Technology-Enhanced Decision-Making Processes

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.

In the rapidly evolving enterprise landscape, ERP is no longer just about automation — it’s about augmentation. As AI reshapes the fabric of operational intelligence, C-Level leaders must shift from legacy systems toward architectures that think, adapt, and learn. This article explores how modern ERP systems are transforming into intelligent engines that amplify human capability, not replace it. The transition from workflows to intelligence marks a fundamental shift. No longer do systems just route tasks — they contextualize them, learn from outcomes, and recommend the best next steps. For executive leaders, this means real-time decision frameworks that are data-rich, insight-driven, and predictive by design. We spotlight how Machine Learning becomes the nervous system of the enterprise, quietly tuning forecasts, optimizing supply chains, and exposing hidden risks. Unlike traditional reporting, these new systems can anticipate and act, creating a proactive rather than reactive operational culture. Yet, we draw a clear boundary: AI doesn’t replace human judgment — it augments it. Modern ERP must serve as executive copilot, not autocrat. Systems must be designed to amplify strategy, not override it, providing clarity while leaving ultimate control in the hands of leadership. The most forward-thinking organizations understand: The future of ERP isn’t humanless — it’s Human-Plus. A future where technology empowers human intent, accelerates time-to-insight, and unlocks a new standard of organizational agility. #ERP #AIIntegration #Leadership #DecisionMaking #EnterpriseArchitecture #DigitalTransformation #HumanPlus #CLevelThinking

This paper presents a vendor-agnostic, human factors-based guideline for designing, evaluating, and improving clinical decision support (CDS) systems, developed through an iterative process involving expert consultation and stakeholder feedback. 1️⃣ The guideline emphasizes assessing whether CDS is an appropriate solution to specific clinical problems, integrating it into workflows, and avoiding overreliance on interruptive solutions like alerts. 2️⃣ By applying human factors principles, it aims to minimize unintended consequences such as alert fatigue, workflow disruptions, and cognitive biases, ensuring alignment with user needs and clinical contexts. 3️⃣ Developed through a two-phased process, the guideline integrates insights from literature, industry standards, and expert feedback to provide evidence-based, practical guidance. 4️⃣ Phase 1 focused on consolidating best practices and creating a flexible design framework adaptable to various system constraints, including off-the-shelf solutions. 5️⃣ Phase 2 involved a workshop with 30 stakeholders, confirming the guideline addressed 15 of 19 user expectations, including decision-making support, CDS design, and iterative improvement. 6️⃣ The guideline introduces design principles over rigid recommendations, supporting tailored and feasible CDS implementation across diverse settings. 7️⃣ It underscores the importance of iterative evaluation, allowing for continuous improvement and decommissioning of ineffective CDS tools to avoid overburdening users. 8️⃣ Participants highlighted its utility in structuring CDS discussions, prioritizing options, and promoting multidisciplinary collaboration for effective implementation. 9️⃣ Recommendations for enhancement included more guidance on system capabilities, technical feasibility, combining CDS options, and creating templates for practical application. 🔟 The guideline advocates embedding CDS discussions into existing decision-making frameworks, fostering structured, collaborative, and evidence-informed practices. ✍🏻 Selvana Awad, Thomas Loveday, Richard Lau, Melissa Baysari. Development of a Human Factorse-Based Guideline to Support the Design, Evaluation, and Continuous Improvement of Clinical Decision Support. Mayo Clin Proc Digital Health. 2025. DOI: 10.1016/j.mcpdig.2024.11.003

During my travels from Silicon Valley to Frankfurt, I've observed a fundamental shift in how technology leaders operate. The most successful ones aren't those with the deepest technical knowledge, they're the ones who've mastered the art of rapid synthesis. They've learned to trust algorithmic recommendations while maintaining human judgment for strategic nuance. What we're witnessing isn't just faster analytics, it's the emergence of what I call "compression leadership." Traditional quarterly strategic reviews are becoming weekly sprint decisions. Gartner predicts that 25% of supply chain decisions will be made across intelligent edge ecosystems through 2025, pushing decision-making closer to the source of data and action. Gartner identifies that D&A is going from the domain of the few, to ubiquity, creating what researchers call "decision-centric vision." But here's the paradox: as data becomes ubiquitous, the ability to filter signal from noise becomes exponentially more valuable. 82% of operations executives face challenges in balancing short-term needs with long-term strategic changes, according to PwC's 2025 Digital Trends in Operations Survey. One such CEO I met earlier didn't succeed because he processed data faster than his competitors. He succeeded because his organization had reimagined the decision architecture itself. Instead of hierarchical approval chains, they built real-time feedback loops. Instead of monthly reports, they created continuous intelligence systems that surface insights the moment they become actionable. As someone who has coded algorithms and led global teams for over 15 years, I've learned that the future belongs to leaders who can think in milliseconds but act with the wisdom of decades. The 15-minute CEO isn't rushing decisions, they're operating with compressed cycles of extraordinary precision. The question isn't whether your organization can adapt to this pace. The question is whether you're building the decision infrastructure to thrive at the speed of insight.

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

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.

Today's fast-paced decision-making demands can overwhelm individuals, leading to decision fatigue, where the caliber of decisions declines. This is particularly pertinent for organizational leaders. To combat this challenge, senior leaders need to refine their decision-making frameworks and harness technologies like Generative AI and Quantum Computing to ease cognitive burdens. Decision fatigue appears as a decreased ability to make effective decisions and can be mitigated through technology. AI and Quantum Computing offer immense promise in streamlining complex decision processes. Leaders should prioritize high-stakes decisions, delegate smartly, utilize tech tools for data interpretation, and ensure periodic mental rest to retain their decision-making acumen. Effective management of decision fatigue requires pinpointing crucial decisions, refining decision-making workflows, wisely delegating tasks, applying technological aids for analysis and automation, and ensuring regular breaks. Such strategies allow leaders to maintain energy and efficacy in their decision-making processes.

Ever felt overwhelmed by the tech choices you need to make? You're not alone. In the fast-paced world of technology, balancing short term gains with long term benefits can be tricky. Here's how to approach it: Identify Immediate Needs → What problem are you solving? → Is it a quick fix or a foundational issue? Evaluate Long Term Impact → How will this decision affect your business in 510 years? → Will it scale with your growth? Cost vs. Value → Short term might be cheaper, but is it worth it? → Longterm investments often bring higher returns. Flexibility and Adaptability → Can the technology adapt to future changes? → Rigid solutions may hinder growth. Security Considerations → Does the solution meet current security standards? → How will it handle evolving threats? Involve Key Stakeholders → Get input from your team. → Diverse perspectives lead to better decisions. Continuous Learning → Stay updated with tech trends. → What works today might be obsolete tomorrow. Remember, balancing short term and long term benefits isn't about choosing one over the other. It's about finding a harmony that drives sustainable growth. What's your approach to making tech decisions? Feel free to share your thoughts! Let's learn from each other.

🌟 𝐈𝐦𝐚𝐠𝐢𝐧𝐞 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬—𝐛𝐞𝐟𝐨𝐫𝐞 𝐢𝐭 𝐡𝐚𝐩𝐩𝐞𝐧𝐬. 🌟 Unlocking the Future: The Synergy of AI and Digital Twin Technology In today’s rapidly evolving digital landscape, the fusion of 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐀𝐈) with 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 is not just a trend—it’s a transformative force reshaping industries. 🔎 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧? A Digital Twin is a virtual replica of a physical asset, process, or system, continuously updated with real-time data. This digital counterpart allows organizations to monitor, analyze, and simulate scenarios, offering a 360-degree view of their operations. 𝐃𝐓𝐓 𝐌𝐚𝐫𝐤𝐞𝐭 𝐒𝐢𝐳𝐞 𝐚𝐧𝐝 𝐆𝐫𝐨𝐰𝐭𝐡: The global digital twin market size was estimated at USD 16.75 billion in 2023 and is projected to grow at a compound annual growth rate (CAGR) of 𝟑𝟓.𝟕% from 2024 to 2030. 𝐑𝐞𝐠𝐢𝐨𝐧𝐚𝐥 𝐆𝐫𝐨𝐰𝐭𝐡 🔷 The Asia Pacific Digital Twin market is expected to grow at a CAGR of 36.7% from 2024 to 2030. 🔷India's market is forecast to grow at a 45.8% CAGR from 2024 to 2030. 💪 𝐓𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐀𝐈: When AI enters the equation, the potential of Digital Twins is amplified. AI enhances these digital models by enabling them to learn from vast amounts of data, predict outcomes, and even optimize processes autonomously. This marriage of technologies opens doors to new possibilities that were once unimaginable. 🟢 𝐊𝐞𝐲 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 𝐨𝐟 𝐀𝐈-𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧𝐬: 1. Operational Excellence: ◾ Predict and prevent failures with predictive maintenance. ◾ Reduce downtime and extend the life of assets. ◾ Optimize resource usage, leading to significant cost savings. 2. Data-Driven Decision-Making: ◾ Make informed decisions with real-time insights. ◾ Simulate various scenarios to assess risks and opportunities. ◾ Accelerate response times to changing conditions. 3. Innovation and Agility: ◾ Test and validate new designs in a risk-free virtual environment. ◾ Rapidly iterate and refine processes without disrupting operations. ◾ Foster a culture of continuous improvement and innovation. 🟢 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: ◾ Manufacturing: Streamline production lines, enhance quality control, and reduce waste. ◾ Healthcare: Personalize patient care, optimize hospital operations, and improve medical device performance. ◾ Smart Cities: Manage infrastructure efficiently, reduce energy consumption, and improve citizen services. 𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐢𝐬 𝐍𝐨𝐰: The integration of AI with Digital Twins is not just a concept for the future—it’s happening now. Companies that embrace this synergy are gaining a competitive edge by unlocking new levels of efficiency, innovation, and resilience. How do you see AI and DTT shaping your industry? Let’s connect and explore how we can leverage these powerful tools to drive transformation together. 👇