Intelligent Business Decisions

How to Analyse a Company (Like a Real Financial Analyst) Most people look at the stock price. Analysts look beneath it. Because the secret to smart investing isn’t predicting it’s understanding. Here’s how professionals break down a company: [1]. Understand the Business Before the balance sheet, comes clarity. What does the company actually do? Where does its money come from? Is it cyclical, defensive, or growth-oriented? Does it have an edge: brand, patents, or market share? If you don’t understand how it makes money, you can’t value what it’s worth. [2]. Analyse the Financials Numbers tell a story, if you know how to read them. Income Statement: Revenue growth (YoY) → Is it expanding or stagnating? Gross & Net Margins → Are profits growing with sales? EPS trend → Consistency builds trust. Balance Sheet: Current Ratio = Liquidity Debt-to-Equity < 0.35 → Stability ROE > 15% → Efficiency Cash Flow Statement: OCF > Net Income → Real cash, not accounting profits. Interest Coverage > 2.5 → Comfort with debt. Free Cash Flow = OCF – CapEx Healthy cash flow means survival. Healthy margins mean growth. [3]. Evaluate Valuation Now the question — is it worth it? P/E → Are you overpaying for growth? PEG → Growth-adjusted pricing (lower is better) EV/EBITDA → Compare across peers DCF → Find intrinsic value Because price is what you pay. Value is what you get. [4]. Assess Management & Risk A company is only as strong as its leadership. Transparent governance → Trust Consistent strategy → Vision Red flags → Sudden accounting shifts, share dilution, or rising debt. Good management compounds value faster than numbers do. [5]. Decide with Logic, Not Emotion Ask yourself: Is it undervalued? Is it growth, value, or dividend play? What’s my exit plan? You don’t need to be smarter than everyone just more disciplined than most. In investing, clarity is your greatest edge. The deeper you understand the business, the lesser you’ll depend on luck. ----- Jeetain Kumar, FMVA® Founder, FCP Consulting Helping students break into finance and consulting PS: If you want to start your career in finance, check the link in the comments to book a 1:1 session with me #finance #cfa #investment #interviews #consultation

The real risk with Artificial Intelligence today is not that it’s being used as a tool—but that it’s increasingly treated as a silver bullet. This shift, often described by experts as 𝗔𝗜 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘀𝗺, reflects a growing belief that AI can resolve complex social, ethical, and organizational problems simply by applying more data and better models. AI has undeniably earned its place as a utility. It automates routine work, improves efficiency, and enables data-driven decisions across domains such as healthcare, finance, and agriculture. But problems emerge when this utility mindset mutates into blind faith. When AI is framed as a universal solution, four failure modes consistently surface: 𝗢𝘃𝗲𝗿-𝗿𝗲𝗹𝗶𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗯𝗶𝗮𝘀 People defer to AI recommendations over their own judgment—especially when outputs sound confident. The result is diminished critical thinking, weaker challenge, and reduced creativity. 𝗙𝗮𝗯𝗿𝗶𝗰𝗮𝘁𝗲𝗱 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 (𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀) AI systems can produce plausible but false outputs. High-profile legal cases, where generative AI tools fabricated court citations, illustrate how credibility can collapse when verification is skipped. 𝗕𝗶𝗮𝘀 𝗮𝗺𝗽𝗹𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Trained on historical data, AI systems often reproduce—and sometimes intensify—existing racial, gender, and socioeconomic biases, particularly in hiring, credit scoring, and criminal justice applications. 𝗘𝗿𝗼𝘀𝗶𝗼𝗻 𝗼𝗳 𝗵𝘂𝗺𝗮𝗻 𝗮𝗴𝗲𝗻𝗰𝘆 Decision-making responsibility quietly shifts from humans to machines, creating a “responsibility gap” where ethical, political, and accountability judgments are effectively outsourced. 𝗧𝗵𝗲 𝗪𝗮𝘆 𝗙𝗼𝗿𝘄𝗮𝗿𝗱: 𝗛𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗟𝗼𝗼𝗽 Researchers such as 𝗦𝘁𝘂𝗮𝗿𝘁 𝗥𝘂𝘀𝘀𝗲𝗹𝗹 argue that the antidote to AI solutionism is not less AI—but better integration of human judgment. 𝗛𝘆𝗯𝗿𝗶𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝘄𝗼𝗿𝗸𝘀 𝗯𝗲𝘀𝘁 AI excels at pattern recognition and scale; humans excel at context, values, and judgment. The highest-quality outcomes emerge when the two are deliberately combined. 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝗯𝘆 𝗱𝗲𝘀𝗶𝗴𝗻 Organizations are moving toward principles of transparency, accountability, and human verification—ensuring that consequential decisions are reviewed, challenged, and owned by people. The trend is clear: the future is not about replacing human intelligence, but about building “𝘀𝗮𝗳𝗲-𝗯𝘆-𝗱𝗲𝘀𝗶𝗴𝗻” 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀—assistants that augment human capability rather than substitute for it. AI is powerful. But without humans firmly in the loop, it is not wise.

As Duarte grew, I’d hear feedback that decisions were made too slowly, which confused me. In reality, we didn’t have a system to recognize when the team was asking for a decision. We thought they were just informing us, so decisions would languish. We weren’t ignoring them, failing to act, or even making incorrect decisions... We just didn’t realize a decision needed to be made in the first place. It dawned on the exec team that the lack of clarity during the conversation is what slows teams down. Leaders and teams can share the same language for decision-making. Much of it is about shaping recommendations that actually lead to the right type of action and making the urgency clear. Here’s the shift that changed everything… We started mapping every decision against two factors: urgency and risk. Low risk, low urgency: Decide without me. Your team runs with it. Low risk, high urgency: Inform on progress. They update you, but keep driving. High risk, low urgency: Propose for approval. They bring a recommendation, and you decide together. High risk, high urgency: Escalate immediately. You're in it together, right now. Once my team understood which quadrant a decision lived in, they knew exactly how to approach me. And I knew exactly what my role was. The framework gave us a shared language. People can’t act on ideas if they don’t understand how decisions are made. Leaders should define how recommendations move from idea to approval to action. That transparency keeps progress from stalling. Remember: One of the biggest threats to your company isn't a lack of good ideas. It's a lack of clarity. #Leadership #ExecutiveLeadership #OrganizationalCulture #DecisionMaking

The Gap Between Installed and Impactful Two numbers stayed with me this week: -92% of CHROs expect AI to be more deeply embedded in their workforce this year. (SHRM, 2026 CHRO Priorities and Perspectives Report) -Yet only 45% of managers say AI has improved their team's work as much as they expected. (Gartner, HR Survey on Manager-Led AI Adoption, 2026) One number measures adoption. The other measures impact. And the gap between the two may be the most important leadership challenge of our time. We are not struggling to deploy AI anymore. We are struggling to create better decisions because of it. Technology was never the hardest part. #Judgment is. Many organisations introduced AI into existing ways of working without redesigning how decisions are made. We automated tasks, but not thinking. We accelerated workflows, but not wisdom. That's why the conversation for HR has fundamentally changed.  It's no longer: "How do we get people to use AI?"  It's: “The question is no longer whether AI should be involved. It's where human judgment becomes non-negotiable”  Because AI can generate answers. People create judgment. And judgment is still what drives trust, leadership, culture and business outcomes. At Ericsson, these are the conversations that matter most. Not how many AI tools we have introduced, but whether they are helping our people make better decisions, collaborate more effectively and create greater value. The organisations that lead the next decade won't necessarily be the ones with the most AI. They'll be the ones that build the strongest human judgment around it. That's where competitive advantage will come from. What do you think is the biggest barrier today, AI adoption or decision quality?

Only 8% of CEOs have tenures exceeding 20 years in their role. After studying those who succeed, I've discovered they all share the same 4-part mental model. It's not about industry expertise or technical knowledge. It's not about charisma or connections. It's about how they think. It’s a powerful framework that these long-tenured CEOs consistently apply. It's called HEAD: H - Helicopter vision The best leaders can zoom out to see the entire landscape, then zoom in on critical details. Most executives excel at one but fail at the other. They're either: • Too strategic with no grasp of execution details • Too tactical with no sense of the bigger picture Great leaders move fluidly between altitudes. E - Execution intelligence This is about breaking complex challenges into solvable parts. When faced with a seemingly impossible problem, exceptional leaders: • Deconstruct it into components • Identify the critical path • Remove unnecessary complexity One candidate told me how she broke down a failing product launch into 7 components and identified just 2 that needed fixing. That is execution intelligence. A - Analytical integration Beyond just breaking things down, can they reassemble the pieces into a coherent whole? This is where many brilliant people fail. They can dissect problems beautifully but can't integrate solutions back into the organization. The best leaders bridge analytical thinking with practical implementation. D - Disruptive imagination I've never met a great leader without this quality. It's the ability to see the same facts as everyone else but find completely different solutions. To connect dots others don't even see. This isn't about being creative for creativity's sake. It's about imagination applied to real business challenges. Where do you think most CEOs fall short in these four dimensions? 

“The technology worked. The value didn't arrive.” That’s what Bain found in a recent survey, where nearly 40% of companies saved less than 10% from their automation efforts. Most expected double that. How does this happen? Three reasons: The first is data readiness. I’ve written about this before: companies need to invest the time in getting to clean data, clear definitions, and consistent labels. And doing this work upfront is what enables AI outcomes down the line. This is a CEO-level problem, not an IT one. The next is that companies chase the wrong metrics when they roll out automation projects like this. When you measure success by adoption, tokens used, or workflows run, you're optimizing for activity. And while high activity can feel like progress along the way, it’s not the same as seeing outcomes at the end. Finally, there’s often a business-model conflict at play. Most of the AI vendors involved in these projects benefit from your AI usage being high. They aren't incentivized to connect automation to value delivered. The honest version of AI economics is charging for outcomes. So for any business leader looking to be successful with AI, focus on your data readiness first. Make sure you are optimizing for outcomes, and work with a vendor with aligned incentives. I have linked he survey in the comments

A major UK retailer recently said no to migrating to SAP S/4HANA. Kingfisher plc kept ECC running, moved it to Google Cloud, brought in third-party support, and built AI personalization on top. The story spread quickly. The internet treated it as proof that the SAP roadmap is optional. That is not the lesson worth carrying. The line that should travel further came from John Burns at Summit BHC, quoted in CIO Online. He stressed the importance of Phase Zero. Before any architecture decision, the organization has to standardize its data, align its processes, and govern its front end. Without that work, splitting the layers solves nothing. Kingfisher's choice worked because they had already done the work. Their architecture was modular and API-first by design. The path decision came after the readiness decision, not instead of it. Most organizations get this backwards. They debate the platform options as if the choice itself creates value. It doesn't. The path only creates value if the organization can absorb it. Last week I wrote about scope getting decided by default. Readiness is the deeper version of the same problem. Skip the work and you don't have a real choice between paths. You only have the illusion of one. Across enterprise programs I've delivered on four continents, the predictable failure has not been the platform decision. It has been the assumption that picking the right path substitutes for the work that should have happened two years earlier. No technology decision compensates for an unready organization. #SAP #DigitalTransformation #ProgramGovernance #ERPModernization

Nine years ago, SAP sued one of its largest customers for $600 million. Not for pirating software. Not for unpaid invoices. AB InBev was sued because it used its own data, inside a system it had paid dearly for, to integrate with Salesforce and other third-party applications that SAP had not sanctioned. At the time, most people filed it away as a licensing technicality. Today it is the single most important question in enterprise architecture: who decides how your data may be used? Because everything executives now want depends on the answer. Composable ERP. Best-of-breed bolt-ons. AI agents that reach across systems. All of it requires moving data in and out of the system of record freely. And SAP has moved in the opposite direction, instituting API policies that restrict integration with third parties unless SAP approves them. My view is straightforward. If you have spent tens or hundreds of millions of dollars on a platform and filled it with your own transactions, your own customers, and your own financials, you should be free to do whatever you want with that data as long as you are not breaking the product. That should not require anyone's permission. Three implications every SAP customer should be thinking about: 1. Vendor lock is now a board-level exposure, not an IT concern. Consolidated data, embedded AI agents, and restricted integration rights stack into a level of dependency very few boards have consciously approved. 2. Costs will rise. AI capabilities are being priced as loss leaders across the industry. Once the workflows are built and switching is impractical, pricing normalizes upward. 3. Rigidity is the real risk. SAP pioneered the integrated ERP model and it served a generation of companies well. But it is a 51-year-old company still operating in many ways like it did in the 1970s, at a moment when Microsoft and even Oracle are building openly. This is not an argument against S/4HANA. It is an argument for implementing it differently: move on your own timeline rather than SAP's 2030 deadline, treat SAP as your system of record rather than your everything, map the sanctioned and unsanctioned APIs before you sign, and use whatever negotiating leverage you have while you still have it. AB InBev had deep pockets, sophisticated leadership, and a massive SAP footprint. It still ended up settling. Answer the data ownership question deliberately and in writing, before signature, and you will be in a far stronger position than they were. Full breakdown in this week's newsletter. What are your thoughts? #SAP #S4HANA #ERP #DigitalTransformation #EnterpriseAI

What major breakthrough will reshape 2026 and why? In 2026, the biggest shift in my industry won’t be louder marketing or better tools. It will be a quiet one: AI will start judging, not just doing. I realised this during a recent leadership review I was part of. -Two high-performing teams. -Same tools. Same budgets. -One relied heavily on AI to automate outputs. The other used AI to question decisions where to focus, who to trust, what to stop doing. -The difference was striking. -The second team outperformed by 27% in pipeline quality, not volume. This aligns with the data. McKinsey & Company reports that companies embedding AI into decision-making (not just execution) see 20–30% higher revenue growth. LinkedIn’s Economic Graph shows content driven by original insight receives 2x higher engagement than AI-generated, generic posts. By 2026, AI’s real impact will show up in places we don’t publicly celebrate: Performance reviews will rely more on outcome data than visibility or politics Hiring and promotions will move toward skill signals over resumes Content distribution will reward credibility and consistency over frequency Here’s the paradox most people miss. As AI gets better at pattern recognition, human judgement becomes the scarcest asset. In B2B growth and personal branding, trust already drives over 80% of buying decisions (LinkedIn B2B Institute). AI can surface signals but it can’t replace lived experience, context, or conviction. The leaders who win in 2026 won’t be the ones using AI everywhere. They’ll be the ones who know when to lean on AI and when to lead without it. That shift from automation to judgement will define the next competitive edge. #BigIdeas2026 #LinkedInNewsIndia

𝗦𝗔𝗣 𝗰𝗮𝗻 𝗿𝗲𝗺𝗮𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗿𝗲. …but it cannot become the only door. That is the real tension in many Procurement Tech strategies. Companies are increasingly tired of SAP. And yet, they still confirm SAP Ariba as their Procurement core. Contradiction? Not really. Many companies still want what SAP gives them: ▪️One strategic partner ▪️One core architecture ▪️One support model ▪️One internal skill base ▪️One integrated backbone ▪️One throat to choke when things go wrong And, of course, compliance, auditability and protection of existing investments matter too. So yes, I get it. The logic could make sense. But in Procurement, it should also trigger a red flag. Because single-sourcing yourself into dependency is rarely a great strategy. Especially when the experience is still full of: Click-e-ti-click Workarounds Notification noise Poor navigation Training slides nobody wants to read And rest assured: more training will not fix that. You cannot train people out of a subpar user experience. Maybe Gen X users like me learned to live with enterprise software pain. Bugs. Shortcomings. Ugly screens. Strange logic. “Just follow the process.” But we have learned to appreciate modern, multimodal tools and contextual interactions too. And the next generation will not buy the „one ring to rule them all“ enterprise logic. They grew up with apps, search, chat and instant answers. They will not ask: “Where is the right buying channel in Ariba?” They will ask: “Why do I need to know Ariba even exists?” Yes, the Next-Gen version will fix many of these things but it will take and require to hook up to a product roadmap and a lot of hope. And this is where the current SAP API debate becomes relevant. Because if SAP also controls which front doors, agents and orchestration layers can access the core, this is no longer only a UX question. It becomes an architecture question, perhaps even a moat question. ☑️SAP as the backbone makes sense. ⛔️SAP as the only door is dangerous. Procurement productivity will not be won by technology people are forced to use. But as i keep repeating: It will be won by the experience people actually want to use. What‘s your view on single-sourcing your technology vs having choice to design your user experience as it best fits.