Tips for Analysts to Adapt to AI Changes

Here’s the line no one wants to say out loud, especially in consulting and analytics circles: AI is already better at collecting and summarising data than most analysts. Not opinion, just fact. AI scrapes reports, processes datasets, and outputs coherent summaries in seconds. Which means the future won’t belong to analysts who produce information, it will belong to analysts who produce insight. 𝗗𝗮𝘁𝗮 𝗚𝗮𝘁𝗵𝗲𝗿𝗲𝗿𝘀 𝘃𝘀. 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 𝗧𝗵𝗶𝗻𝗸𝗲𝗿𝘀 Data gatherers: Pull facts, Organise tables, Summarise trends AI does that faster. Pattern thinkers: Spot discontinuities, Connect dots others miss, Predict what happens next AI can generate outputs, humans must interpret implications. 𝗦𝘂𝗺𝗺𝗮𝗿𝗶𝘀𝗲𝗿𝘀 𝘃𝘀. 𝗦𝘆𝗻𝘁𝗵𝗲𝘀𝗶𝘇𝗲𝗿𝘀 AI summarises beautifully. It compresses, It rephrases, It repackages. But synthesis? That’s different. Good synthesis answers: 👉 “So what does this mean for our business?” 👉 “Where will this break first?” 👉 “What decision does this enable?” Summaries inform. Synthesis influences. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝘃𝘀. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗙𝗿𝗮𝗺𝗲𝗿𝘀 Everyone’s learning to write prompts. Few are learning to define problems. This matters because: AI answers the question you ask, not the question you should have asked. Problem framers don’t just seek answers, they formulate the right questions. That’s the rare skill companies will pay for. 📍𝐇𝐞𝐫𝐞’𝐬 𝐭𝐡𝐞 𝐁𝐢𝐠 𝐒𝐡𝐢𝐟𝐭 𝐘𝐨𝐮’𝐥𝐥 𝐒𝐞𝐞 𝐓𝐡𝐢𝐬 𝐘𝐞𝐚𝐫 The analysts who thrive post-AI will be the ones who: ✔ ask better questions ✔ think multiple moves ahead ✔ integrate context, human behaviour, politics, incentives ✔ challenge assumptions before reporting data AI won’t replace analysts who think. It will replace analysts who don’t. Here’s what I want to know: In your experience, what’s one thinking skill that AI can’t replicate, but makes all the difference in analysis and strategy? 👇 Drop it below. #AI #Analytics #Consulting #FutureOfWork #Strategy #DataScience #Leadership #DecisionMaking

𝐇𝐨𝐰 𝐝𝐨 𝐈 𝐬𝐭𝐚𝐲 𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐢𝐧 𝐭𝐡𝐞 𝐀𝐈 𝐞𝐫𝐚? The question I keep getting from professionals across every function — engineering, marketing, finance, operations: "What should I be doing right now to enhance my chances of keeping and flourishing in my job?" Having watched this shift play out across our portfolio companies, here is how I think about it. 𝐁𝐮𝐭 𝐟𝐢𝐫𝐬𝐭, 𝐨𝐧𝐞 𝐡𝐚𝐫𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧. Before you re-skill, ask whether the company you work for has a future in the AI era. If your company's core product is being replaced by AI — not enhanced, not contested, but replaced — reskilling inside that company may not be enough. Getting out early is not disloyalty. It is career survival. Assuming you are in the right place — three things, in order. 𝐒𝐡𝐢𝐟𝐭 𝐟𝐫𝐨𝐦 𝐞𝐱𝐞𝐜𝐮𝐭𝐨𝐫 𝐭𝐨 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐨𝐫. Your value is no longer in doing the work — it is in knowing what work to do, why, and whether the output is right. The person who can break a problem down, delegate to AI, and judge the result is more valuable than the person who can execute a single step perfectly. This is a fundamental shift in identity — from "I am good at X" to "I know when X is done well." 𝐁𝐮𝐢𝐥𝐝 𝐀𝐈 𝐟𝐥𝐮𝐞𝐧𝐜𝐲 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐝𝐚𝐢𝐥𝐲 𝐮𝐬𝐞, 𝐧𝐨𝐭 𝐜𝐨𝐮𝐫𝐬𝐞𝐬. Stop taking "AI for professionals" courses. Start using AI tools in your actual work, every day. Draft with it, analyze with it, review with it. Fluency comes from repetition, not theory. The people pulling ahead are the ones who integrated AI into their daily workflow six months ago. 𝐃𝐞𝐞𝐩𝐞𝐧 𝐲𝐨𝐮𝐫 𝐝𝐨𝐦𝐚𝐢𝐧, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐲𝐨𝐮𝐫 𝐭𝐨𝐨𝐥𝐬. AI commoditizes execution. What it cannot replicate is your understanding of why things work the way they do in your industry — the exceptions, the judgment calls, the context. When you can see the full picture of how outcomes are produced, you start thinking in terms of improving those outcomes, decreasing cycle times, and removing friction. That is where AI becomes a force multiplier — not on isolated tasks, but across workflows. 𝐈𝐌𝐏𝐎𝐑𝐓𝐀𝐍𝐓: Ask the hard question about your company first. Then shift your mindset from executor to orchestrator. Build AI fluency through daily use, not courses. And deepen the domain expertise that no model can replace. The window to build these habits is now — not next year. What has worked for you in re-skilling for AI? Would love to hear.

If you're a data analyst whose main toolkit consists solely of Power BI, it might be time to ponder your next career move. With the advent of Microsoft Copilot for Power BI, along with Copilots in Microsoft Fabric and across the Microsoft platform, we are standing on the brink of a significant shift in data analytics. These powerful new tools are set to reshape the landscape, enhancing productivity and reducing the routine tasks that many analysts handle today. Microsoft Copilot for Power BI integrates cutting-edge AI capabilities, allowing advanced users like myself to perform complex analyses and generate insights at an unprecedented pace and scale. The implication? A potential decrease in demand for traditional data analysis roles as these tools begin to partially automate what was once manual, time-consuming work. For those looking to stay ahead, transitioning to data engineering could be a wise strategy. Data engineering focuses on the architecture and infrastructure for data generation, collection, and analysis systems—the foundations on which tools like Power BI operate. As automation in the front end increases, the demand for robust data systems and architecture is likely to grow and there is already a shortage of data engineers. Here are some tips for making the transition: ✨ Deepen your programming skills – Python and SQL are essential languages in the data engineering space. ✨Understand big data technologies – Familiarize yourself with platforms like Spark and Kusto and Event Hubs. ✨Learn about all the cloud services – Gain proficiency in other cloud platforms such as AWS, Microsoft Azure, or Google Cloud, which are integral to modern data solutions. ✨Develop a solid grasp of data modelling and ETL processes using notebooks and medallion architecture – These skills are critical as they deal with how data is extracted, transformed, and loaded. ✨Engage with the community and continue learning – Follow leading data engineering blogs, attend webinars, and participate in forums to stay updated on the latest trends and technologies. As we navigate these changes, staying adaptable and continually enhancing our skills will be crucial in remaining relevant and competitive in the evolving job market. How are you preparing for the integration of AI tools like Microsoft Copilot into your data analytics workflows? What skills are you focusing on to stay ahead in your career? Follow me for updates on the following topics and look out for my weekly Lunch 'n' Learn sessions. #DataAnalytics #MicrosoftCopilot #PowerBI #DataEngineering #CareerAdvice #ArtificialIntelligence #FutureOfWork #TechTrends

We have a fundamental shift we need to make on many projects. The "requirements phase" needs to be thought about differently. ❓How about no requirements phrase? 😱 There I said it! Before you react, let me explain. Of course I want us to do analysis, 10000%. Of course the #BusinessAnalyst role is important, and we need to THINK about WHAT we are building and WHY. However, I think for many teams, projects, and products, a "requirements phase" is outdated. A development team waiting for a formal hand-off of an approved spec, BRD, or PRD.  We need to work in parallel, leverage AI for drafting, AND rapid prototyping, and learn more along the way, rather than the old focus of getting the perfect spec input. Now, I am all for a really good spec input as input to AI Coding Agents. But this spec looks different than a spec you may have created before. Thanks to AI tools, developers can move incredibly fast. Being present and available when they need clarity is far more valuable than a document or JIRA story handed over at the start of a sprint. What does working in parallel actually look like? • Rapid prototype with AI together with business stakeholders. • Sit with your developers. Literally. Be present during the build to answer questions the AI Coding Agents have about context and user scenarios. • Use AI to help answer questions in real time, rather than relying on a document written weeks ago. • While development is moving, work with stakeholders on the decisions that need to be made next. • Work on incremental planning, prioritizing pieces rather than a large scope. • Let the work surface the questions. You don't have to anticipate everything upfront. Requirements still happen. Analysis still happens. It just happens alongside development, woven into the work as it unfolds. This is totally possible. I've seen it work. It requires trust, presence, sometimes funding and resource planning changes, and a willingness to let go of the document as the primary hand-off and source of development input. The new value is the clarity you create and decisions you help make. What's your experience working in parallel with development? Let's talk about it. 👇 Hi - I'm Angela - I help BAs and BA Teams learn and grow! Follow me here on Linkedin, Watch my courses on Linkedin Learning, and join my community on BA-Cube: https://ba-cube.mn.co #businessanalysis

In a world where access to powerful AI is increasingly democratized, the differentiator won’t be who has AI, but who knows how to direct it. The ability to ask the right question, frame the contextual scenario, or steer the AI in a nuanced direction is a critical skill that’s strategic, creative, and ironically human. My engineering education taught me to optimize systems with known variables and predictable theorems. But working with AI requires a fundamentally different cognitive skill: optimizing for unknown possibilities. We're not just giving instructions anymore; we're co-creating with an intelligence that can unlock potential. What separates AI power users from everyone else is they've learned to think in questions they've never asked before. Most people use AI like a better search engine or a faster typist. They ask for what they already know they want. But the real leverage comes from using AI to challenge your assumptions, synthesize across domains you'd never connect, and surface insights that weren't on your original agenda. Consider the difference between these approaches: - "Write a marketing plan for our product" (optimization for known variables) - "I'm seeing unexpected churn in our enterprise segment. Act as a customer success strategist, behavioral economist, and product analyst. What are three non-obvious reasons this might be happening that our internal team would miss?" (optimization for unknown possibilities) The second approach doesn't just get you better output, it gets you output that can shift your entire strategic direction. AI needs inputs that are specific and not vague, provide context, guide output formats, and expand our thinking. This isn't just about prompt engineering, it’s about developing collaborative intelligence - the ability to use AI not as a tool, but as a thinking partner that expands your cognitive range. The companies and people who master this won't just have AI working for them. They'll have AI thinking with them in ways that make them fundamentally more capable than their competition. What are your pro-tips for effective AI prompts? #AppliedAI #CollaborativeIntelligence #FutureofWork

The examples from Amazon, JPMorgan, and others show we're at the start of a huge shift. This isn't just about the layoffs this year, it's a fundamental change in how companies operate. We're moving from a focus on automating a few tasks to a new world of AI & human working together on complex problems. This change is why we’re seeing "The Great Shrinking" of corporate teams. My advice for both sides? For Employers- 1. Prioritize Strategic Workforce Planning and Reskilling: - Before you even think about letting people go, figure out what skills your company needs for the future. - Many of your current employees have deep knowledge of your business. - Give them a path to new roles by providing training to learn how to work with AI. 2. Be Radically Transparent: - Don’t let rumors take over your communication strategy! - Be clear and open with your teams about how AI will change their jobs and what you’re doing to support them. - Transparency builds TRUST. 3. Reevaluate How You Judge Performance: - In an AI-powered workplace, success is no longer just about the number of tasks completed. - Reward skills that AI can’t replace, like creativity, empathy, critical thinking, and relationship building. For Employees- You have MORE control than you think. Don't wait for change to happen to you… be an active part of it. 1. Learn Continuously: - The most important skill today is the ability to LEARN and UNLEARN. - Find out which AI tools are being used in your field and learn them fast. 2. Focus on Becoming that “Human in the Loop": - AI needs human oversight. - Build skills that make you essential, like creative problem-solving, critical thinking, and empathy. 3. Plan Your CAREER, Not Just Your Job: - The days of a single, lifelong career are fading. - Think about your next role and what you need to learn to get there. - Be proactive about your own professional growth, invest money and time in YOU. Data With Serena™️

If I were leveling up as an FP&A analyst right now, I'd focus on these 5 areas (that no finance certification will teach you) 1. Learn how to pressure-test AI-generated forecasts     AI forecasting tools are already inside your ERP and planning software whether you asked for them or not. The dangerous analyst is the one who trusts the output without knowing what the model is optimizing for. Learn to interrogate these outputs: what data trained it, what it ignores, and where it historically breaks down. The analyst who can audit AI becomes the one leadership actually trusts.     2. Get fluent in working capital at the operational level     Most FP&A analysts can read a cash flow statement but can't tell you why DSO moved 8 days last quarter or what's actually sitting in the payables aging. Working capital is where the P&L and real business operations collide. And most analysts avoid it because it requires leaving the model and talking to procurement, AR, and ops. That discomfort is exactly where your leverage is.     3. Stop reporting costs. Start reporting cost behavior     There's a massive difference between telling leadership "OpEx was up 12%" and telling them "fixed costs held flat but variable costs scaled faster than revenue, which means our operating leverage is moving in the wrong direction." One is a report. The other is a diagnosis. Understanding how costs behave, fixed, variable, stepped, semi-variable, and what drives each is what separates analysts from advisors.     4. Master the art of the one-page brief      The higher up the conversation goes, the less time there is. CFOs and CEOs don't want a 40-tab model. They want to know the answer, why it matters, and what happens next, in under 60 seconds. Obsess over translating complexity into a single page that forces a decision. This is a writing and thinking skill as much as a finance skill, and almost nobody in FP&A trains for it deliberately.     5. Understand how the business actually wins new revenue      Most FP&A analysts model revenue but have never sat in a sales call, reviewed a pipeline review, or understood why deals actually close or fall apart. If you don't know how the business generates revenue at a ground level, your forecast is just math on top of someone else's assumptions. Spend time with sales and commercial teams. Your models will never be the same.     The analysts who will matter most in the next 5 years aren't the ones who know the most tools. They're the ones who understand the business well enough to know which questions even need answering. ♻️ Save this or share it with someone building their FP&A career.

In earlier posts, I've discussed the immense promise and major risks associated with the new wave of text-prompted AI analytical tools, e.g., ADA, Open Interpreter, etc. Here are some best practices to avoid these pitfalls... 🔸 Prepare Written Analysis Plans - many Data Analysts are unfamiliar with this approach and even fewer regularly implement it ( < 20% by my estimates). But preparing and sharing a written plan detailing your key questions and hypotheses (including their underlying theoretical basis), data collection strategy, inclusion/exclusion criteria, and methods to be used prior to performing your analyses can protect you from HARKing (hypothesizing after results are known) and generally increase the integrity, transparency and effectiveness of your analyses. Here's a prior post with additional detail: https://lnkd.in/g6VyqCsc 🔸 Split Your Dataset Before EDA - Exploratory Data Analysis is a very valuable tool, but if you perform EDA and confirmatory analyses on the same dataset, you risk overfitting, and expose your analysis to risks of HARKing and p-hacking. Separating your dataset into exploratory and confirmatory partitions allows you to explore freely without compromising the integrity of subsequent analyses, and helps ensure the rigor and reliability of your findings. 🔸 Correct for Problem of Multiple Comparisons - also known as the "Familywise Error Rate", this refers to inflating the probability of a Type I error when performing multiple hypotheis tests within the same analysis. There are a number of different methods for performing this correction, but care should be taken in the selection since they have tradeoffs between likelihoods of Type I (i.e., "false positive) and Type II (i.e., false negative) errors. 🔸 Be Transparent - fully document the decisions you make during all of your analyses. This includes exclusion of any outliers, performance of any tests, and any deviations from your analysis plan. Make your raw and transformed data, and analysis code available to the relevant people, subject to data sensitivity considerations. 🔸 Seek Methodological and Analysis Review - have your analysis plan and final draft analyses reviewed by qualified Data Analysts/Data Scientists. This will help ensure that your analyses are well-suited to the key questions you are seeking to answer, and that you have performed and interpreted them correctly. None of these pitfalls are new or unique to AI analytic tools. However, the power of these tools to run dozens or even hundreds of analyses at a time with a single text prompt substantially increases the risks of running afoul of sound analytical practices. Adhering to the principles and approaches detailed above will help ensure the reliability, validity and integrity of your analyses. #dataanalysis #statisticalanalysis #ai #powerbi

AI is changing how we work. It's fundamentally reshaping team dynamics. From fluid roles to global collaboration, today’s team dynamics are evolving faster than ever. Understanding these 12 shifts isn’t optional; it’s critical to staying agile, competitive, and future-ready: 1/ From Fixed to Fluid Roles ↳ Teams swap tasks based on AI proficiency ↳ Skills matter more than titles 💡 Pro tip: Create a team skills matrix that tracks both AI and human capabilities. 2/ From Knowledge Silos to Open Learning ↳ AI tools democratize expertise ↳ Everyone becomes a teacher-learner 💡 Pro tip: Set up a shared prompt library where teams document their AI breakthroughs. 3/ From Linear to Parallel Processing ↳ Multiple projects run simultaneously ↳ AI handles routine tasks while teams focus on strategic thinking 💡 Pro tip: Use AI project managers to track parallel workstreams. 4/ From Competition to Collaboration ↳ Success = enhancing AI outputs ↳ Shared prompt libraries 💡 Pro tip: Create weekly "AI win sharing" sessions where teams present their best AI solutions. 5/ From Meetings to Async Intelligence ↳ AI summarizes discussions ↳ Continuous feedback loops 💡 Pro tip: Use AI meeting summaries as living documents that teams can enhance asynchronously. 6/ From Individual to Collective Problem-Solving ↳ AI provides initial solutions ↳ Teams refine together 💡 Pro tip: Start problems with AI-generated solutions, then use human wisdom to enhance them. 7/ From Status Updates to Strategy Sessions ↳ AI handles progress tracking ↳ Meetings focus on innovation 💡 Pro tip: Automate status reports with AI. Save meeting time for strategic discussions only. 8/ From Fixed Skills to Learning Networks ↳ Continuous AI upskilling ↳ Rapid knowledge sharing 💡 Pro tip: Rotate "AI champions" monthly to spread expertise across the team. 9/ From Task Completion to Value Creation ↳ AI handles the routine ↳ Teams focus on innovation 💡 Pro tip: Track time saved by AI and reinvest it in innovation projects. 10/ From Hierarchical to Neural Networks ↳ Expertise flows freely ↳ Innovation comes from everywhere 💡 Pro tip: Create open channels where anyone can share AI innovations. 11/ From Risk Aversion to Rapid Testing ↳ AI reduces experiment costs ↳ Faster iteration cycles 💡 Pro tip: Set up an "AI sandbox" where teams can experiment. 12/ From Individual Metrics to Team Impact ↳ Shared success metrics ↳ Focus on team outcomes 💡 Pro tip: Create team-based AI efficiency scores instead of individual performance metrics. These shifts are building a new foundation for how teams think, collaborate, and innovate. The key is to adopt change intentionally, not all at once. Start where your team has the most momentum, and let AI become a catalyst for stronger, smarter collaboration. Which team dynamic shift are you experiencing most strongly? Share below 👇 ♻️ Repost if your team is navigating these changes. Follow Carolyn Healey for more like this.

The role of the Data Analyst is undergoing a major shift. It is no longer just about building dashboards or answering ad hoc questions. The next generation of Data Analysts will focus on training, evaluating, and supervising AI copilots, much like onboarding a new team member. For these copilots to be useful, three things are critical: 1 - Aggregating data across systems, because the most valuable insights live at the intersection of product, marketing, sales, finance, and more 2 - Modeling the data to clean, structure, and join it in a way that reflects the business 3 - Establishing a strong semantic layer, so the AI can understand business definitions, metrics, and context, and respond reliably across Slack, APIs, apps, or embedded experiences Without these foundations, text-to-SQL is just guesswork. With them, large language models (LLM) can deliver clarity, consistency, and actionability. This changes the role of the Data Analyst. From Report Builder to AI coach. From Dashboard Designer to Semantic Modeler The future of data is not tools versus people; It is people training the tools, with structured knowledge as their superpower.