Jobs.” This report examines the impact of AI on 47 ICT job roles and offers tailored training recommendations. It's a unique guide to the skills needed for the AI future, with recommendations that couldn't be clearer, timelier, or more urgent. Here are some of the top takeaways: - 92% of ICT jobs will undergo high or moderate transformation due to AI. - 40% of mid-level and 37% of entry-level ICT positions will see high levels of transformation. - Skills like AI ethics, responsible AI, prompt engineering, and AI literacy will become crucial. - Foundational skills such as AI literacy and data analytics are essential across all ICT roles. Read the full report here: https://lnkd.in/gWfPc8WT The risks associated with an under-skilled, unprepared workforce are global in scale, ranging from economic wage gaps to trade imbalances, technological stagnation, social and ethical issues, and national security threats. This creates a pressing need for a coordinated effort to reskill and upskill employees around the world. By investing in a long-term roadmap for an inclusive and skilled workforce, we can help all populations participate and thrive in the era of AI. Led by Cisco and joined by industry giants like Accenture, Eightfold, Google, IBM, Indeed, Intel Corporation, Microsoft, and SAP the Consortium will train and upskill 95 million people over the next 10 years through their individual organizations' commitments.

AI field note: Reducing the 'mean time to ah-ha' (MTtAh) is critical for driving AI adoption—and unlocking the value. When it comes to AI adoption, there's a crucial milestone: the "ah-ha moment." It's that instant of realization when someone stops seeing AI as just a smarter search tool and starts recognizing it as a reasoning and integration engine—a fundamentally new way of solving problems, driving innovation, and collaborating with technology. For me, that moment came when I saw an AI system not just write code but also deploy it, identify errors, and fix them automatically. In that instant, I realized AI wasn’t just about automation or insights—it was about partnership. A dynamic, reasoning collaborator capable of understanding, iterating, and executing alongside us. But these "ah-ha moments" don’t happen by accident. Systems like ChatGPT or Claude excel at enabling breakthroughs, but it really requires us to ask the right questions. That creates a chicken-and-egg problem: until users see what’s possible, they struggle to imagine what else is possible. So how do we help people get hands-on with AI, especially in enterprise organizations, without relying on traditional training? Here are some approaches we have tried at PwC: 🤖 AI "Hackathons" or Challenges: Host short, low-stakes events where employees can experiment with AI on real problems. For example, marketing teams could test AI for campaign ideas, while operations teams explore process automation. ⚙️ Sandbox Environments: Provide low-friction, risk-aware access to AI tools within a dedicated environment. Let users explore capabilities like text generation, workflow automation, or analytics without worrying about “messing something up.” 🚀 Pre-built Use Cases: Offer ready-to-use templates for specific challenges, such as drafting a client email, summarizing documents, or automating routine reports. Seeing results in action builds confidence and sparks creativity. At PwC we have a community prompt library available to everyone, making it easier to get started. 🧩 Embedded AI Mentors: Assign "AI champions" who can guide teams on applying AI in their work. This informal mentorship encourages experimentation without formal, structured training. We do this at PwC and it's been huge. ⚡️ Integrate AI into Existing Tools: Embed AI into everyday platforms (like email, collaboration tools, or CRM systems) so users can naturally interact with it during routine workflows. Familiarity leads to discovery. Reducing the mean time to ah-ha—the time it takes someone to have that transformative realization—is critical. While starting with familiar use cases lowers the barrier to entry, the real shift happens when users experience AI’s deeper capabilities firsthand.

🔮 AI Accessibility Design Patterns. With practical guidelines for designers to keep in made to make AI experiences more accessible and inclusive ↓ AI features are rarely accessible by default. As we rush to ship AI-powered products, most of the time AI interactions are barely usable nor accessible or inclusive. Too often with open-ended input ("ask-me-anything"), poorly structured output and plenty of slow, repetitive and inefficient tasks. Writing prompts well is hard and time-consuming. Navigating within AI-generated wall of text is difficult. Finding relevant bits in long-lasting conversations is an adventure. And tweaking queries and AI output to meet user's needs and expectations is remarkably painful. These aren’t attributes of great AI experiences. In fact, AI features have a lot of UX challenges which require intentional and deliberate UX work: 1. AI suddenly imagines things 2. AI silently assumes things 3. AI suddenly forgets things 4. AI suddenly changes its mind 5. AI says what people want to hear 6. AI often takes too long to reply 7. AI is too verbose when replying 8. Quality of AI output declines over time 9. Only amplifies averages and mistakes 10. Rarely asks for missing details or context On the other hand, the accessibility of AI products is uncharted territory. AI features typically come with a lot of accessibility challenges, and usually they aren’t addressed at all: 1. Users could use a task builder for better prompts 2. Add “Skip to chat” or “Skip to last reply” links 3. Keyboard navigation works bottom up (Shift + Tab) 4. Group interaction controls to reduce tabbing 5. As AI is busy, keep buttons enabled, show hints 6. Repetitive “busy” messages for screen reader users 7. Add navigation landmarks to navigate within AI responses 8. Highlight what's AI-generated and what isn't 9. Link references to relevant fragments, not pages 10. References should show up on tap/click, not hover. 11. Allow users to to adjust the verbosity of AI output. 12. Most charts and visuals don't have proper alt texts. In fact, "Ask-me-anything" is an incredibly poor design pattern in AI interfaces. Users can ask anything, but they never know what exactly to ask — and more specifically, how to articulate it efficiently. A task builder can help bring structure around AI input, along with higher speed and accuracy (attached). One thing to note is the "inverted navigation nightmare". Chat moves down the page, but keyboard navigation works from bottom up. And on the way to the conversation, there are always UI controls that aren’t easy to skip. Grouping all UI controls and allowing users to skip them at once would help. If you'd like to dive deeper, I can wholeheartedly recommend a series of articles by Michael Gower — https://lnkd.in/eQNCHf7M — an important yet often overlooked area that deserves attention and good UX work, but is unexplored yet.