Future Job Role Analytics

For the longest time we've had two main options to help people perform: upskilling or performance support. Just-in-case vs just-in-time. Push vs pull. With AI, we now have a third - enablement. It's different from what we've had before: 𝐔𝐩𝐬𝐤𝐢𝐥𝐥𝐢𝐧𝐠 ("teach me") - commonly done through hands-on learning with feedback and reflection, such as scenario simulations, in-person role-plays, facilitated discussions, building and problem-solving. None of that has become less relevant, but AI has enabled scale through AI-enabled role-plays, coaching, and other avenues for personalised feedback. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 ("help me") - support in the flow of work, previously often in the format of short how-to resources located in convenient places. AI has elevated that in at least two ways: through knowledge management, which helps retrieve the necessary, contextualised information in the workflow; and general & specialised copilots that enhance the speed and, arguably, the expertise of the employee. Yet, 𝐞𝐧𝐚𝐛𝐥𝐞𝐦𝐞𝐧𝐭 (‘do it for me’) is different – it takes the task off your plate entirely. We’ve seen hints of it with automations, but the text and analysis capabilities of genAI mean that increasingly 'skilled' tasks are now up for grabs. Case in point: where written communication was once a skill to be learned, email and report writing are now increasingly being handed off to AI. No skill required (for better or worse) – AI does it for you. But here's a plot twist: a lot of that enablement happens outside of L&D tech. It may happen in sales or design software, or even your general-purpose enterprise AI. All of which points to a bigger shift: roles, tasks, and ways of working are changing – and L&D must tune into how work is being reimagined to adapt alongside it. Nodes #GenAI #Learning #Talent #FutureOfWork #AIAdoption

A lot of data teams are having the same quiet thought right now. 💭 “If AI can write the SQL and build the dashboard… what happens to us?” 😳 It’s a fair question. The last decade of data work has been dominated by repetitive tasks: writing SQL, maintaining pipelines, cleaning data, keeping dashboards alive. Those are exactly the tasks AI is starting to automate. Early-career data work is already being reshaped. So yes, some familiar roles will become less central. But here’s the paradox: 𝘋𝘢𝘵𝘢 𝘱𝘦𝘰𝘱𝘭𝘦 𝘮𝘢𝘺 𝘣𝘦 𝘵𝘩𝘦 𝘣𝘦𝘴𝘵 𝘱𝘰𝘴𝘪𝘵𝘪𝘰𝘯𝘦𝘥 𝘨𝘳𝘰𝘶𝘱 𝘪𝘯 𝘵𝘩𝘦 𝘦𝘯𝘵𝘦𝘳𝘱𝘳𝘪𝘴𝘦 𝘵𝘰 𝘵𝘩𝘳𝘪𝘷𝘦 𝘪𝘯 𝘵𝘩𝘦 𝘈𝘐 𝘦𝘳𝘢. 💪 Why? Because data work has never been deterministic. Software engineering ships when things behave the same way every time. Data work doesn’t. Two analysts can look at the same dataset and reach different conclusions. Ambiguity and iteration are the job, not failure modes. LLMs behave much more like analysts than like software. They’re inherently non-deterministic, and getting them to answer reliably requires training, semantic guidance, careful prompting, and iterative refinement. That kind of work is second nature to strong data practitioners — but foreign to many traditional engineering and business workflows. It also explains why so many organizations are stuck. In our survey, 31% cited lack of AI talent and 23% cited unclear ownership as blockers to scaling AI. 🔮 Prediction #6️⃣ for 2026 🔮 Some roles in data will naturally become obsolete but the natural strength of data people in navigating non-determinism will make them some of the most valuable people in the company. What will change is how those roles show up: ➡️ Analysts evolving into analytics context engineers ➡️ Engineers evolving into data & AI engineers ➡️ Platform teams taking on new work across context, unstructured data, lineage, and observability How do you see data roles evolving in your own organization?

Recent workforce data shows that over 70 % of employers expect professionals to be proficient with digital collaboration tools and AI enabled workflows by 2026, and that expectation is already reshaping how roles are defined. This is why most companies are now asking for augmented profiles. It is not about replacing roles, but evolving them. Take tech project management for example. The expectation is shifting from a traditional role toward an augmented professional who knows how to work alongside AI, adapts quickly, and operates effectively in faster paced environments where AI supports planning, analysis, and execution. The same evolution is happening across many roles. This shift is not limited to one function. It applies across profiles, from testers to developers, product leads, designers, and domain experts who can critically challenge AI outputs rather than simply accept them. While this wave is hitting technology roles first, it will rapidly extend to functional roles and expert professions, becoming embedded across industries and job types. As this transition accelerates, having corporate licenses for tools like Gemini or Claude is becoming essential. Organization level licenses provide significantly better security, governance, and data control than personal accounts, while enabling teams to collaborate with confidence. Still, access alone is not what creates value. The real advantage comes from using these tools securely, optimizing token usage, standardizing outcomes, and maintaining consistently high quality results. It is about disciplined and intentional AI usage, not just availability. Another recurring gap is that many professionals are experimenting with AI without clearly understanding when to use a simple prompt, when to rely on a structured skill, or when an agent approach is more appropriate. Knowing how to navigate these modes is what turns AI from a convenience into a true productivity multiplier. This evolution is a race for relevance, but not in a discouraging sense. On the contrary, the sooner we choose to embrace this shift, the faster we build meaningful, future ready skills and strengthen our professional competitiveness. Those who actively learn to work with AI will not only stay relevant, they will help define the standards of the next generation of work. #ai #skills #tech #jobs

🎙 New episode of FP&A Today 🎙 Super fired up about this episode on a breakthrough research paper that showed LLMs outperformed human analysts at predicting future earnings. I had the pleasure of welcoming Alex Kim, a PhD student at the University of Chicago, to discuss his research on financial statement analysis using large language models (LLMs), and the surprising results from this study. Key points from our discussion: 🤖 AI Performance: LLMs achieved 60% accuracy in predicting earnings direction, compared to 53-57% for human analysts. 👩💼 Complementary Abilities: AI and human analysts offer different strengths that can be combined effectively. 👨💻 Evolving Roles: As AI capabilities expand, finance professionals should focus on areas where human expertise remains valuable. 💻 Practical Applications: We explored how finance teams can incorporate LLMs and machine learning models into their processes. 📑 Ongoing Development: Keeping up with AI advancements in finance is increasingly important for professionals in our field. (Link to the research paper in the comments.) This episode offers insights into the growing role of AI in finance and how it may shape our industry's future. https://lnkd.in/g-EVa2yC

Transforming Recruitment with AI: Unprecedented Efficiency and Fairness! In today's fast-paced talent landscape, integrating Artificial Intelligence (AI) into recruitment processes is not just an innovation—it's a necessity. Here's how AI is revolutionising talent acquisition: 1. Accelerated Hiring Processes AI streamlines candidate sourcing and screening, drastically reducing time-to-hire. Chipotle Mexican Grill Success: By implementing the AI program "Ava Cado," Chipotle increased application completion rates from 50% to 85% and slashed onboarding time from 12 days to just 4. Recruiter Efficiency: Recruiters save an average of 4.5 hours per week using AI tools. 2. Significant Cost Reductions AI-driven automation cuts operational expenses associated with hiring. Global Impact: Organizations have reported up to a 30% reduction in hiring costs per candidate through AI automation. Regional Savings: In North America, AI adoption led to a 40% reduction in recruitment costs, with Europe closely following at 36%. 3. Enhanced Productivity and Revenue AI not only streamlines processes but also boosts overall productivity. Revenue Growth: Companies utilizing AI in recruitment have seen a 4% increase in revenue per employee. Market Expansion: The AI recruitment industry is projected to reach a market size of $942.3 million by 2030, reflecting its growing influence. 4. Mitigating Bias and Promoting Fairness AI aids in creating a more equitable hiring landscape. Bias Reduction: 43% of hiring decision-makers believe AI helps eliminate human biases in recruitment. Inclusive Hiring: AI-driven platforms are designed to ensure equitable treatment of candidates, regardless of race, gender, or ethnicity. Embracing AI in recruitment is not merely a technological upgrade; it's a strategic move towards a more efficient, cost-effective, and fair hiring process. The data speaks for itself—AI is the future of talent acquisition. Are you adopting? #AIRecruitment #TalentAcquisition #HRTech #FutureOfWork #RecruitmentInnovation

Talent Acquisition Metrics and Analytics!! Talent acquisition metrics and analytics are essential tools for optimizing and improving the recruitment process. By analyzing data, talent acquisition teams can make more informed decisions, enhance recruitment strategies, and ultimately attract and hire the best talent. Here are some Key Metrics in Talent Acquisition to consider when discussing talent acquisition analytics: ▶️ Time to Fill: Measures the time from posting a job to making an offer. Shortening this time improves efficiency and reduces hiring costs. ▶️ Time to Hire: The time taken from the initial interview to the candidate’s acceptance. A shorter time indicates a smooth hiring process. ▶️ Cost Per Hire (CPH): The total cost involved in hiring, including advertising, recruiter fees, and onboarding expenses. Tracking CPH helps manage recruitment budgets. ▶️ Offer Acceptance Rate: The percentage of candidates who accept job offers. A low rate could indicate issues with compensation or cultural fit. ▶️ Quality of Hire: Measures the performance and retention of new hires, typically assessed through performance reviews and turnover rates. ▶️ Candidate Experience: Involves metrics like satisfaction scores and response time, which impact employer branding and can affect future candidate engagement. ▶️ Diversity Metrics: Tracks the diversity of applicants and hires, including gender, ethnicity, and other factors, to ensure fair and inclusive hiring practices. ▶️ Recruitment Funnel Analytics: Analyzes conversion rates between stages of recruitment, like from application to interview or interview to offer. Identifies where candidates drop off and allows for process optimization. ▶️ Predictive Analytics: Uses historical data to forecast hiring needs, job performance, and candidate success, helping to make more proactive recruitment decisions. ▶️ ROI of Talent Acquisition: Measures the return on investment of recruitment activities by comparing recruitment costs to the value brought by new hires (e.g., performance, retention). Benefits of Analytics in Talent Acquisition: ▶️ Improved Decision-Making: Data-driven insights help recruiters make more informed choices about candidates, processes, and strategies. ▶️ Process Optimization: Analytics help identify bottlenecks, inefficiencies, and areas for improvement in the recruitment workflow. ▶️ Better Candidate Fit: By tracking metrics like quality of hire and predictive analytics, recruiters can identify candidates who are likely to succeed and stay with the company long-term. ▶️ Enhanced Employer Branding: A positive candidate experience, measured through feedback and response times, enhances the organization’s reputation as an employer of choice. By tracking these metrics and leveraging analytics, talent acquisition teams can refine their recruitment processes, improve candidate experiences, and ultimately make better hires.

BREAKING: Massachusetts Institute of Technology new “labor twin” shows that AI can already perform up to 12% of U.S. work tasks, influencing more than $1.2 trillion in wages across finance, healthcare and professional services. I pay close attention to work like this because it reveals something leaders often miss. The future of work isn’t shaped by job titles. It’s shaped by tasks—and MIT’s model shows that some roles already have 40–60% of their tasks exposed to today’s AI systems. This aligns with how I define the AI Learning Economy. It’s an environment where work, skills and value creation change faster than traditional systems can adapt. The advantage goes to people and organizations that learn quickly, innovate continuously and move at the speed of the technology that’s shaping their world. I see this clearly inside the companies and institutions I work with. When leaders stop debating job replacement and start mapping tasks, the entire picture shifts. AI removes friction. People shift into higher-value work. Teams innovate because routine barriers disappear. Work becomes more dynamic, not more fragile. The real challenge isn’t AI. It’s whether leaders are willing to adjust their systems, roles and expectations to match the pace of the AI Learning Economy. Organizations built for stability struggle. Organizations built for learning and innovation accelerate. This is the moment to redesign how your teams learn, adapt and innovate. Adjust roles around task exposure. Build the skills people need next. And create an environment where continuous learning and innovation are part of how work actually gets done. https://lnkd.in/gszN4rW7

Data-Driven Hiring: How to Use Analytics to Find the Best Talent The talent market is fast-moving, and guesswork no longer cuts it. Hiring decisions should be driven by data and analytics. Here’s how you can make smarter talent acquisition choices using analytics: 1. Identify Hiring Gaps: Use workforce analytics to identify skills gaps in your current team. What’s missing, and how can you fill that with your next hire? This targeted approach ensures you’re not just filling seats, but addressing actual needs. 2. Track Candidate Sources: Which platforms are bringing in the best candidates? Whether it’s LinkedIn, referrals, or job boards, tracking these metrics helps you refine your sourcing strategy. 3. Predictive Analytics for Retention: Don’t just focus on hiring - use data to predict retention trends. Which types of hires have the longest tenure? Use these insights to adjust your recruitment strategies and keep top talent longer. #DataDrivenRecruitment #HRAnalytics #TalentAcquisition #HiringTrends #HRTech #CareerAdvice #ShivaniTiwari LinkedIn

Gartner's analysis highlights a significant shift in AI-related roles, moving from traditional technical roles to a more specialized and cross-functional structure. Key areas include the rise of emerging roles like prompt engineers, AI ethicists, and decision engineers, alongside established roles needing to adapt to new demands. This evolution also emphasizes the importance of AI fluency across various business functions and the need for strategic, ethical, and user-oriented skills in AI development. Here's a breakdown of the key aspects: Established AI Roles: • AI Developer: Builds and refines AI models. • Data Scientist: Analyzes data to derive insights using AI. • ML Engineer: Bridges the gap between machine learning models and practical applications. • Data Engineer: Focuses on building and managing data pipelines. Emerging AI Roles: • Prompt Engineer: Masters the art of crafting effective prompts to elicit desired responses from AI models.  • Model Validator: Ensures the quality and reliability of AI models.  • AI Ethicist: Addresses ethical concerns related to AI bias, fairness, and responsible development.  • Decision Engineer: Optimizes AI-driven decision-making processes.  • AI Architect: Designs the overall structure and architecture of AI systems, ensuring scalability and security.  • AI Product Manager: Integrates AI into products and services to maximize business impact.  • AI Risk & Governance Specialist: Focuses on the ethical and responsible deployment of AI.  • Data & Analytics Translator: Bridges the gap between technical AI teams and business stakeholders.  • Knowledge Engineer: Structures AI knowledge bases for enhanced reasoning. Key Takeaways: • Specialization is key: AI expertise is no longer limited to a few core roles. Specialized roles like prompt engineers and AI ethicists are becoming crucial. • Cross-functional collaboration: AI development requires a collaborative approach, with various roles working together to ensure successful deployment and adaptation. • Ethical considerations: AI ethics is becoming a critical area of focus, requiring dedicated roles to address potential biases and ensure responsible development. • Adaptability is essential: Both established and emerging roles need to adapt to the evolving landscape of AI and its applications. • AI fluency across the organization: Organizations need to foster AI fluency across various departments, not just within specialized teams, to maximize the benefits of AI.

LLMs force a shift from performing work to specifying work. Because the machine only responds to what you articulate, you end up writing constantly: intent, constraints, context, expected output. The act is functional rather than literary. You strip away tone, polish and performance. What remains is the core of knowledge work: clarifying the problem, defining the goal, summoning the factual context. The immediacy of feedback is captivating. You see instantly how a clearer description yields a better result. Over time this trains the fundamental skills of high-leverage white-collar work: documenting ideas, setting objectives, defining deliverables, and enriching the brief with the right context. A generation now growing up with this muscle memory will expect work to run this way. Their default workflow will be: capture the idea in writing, specify the task, let the machine execute or assist. Meetings without agendas, projects without specs, assignments without context will feel amateurish. The norms of today’s office—already bloated by vague communication and performative collaboration—will look as dated as the mid-century rituals of secretaries typing dictated letters and executives spending afternoons in performative chatter over drinks. The shift is not that AI replaces office work; it rewires the discipline of doing it.