I recently did a Podcast with Mark Blackwell from Arkaro, which I embedded (41:28 min) and the executive summary underneath. Mark and I had a great conversation.
Executive Summary
Why do planners spend 50% of their time on tasks that waste their brain power? Niels van Hove, globally recognised thought leader in integrated business planning and human-AI collaboration, argues that the competitive advantage in supply chain planning won’t come from automation alone—it will come from how humans and AI collaborate on decisions.
Core insights:
- Planners spend 50% of their time on data crunching and low-value tasks—a “disaster” and waste of human capital that AI can eliminate
- The ChatGPT launch was a wake-up call for the C-suite, but GenAI is just one type of AI—many organisations conflated the two for years
- Automation handles repetitive tasks; augmentation advises on decisions—the competitive difference lies in human-AI collaboration
- Explainability matters less than trust and “explorability”—the ability to explore AI reasoning and guide its logic
- 90% of supply chain professionals use formulas they don’t understand mathematically, yet demand AI explainability—a contradiction worth examining
- Decision avatars—AI-powered executive teams debating scenarios at unprecedented speed—are already possible and could transform IBP meetings
- S&OE (execution) will be highly automated; IBP decisions will be augmented, never automated
- Three actions for CEOs: think backwards from decisions, embrace technology as inseparable from people, and cultivate mindsets that engage with AI rather than sitting back
The Monthly Trip to the Dentist: Why S&OP Became Painful
Integrated business planning should be a decision-making forum. Instead, for many organisations, it has become what Niels calls “a bureaucratic visit to the dentist”—painful, dreaded, and disconnected from its original purpose.
“Planners spend 50% of their time, educated planners coming from university with years of experience, in data crunching and stuff which doesn’t add value,” Niels observes. “That’s a waste of human capital. It’s a disaster in our modern world. And we accept that.”
The burnout is real. When Niels talks to supply chain directors leading planning teams, they tell him their people are exhausted. Yet the industry persists with processes designed decades ago.
The statistics tell the story: 70-80% of demand plans still rely heavily on Excel. Only 30-40% of companies have truly integrated processes connecting demand, supply, finance and executive decision-making. The adoption rate remains low because the work required feels overwhelming.
“We still can’t manage our data and automate it and cleanse it,” Niels notes. “We still have to, as humans, do 50% of that. It just doesn’t add up anymore. Really? I mean, it just doesn’t add up anymore.”
The Wake-Up Call: How ChatGPT Got Board-Level Attention
When ChatGPT launched, something shifted at board level. Niels describes it as a genuine wake-up call—suddenly AI wasn’t just something the data science team experimented with. It was visible, global, and impossible to ignore.
“All of a sudden, it was this sort of use case which wasn’t a forecast somewhere that somebody in planning did,” Niels explains. “This was visible. This was all around the world, 100 million users in a couple of days.”
But the attention came with confusion. For a year or two, many executives conflated all AI with generative AI. “AI is GenAI. No, no. GenAI is a type of AI,” Niels emphasises. “You have to define what you need for your business processes or rethinking your business.”
The distraction has begun to clear. Organisations now recognise that a chatbot in customer service, whilst useful, doesn’t transform a business. Real transformation requires understanding which types of AI serve which purposes.
This connects to what previous Arkaro podcasts have explored: MIT research showing 95% of AI implementations fail to deliver returns. The FOMO-driven rush to adopt “the shiny new thing” without understanding business-specific applications explains much of that failure rate.
Bless Excel: Why Spreadsheets Fulfil Deep Human Needs
Before dismissing Excel, Niels offers a surprisingly sympathetic view. “Excel is great. We all love Excel,” he says. “It gives you freedom outside of the online system. Nobody sees what you do. It provides you creativity. You can make any scenario you want. You feel autonomous.”
These aren’t trivial benefits. Autonomy, creativity, privacy—these are intrinsic human needs. An integrated system where everyone can see your work doesn’t fulfil them.
“Bless Excel. I think Excel or an Excel-like tool will always be there because it fulfils a deep human need.”
The problem isn’t Excel itself. The problem is scale. “You can run an Excel supply chain for a couple of products, one distribution centre and one manufacturing plant. Once you do it globally across 150 countries, 50 factories, 100 distribution centres and 10,000 products—it won’t work that well anymore.”
The inflection point we’re entering isn’t linear change. It’s exponential. “If you look at GenAI and what it can create and then agentic AI, it’s a different world. It’s not the same world as we were in the last 25, 30 years.”
Automation and Augmentation: Understanding the Distinction
The future of planning involves two distinct but related concepts. Niels has been writing about this since 2019, and the framework remains essential.
Automation handles the repetitive: data cleansing, baseline forecasts, gap detection, scenario calculations. “All our physical assets have been automated—our manufacturing, our warehousing, we’ve got driverless cars,” Niels observes. “The knowledge worker, the planners, they haven’t seen any of that automation. They’re still working in the 90s.”
The opportunity is clear. Automate the 50% of planner time currently wasted on non-value tasks. Free up brain power for higher-level work.
Augmentation goes further. “Now the machine is going to advise what a planner could or should do. I’ve detected a gap against your budget. Here are five options you can do about it. Do you agree or not?”
The critical distinction: automation is the foundation, but it’s not the competitive differentiator. “Anybody can automate. The competitive difference will be in human-AI collaboration.
”This has implications for different planning horizons. S&OE—sales and operations execution, the daily and weekly rhythm—will see high levels of automation. “Should I produce 10 or 12? Should I ship this way or that way?
If we interfere there as humans often, I don’t know if we create value.”IBP decisions are different. The underlying planning work can be automated, but decision-making will be augmented, never automated. “IBP decisions will be augmented, not automated,” Niels states clearly.
The Explainability Paradox: Why Trust Matters More
When Mark raises the “black box” problem—how do we explain how a forecast was created—Niels offers a contrarian perspective.
“Explainability is great. Ideally, we want it. But in the end, you want trust. Do I trust this human? Do I trust this AI? Without trust, nothing moves between humans. My proposition is also not between human and machine.”
The question he poses: “Why do we need explainability? To gain trust. So how long do we need explainability after we build trust? Do we still need it?”
Instead of explainability, Niels emphasises “explorability”—the ability to explore underlying data, examine the AI’s reasoning, guide its logic, set goals. “I actually control this AI. It does what I want to do. So, hey, you build trust over time.”
Then comes the challenge. “Have you ever heard of exponential smoothing as a forecast method? Have you ever heard of King’s formula to set safety stock?”
These formulas from the 1950s are used by 80-90% of supply chain professionals. And Niels puts forward that more than 90% of those professionals don’t understand the mathematics behind them.
“We use them. We trust them. I’ve no idea what the maths is behind it. And now I have an AI, I have no idea what the maths is behind it. I need explainability. I need to be explained what it does. Yeah, maybe. But for how long?”
The contradiction is stark: we demand explainability from AI whilst using mathematical formulas we’ve never actually understood. What we really need isn’t explanation—it’s earned trust and the ability to explore.
Decision Avatars: A Vision for AI-Powered IBP
Asked to imagine the future unconstrained, Niels describes something already possible if organisations commit the resources.
“What we can do with LLMs now is create decision avatars.”
The concept: take on the behaviour of an LLM-based finance manager. Upload all the finance books in the world. Give it certain behaviours aligned to company profile and culture. Test it. Then upload all the P&Ls and financial investments from the company’s history.
Do the same for operations. For marketing. For sales.
“So now I have an executive leadership team, decision avatars, with specific behaviour and specific function, with all the world knowledge on marketing or sales and all the knowledge from the company from the last 10, 20, 50, 100 years.”
Give them a problem. Let them debate. Prompt for guidance. Request five options after robust discussion.
“We created not just a digital assistant—we created a digital team that goes at a speed like we can’t even imagine through scenarios. Give us their best five. We as humans, we’re going to check that. We’re going to use explorability, maybe not explainability. See how they derived that. What’s the logic?”
The human executive team then discusses the top options—prepared by their digital IBP team. The problem could be strategic (should we build this factory, enter this market) or tactical. The approach remains the same.
“It’s already possible if we put the resources into it. Why not?”
Three Pieces of Advice for CEOs
Imagine a CEO whose S&OP process has deteriorated into exactly the bureaucratic mess Niels describes. What should they do?
First: think backwards from decisions. “A company is what it decides and acts on. All the glossy other stuff—in the end, that’s how value is generated.”
S&OP was designed in the 1980s as a decision-making forum. It became something else. “Understand your decisions. Work back from the decision. Then what options? Then what data do you need? And start designing like that.”
Niels predicts organisations will discover they need only a fraction of current S&OP time to make real decisions.
Second: embrace technology. “There’s people who say people, process, and technology. It’s technology and people will become one. Soon you can’t differentiate anymore.”
If you’re still thinking in separate buckets, you’ve already lost the competitive battle. The collaboration between machine and human is indivisible.
Third: cultivate the right mindsets and behaviours. “If you sit back and say, show me the proof, and you don’t show proactiveness, you don’t engage with the AI, you don’t try to teach the AI first and then learn from the AI and make it a collaborative process…”
Niels is blunt about what he’d do as a CEO: “I would pick those people. They wouldn’t stay for long. I would recruit on that. I would put my performance criteria on it. Because AI is here to stay and will only get better.”
The accountability lies with individuals. You cannot sit back, refuse to engage, and demand explainability. AI requires curiosity, proactive learning, and genuine collaboration.
Why This Matters Now
The contrast couldn’t be sharper. On one side: planners burning out on data crunching, processes that feel like dental visits, 50% of professional time wasted. On the other: automation freeing human capital, augmentation enhancing decisions, digital teams preparing options at unprecedented speed.
The technology exists. The gap is human: mindset, culture, willingness to engage.
As Niels frames it, we’re at an inflection point. This isn’t the linear change that characterised the past 25 years. It’s exponential. Organisations can choose to stay in the old world, but the question becomes: for how long can you remain competitive?
The answer, for those paying attention, is increasingly clear.
Thanks to Mark and Akaro for this engaging conversation
Supply Chain Trend 