Decision-Making In Negotiations

Confession time: As a leader, I often get asked if I'm more intuitive or calculative in my decision-making. The truth is, it's a bit of both. Recently, we were in the middle of expanding our vendor partnerships at Falabella, and an opportunity came up with a key supplier. The catch? We had only 72 hours to decide before a competitor could swoop in. My gut told me this partnership was the right move—it aligned with our long-term goals, and the supplier's reputation was solid. But I couldn’t just go off instinct. I called an emergency meeting with my team. We reviewed everything—from the supplier’s past performance to our budget forecasts and potential market shifts. I knew we had to move fast, but I wanted to make sure every angle was covered. In the end, the numbers confirmed what my instinct was already telling me—I made the call to sign the deal. Looking back, it wasn’t just about moving quickly—it was about being decisive with the right balance of instinct and analysis. In moments like this, I make sure to keep a few things in mind: > First, while my decisions are based on facts, I never forget the human side—how my choices impact my team, my partners, and the people around me. > Second, I’m constantly aware that leadership is as much about people as it is about strategy. > Finally, it's important to act swiftly but thoughtfully, blending instinct with calculated risks. What about you? Do you lean more toward intuition or calculation when making decisions?

Data or Gut Feelings. Whenever I’ve made strategic decisions while neglecting my gut feelings, I have felt a tinge of regret. Leaders are often urged to make data-driven decisions in this age of abundant data. Data is significant; it offers valuable insights by revealing past trends and providing predictive analytics, yet I believe it has limitations. Data alone will not always account for individual circumstances, unexpected challenges, or the essential human elements crucial to effective leadership. On the other hand, intuition - rooted in experience, judgment, and the ability to recognise patterns - can be incredibly powerful, especially in uncertain or quickly changing environments. Still, we must acknowledge that biases and narrow perspectives can sway intuition. Today’s leaders face the interesting challenge of blending analytical skills with intuitive wisdom rather than choosing one over the other. For example, while data may highlight an emerging market trend, intuition empowers leaders to assess whether the timing, cultural relevance, or team readiness aligns with taking action. A potent way to bridge this gap is by asking lots of critical questions during decision-making: Cultivating a habit of evaluating choices from numerical and descriptive angles ensures a more robust approach. The essence of future leadership lies in mastering the art of merging analytics with intuition. We can achieve this by fostering critical thinking to evaluate data accuracy, employing scenario planning, evaluating multiple alternatives to juxtapose gut feelings with measurable insights, and building diverse team thinking to challenge assumptions. Practical steps, such as conducting post-mortems to reflect on decision-making processes, help bring this balance to life. When data and intuition unite, leaders can make much more impactful decisions. So, I vote for a harmonious combination.

Reflecting on two intense but incredibly valuable days with my teams in Amsterdam, where we focused on harnessing AI and the platform to unlock greater value for customers, has prompted some deeper thought on our industry's trajectory. For decades, Business Process Management (BPM) remained an exercise in human cognition. Professionals manually applied methodologies such as Lean and Six Sigma within structured frameworks like APQC to strip away waste and standardise workflows. This traditional model relied heavily on internal expertise to identify bottlenecks and enforce process control through direct observation. The subsequent arrival of digital business process analysis tools and, more recently, case-centric process mining shifted the paradigm by significantly accelerating the journey from data to insight. By visualising singular, linear journeys, these technologies democratised process intelligence and enabled a move toward evidence-based decision-making across the enterprise. The field has evolved further with the advent of Object-Centric Process Mining (OCPM), which allows organisations to analyse operations across multiple dimensions simultaneously. By moving beyond isolated cases to view the intricate web of interacting business objects like orders, items, and logistics carriers, OCPM provides a far more accurate digital twin of the modern organisation. However, this depth of analysis creates a new challenge: as the granularity of the model increases, so does the complexity of simulation and the computational burden of identifying meaningful improvements. Within highly complex, cross-functional value chains, this is where Artificial Intelligence currently adds value by distilling vast datasets into simpler, actionable outcomes. Looking forward, it is hybrid quantum-classical computing that promises the definitive leap in value chain optimisation. By utilising quantum processors to navigate the exponential permutations inherent in multi-object environments, these systems will eventually solve global coordination problems that remain too computationally intensive for classical computing. For highly regulated enterprises, this evolution teases a future of quantum-enhanced, object-centric orchestration that secures business value across every link of the chain. While that utopia remains out of reach today, AI already equips companies to create dynamic digital twins that enable the discovery of transformative insights: Where will automation deliver the greatest benefit? What is the optimal ratio in a hybrid human-agent workflow? How can risk be mitigated and value unlocked in governed processes? The list of questions is almost as endless as the possibilities presented by quantum-classical computing. Almost.

The potential of Humans + AI decision-making is superior decisions - and outcomes - across the board. Yet we still do not have decision architectures that clearly integrate the strengths of humans (context, experience, judgment, intuition) and AI (rich data, pattern recognition, scenario analysis). A starting point is that any AI inputs to decisions are explainable. Black box recommendations can only be accepted or rejected. Only when inputs, rationales, logics etc. are presented can AI outputs be meshed with human cognition. Yet humans are generally not good at incorporating external recommendations or rationales into their own cognitive structures. They tend to interpret AI inputs with existing biases, override them, or simply ignore them. One of the most interesting approaches is Evaluative AI, proposed by Tim Miller. Evaluative AI does not provide recommendations, it helps human decision-makers to generate hypotheses and assess them by providing evidence for or against. The decision-maker is in control of the process and hypothesis choice. This is how to put it into practice: 1️⃣ Define the decision and frame the case State exactly what decision must be made, why it matters, and any constraints, then gather the key facts or events so the situation is explicit before you evaluate options. 2️⃣Surface options List viable options yourself and let the tool add or filter to a manageable set, avoiding a single persuasive recommendation. 3️⃣ Select a hypothesis to test Choose one option to examine now, keeping control of the sequence and scope of what gets explored. 4️⃣ Gather evidence for and against, including confidence levels Ask for balanced reasons supporting and refuting the active hypothesis, including degree of uncertainty, so you can calibrate confidence. 5️⃣ Compare trade-offs across options Place two or more options side by side on the same criteria to reveal where each is strong, weak, and in tension. 6️⃣ Decide, log, and revisit as facts change Make the call, record your rationale and rejected alternatives, and re-run the evaluation when new information arrives. This can be implemented using standard LLMs, or embedded in a tool. I'll be sharing more detailed structures on high-performance Humans + AI decisions and work coming up.

𝐆𝐮𝐭 𝐅𝐞𝐞𝐥𝐢𝐧𝐠 𝐯𝐬. 𝐃𝐚𝐭𝐚: 𝐖𝐡𝐲 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬𝐞𝐬 𝐍𝐞𝐞𝐝 𝐭𝐨 𝐌𝐚𝐫𝐫𝐲 𝐁𝐨𝐭𝐡 A few years ago, a retail chain was confident a premium product line would succeed based on "gut feeling." The category head had decades of experience. The team trusted their instincts. Initial excitement was high. But three months in, the numbers told a different story: 📉 Low repeat purchases 📉 Poor regional performance in Tier 2 cities 📉 Inventory piling up What went wrong? They had ignored two key data signals: The product’s price point exceeded the average basket size in target regions. Customer sentiment data (from social listening) showed aspirational interest, but not buying intent. The pivot came only after the analytics team intervened. A/B tests, pricing experiments, and geotargeted campaigns followed. Eventually, the line found success in a different market segment altogether. The lesson? Intuition is invaluable—it sparks ideas, moves quickly, and often comes from deep expertise. But data brings discipline. It helps validate, adjust, and scale with confidence. In fast-moving markets, relying on just one can either stall innovation or invite expensive mistakes. 👉 The real magic happens when instinct and insights work together. 🔄 Over to you: 𝑯𝒐𝒘 𝒅𝒐 𝒚𝒐𝒖 𝒃𝒂𝒍𝒂𝒏𝒄𝒆 𝒈𝒖𝒕 𝒇𝒆𝒆𝒍𝒊𝒏𝒈 𝒘𝒊𝒕𝒉 𝒅𝒂𝒕𝒂 𝒊𝒏 𝒚𝒐𝒖𝒓 𝒅𝒆𝒄𝒊𝒔𝒊𝒐𝒏𝒔? 𝑨𝒏𝒚 𝒆𝒙𝒂𝒎𝒑𝒍𝒆 𝒘𝒉𝒆𝒓𝒆 𝒐𝒏𝒆 𝒔𝒂𝒗𝒆𝒅 𝒚𝒐𝒖 𝒇𝒓𝒐𝒎 𝒕𝒉𝒆 𝒐𝒕𝒉𝒆𝒓? #DataDrivenDecisionMaking #DataAnalytics #DataVsInstinct #BusinessStrategy

Decision-making is a necessity in almost every aspect of daily life. However, making sound decisions becomes particularly challenging when the stakes are high and numerous complex factors need to be considered. In this blog post, written by The New York Times (NYT) team, they share insights on leveraging the Analytic Hierarchy Process (AHP) to enhance decision-making. At its core, AHP is a decision-making tool that simplifies complex problems by breaking them down into smaller, more manageable components. For instance, the team faced the task of selecting a privacy-friendly canonical ID to represent users. Let's delve into how AHP was applied in this scenario: -- The initial step involves decomposing the decision problem into a hierarchy of more easily comprehensible sub-problems, each of which can be independently analyzed. The team identified criteria impacting the choice of the canonical ID, such as Database Support and Developer User Experience. Each alternative canonical ID choice was assessed based on its performance against these criteria. -- Once the hierarchy is established, decision-makers evaluate its various elements by comparing them pairwise. For instance, the team found a consensus that "Developer UX is moderately more important than database support." AHP translates these evaluations into numerical values, enabling comprehensive processing and comparison across the entire problem domain. -- In the final phase, numerical priorities are computed for each decision alternative, representing their relative ability to achieve the decision goal. This allows for a straightforward assessment of the available courses of action. The team found leveraging AHP proved to be highly successful: the process provided an opportunity to meticulously examine criteria and options, and gain deeper insights into the features and trade-offs of each option. This framework can serve as a valuable toolkit for those facing similar decision-making challenges. #analytics #datascience #algorithm #insight #decisionmaking #ahp – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Spotify: https://lnkd.in/gKgaMvbh https://lnkd.in/gzaZjYi7

We built AI to think like the rational agents economists imagined. But it turns out that humans are valuable precisely because we are irrational. For decades, traditional economics assumed people were "Econs" — perfectly rational optimizers who maximize utility, ignore sunk costs, and make decisions like calculators. Behavioural economics proved that wrong. Humans are systematically irrational. We use mental accounting. We let context shape choices. We make decisions that would be called, in technical terminology, ‘delulu.’ And yet we survive, even thrive. The funny paradox is that AI was trained to be the rational agent we never were. LLMs learn domain-specific heuristics instead of general-purpose algorithms. They can sometimes be worse at arithmetic than a simple Casio calculator. They excel at pattern matching but struggle with context, common sense, and the messy reasoning that comes naturally to a four-year-old. We optimized AI for the world that the economists described. But that's not the world we live in. Here's what this means for decision-making: 1: Humans contextualize, AI can’t. AI can process massive datasets and find patterns faster than any human. But it can't tell you why the driver in front of you cut you off, why people vote against their interests, or what birthday present to get your partner. Those require building models of human minds — something that needs empathy. 2: Human "irrationality" is actually adaptive intelligence. The biases behavioural economics uncovered are core features of human minds. Loss aversion kept our ancestors alive, and mental accounting helps us manage complex trade-offs with imperfect information. These "errors" are shortcuts that work in uncertain, resource-constrained environments — where most real decisions happen. 3: The future belongs to hybrid decision-making. - Let AI handle optimization: data analysis, pattern recognition, or quantitative forecasting - Reserve human judgment for context interpretation, ethical trade-offs, or creative problem-solving - Design workflows where each amplifies the other's strengths The best decisions won't come from AI alone or humans alone. They'll come from knowing which tool fits which problem. Rationality without context is sub-optimal at best and dangerous at worst.  The real human edge will be knowing when to calculate and when to contextualize.

According to an August 2024 Gartner webinar poll of IT leaders, between 70% and 90% of the data in enterprises is unstructured. The importance of extracting information residing in documents has grown significantly over the years for organizations investing in decision automation. The use of #LLMs and #GenAI to support development initiatives has been a dominant theme from product roadmaps across decision automation vendors. 🔵 GenAI’s ability to interpret unstructured data like text prompts, process diagrams, and systems data provides new methods to identify what automation developers need. 🔵 Combined with the ability to make next-best-action recommendations from historical pattern recognition, GenAI will change how organizations build automations. 🔵 Prepare for a future of AI-assisted automation development by establishing governance policies that comply with development standards, maintain data security and promote autonomous decision making. We also see that there will be many technological, cultural and organizational obstacles that must be overcome to enable #agentic automation. While the route to fully enabled agentic AI enabled decision automation will be complex, organizations can prepare now by establishing governance policies on topics like development standards, data security and access, and decision intelligence enabled by composite AI. 🔵 Stakeholders involved in decision making using intelligent document processing (IDP) initiatives are much more focused on handling #unstructured data that is very industry/domain-specific. 🔵 By integrating multimodal LLMs within a platform that orchestrates all necessary tools, subject-matter expertise, and human in the loop features, organizations can significantly enhance their decision automation capabilities. Gartner clients subscribed to our Enterprise Applications Leadership research focused on how to Architect, Implement and Integrate Applications can login now to read our: "Predicts 2025: The Future of Automation Is Autonomous" https://lnkd.in/eym3ZRtY [published 12 December 2024 | ID G00821746] authored by my Gartner colleagues Arthur Villa, Saikat Ray, Sachin Joshi.

Was Kahneman wrong when it comes to using data in strategy? 🧠Kahneman, in Thinking, Fast and Slow, taught us that our minds are negatively affected by biases. He says, fast thinking, and intuitive shortcuts lead to predictable errors. His advice, therefore, is to slow down, analyse carefully, and avoid being overconfident. ⚡Gigerenzer, in The Intelligence of Intuition, looked at how we make decisions and saw something else entirely. He suggests that heuristics (mental shortcuts, or rules of thumb) are not flaws but features that evolved to allow us to make good decisions in situations of uncertainty. He tells us not to fight intuition, but to tame it, to learn when it works and when it doesn’t. As a scientist, I used to wholeheartedly believe that data-driven decision-making is the best approach to any problem, including strategy. Collect good data, use the right formula and the correct answer will result. But then I realised, if that were true, big companies with huge resources and capabilities to gather data wouldn’t ever fail. But they do. This nagging doubt has always been in the back of my mind. And I kind of understood that it has to do with uncertainty. But Gigerenzer showed me a different way of thinking about our human intuition. Precisely because strategy lives in the world of uncertainty, where data is incomplete, time is short, and cause and effect are unclear. In that world, analysis starts to fail. Kahneman’s world is one of known risks, measurable probabilities, stable relationships and repeatable patterns. Perfect for finance, process optimisation or quality control. When the rules are clear, analysis wins. Gigerenzer’s world is one of true uncertainty, novel markets, disruptive technologies and ambiguity. Here, too much analysis can paralyse. Here, heuristics, the ability to simplify and act, can outperform complex models. Gigerenzer speaks about adaptive rationality, which really means using the right simplicity in the right situation. Intuition tends to outperform analysis, particularly when you face novel, fast-changing conditions (disruption, innovation… I’m looking at you AI) and you’re overloaded with information. But you need accumulated experience from similar situations. Analysis shines in stable, rule-based environments when cause and effect are understood, and everything is measurable and repeatable. That’s where models and forecasts deliver real value. But when the system itself is shifting, excessive analysis can mislead, giving the illusion of certainty where none exists. 💡Great strategists know when to switch between data and intuition. Kahneman helps us see where intuition fails. Gigerenzer helps us see when it works. That’s why I believe strategy will always remain a human art, not an analytical one, reducible to algorithms or statistics. ------------------- 👋 Hi, I’m Kerstin. I help organisations create strategies under uncertainty by balancing analysis with human judgment. #Strategy

Startups, almost by definition, are games of outliers. You win by making non-consensus bets that turn out to be right. If your advantage is that you see something others don’t, early data will not give you strong confirmation. What you’ll see instead are weak signals and skepticism. In team discussions, I often hear some version of: “We should be more data-driven.” My response is usually that data is extremely useful if three things are true: - first, you actually have enough of it; - second, you trust that it’s set up correctly; - and third, it aligns with your gut feeling, informed by talking to users and your collective experience. If any of those are missing, I prefer to rely on intuition. Not because intuition is magic, but because it is informed by hundreds of conversations, many failed experiments, and prior experience. This matters more in the early days. In the 0 to 1 phase, you don’t use data to decide. You use data to learn whether your thesis is wrong. There are, of course, times when it makes sense to move along the spectrum from art toward commodity. If your intuition is often wrong, you need tighter feedback loops and the discipline of “strong opinions, loosely held.” Once you have a working loop, activation, habit, retention data becomes extremely powerful. It should lead decisions around onboarding improvements, growth experiments, performance, and pricing. At this stage, taste and data complement each other well. But not before the loop exists. There are also cases where the organization becomes large enough that intuition no longer scales easily. Even then, the underlying problem is often cultural rather than a simple choice between taste and data. In most other situations, following your gut leads to better outcomes. Decisions create motion. Motion generates information. Information, both qualitative and quantitative, feeds back into judgment and helps you navigate the idea maze. If you wait for data to justify every decision, you stall. And without momentum, you end up with very little data anyway. So my advice to people early in their product careers is simple: if you don’t yet have strong intuition, defined as being more right than wrong over time, work with someone who does. Study how great products are built. Pay attention to why decisions are made, not just which metrics are cited. Eventually, you’ll develop the judgment needed to push through the 0 to 1 phase. At that point, data can take on a larger role. And only then can you build a culture that genuinely balances taste and data, rather than hiding behind one or the other. --- From my latest essay on data vs intution, link in the comments.