Strategy is Imagination - Making Strategy Fun Again (Part 1)

 

Strategy can be a drag, no? Flat, boring & superficial directives from on high prepared by disinterested technocrats, or even worse, by LLMs pretending to be human. You’ve probably seen the (dis)engagement data.  If you haven’t, give any AI engine the following prompt: ‘What is the current level of engagement in multinational corporations?’ – and run for cover.

Strategy is a big part of the problem, and over the next several articles, I ask ‘what’s wrong with strategy & how do we fix it’.

Strategy development, deployment & execution should be bracing, exciting and engrossing.  A process wherein you embrace your biggest challenges. Who are we? What are we really trying to accomplish? What are our main blockers & what do we do about them? What management systems and capabilities do we need to build to tackle them?

If such questions don’t engage us, is there any zest, brio or magic left in our working lives?

Strategy is Imagination

Strategy is the domain of the Dreamer, Artist, Hipster and Non-Conformist.  To paraphrase Aristotle, strategy is a place where things can be ‘other than they are’. And yet too often strategy comprises dry analysis performed remotely by disinterested people (or worse, AI) who don’t know or care that much about the business, and certainly not the essential ‘knicks & knacks’ behind a magic piece of hardware, software or service.

Remote technocrats with no skin in the game not only spoil the dish – they drive everybody in the kitchen crazy and eventually turn them off completely.  Hence, the global (dis)engagement scores. I’ve written elsewhere that widely used methods like OKR are prone to this syndrome. What’s the ‘View from the Floor’? Let me quote a team leader in a major bank: ‘Here’s another dumb thing they want us to do.’

Strategy is Storytelling

So, what’s the remedy?  Storytelling – strategy begins and ends there.  A strategy is tale with a beginning, middle and end.  It has a clearly defined shape, internal dramas, peaks & valleys and a hopefully satisfying resolution.  Strategy exists in Aristotle’s ‘World of Contingency’ – a place where things can be ‘other than they are’.  Analysis informs & validates the story, but a human beings shape it, feel it, and commit themselves to it.

There are no ‘new stories’ – researchers around the world agree on this. Some argue for seven basic plots, others cite six emotional arcs, and still others 36 basic dramatic situations. But everyone agrees there are no new story plots, emotional arcs or dramatic situations.

The same is true for strategy. In earlier articles I introduced the ‘Stick’ & most strategies comprise some permutation of its core elements. What makes a strategy compelling is the story.  How well have the strategy leader & team grasped the situation confronting them?  How well have they articulated their aspiration and winning logic?  How deep is their understanding of the conditions that must be true for this to work? How passionate is this team & leader?  Do we trust them to stay the course through the inevitable setbacks, disappointments and unforeseen disasters?

What makes for a great story? Compelling characters and their interaction, meaningful conflict, and emotional resonance.  A great strategy is pitch for the future, full of drama around ‘What Is’ versus ‘What Could Be’.  Great strategies have hooks, crises, heroes & villains, turning points, magical tools, self-realization, and a call to action.  Great strategies, like great pitches, are memorable: they pull you and fire you up.

In summary, strategy is imagination & synthesis. If you can turn your strategic problem into a compelling narrative, you’ve taken the first step towards achievement. More to come.

Best wishes,

Pascal Dennis         E: pascal.dennis@leansystems.org

Succeeding in an AI World (part 3)

 

In earlier articles I introduced the equation V = Q x A to illuminate the leader’s challenge in an AI world. How to harness the AI’s luminous potential while avoiding the disturbing risks? Today I’d like to go a little deeper.

The value of any intervention is a function of its quality and the degree of acceptance.

Business interventions typically entail novelty - a new vision, aspiration, goals & winning logic, or a new process, technology, way of working and/or thinking, or an innovation needed to solve a problem or fill an evolving need.

Value means something the CFO can point to on a P & L, or in the case of Innovation, validated learning toward filling a critical & unfilled customer need in an important market (Innovation Accounting). Quality means things like how clearly we’ve defined our problem & hypothesis, the quality & completeness of our data, depth of causal analysis, degree to which hypotheses, data & causes are validated, quality of reflection & pivots, how effectively the intervention is deployed, communicated, and sustained. Acceptance means do people like it, use it, trust & believe in the intervention – and in you as its sponsor & leader.

AI can be great for Quality but is toxic for Acceptance. The public backlash against AI & Big Tech in general has gone mainstream. Pixar’s Toy Story 5 for example, carries a strong anti-Tech message - the toys lose their jobs to AI. It’s all there, the screen addiction, loneliness, and phoniness of the chatbots. So, even Tech has joined the anti-Tech crusade. The deep-rooted & growing revulsion to Tech in movies, music, journalism and fields, is well documented by culture critic, Ted Gioia, and many others.

So, what are key drivers of Acceptance? Excellence – the excellence of the humans who propose, develop, and deploy the initiative. By excellence I mean integrity, capability, competence, decency, honesty, courage, tenacity, warm-heartedness, experience, and vision. In fact, I am consciously evoking the ancient Greek word arete.

Here’s my rationale: I have experienced multiple business transformations as a manager, engineer, advisor, and director. The common thread in all successful initiatives has been the excellence (arete) of leadership. In other words, leadership is the spark, killer app, and driver. Leadership animates Acceptance and Quality, and ensures the initiative creates Value.

I experienced all this most intensely as a young Toyota manager in the 1990’s. Toyota was expanding around the world and deployed their best & brightest executives and senseis to facilitate this massive & risky investment. Decades later these facilities remain world-beating paragons of Safety, Quality, Delivery & Value. Why did my colleagues & I embrace the Toyota Production System and it’s complex & often counterintuitive methods & mindsets? Excellence – the excellence of our leaders & senseis. We sensed their excellence and did not want to let them down. We trusted them and knew we would do right by them. They had our trust, loyalty, respect, and affection – and for me, they always will.

In summary, our AI interventions are governed by the simple equation above. Most initiatives that focus on Q at the expense of A, fail. Use AI to enhance Q but understand the limiting factor. If A is zero, then so is Value. In future articles I’ll address AI & Q, and Excellence – what is it, how do we grow it, and how do we scale it?

Best wishes,

Pascal Dennis         E: pascal.dennis@leansystems.org

How Do We Win in an AI World? (Part 2)

 

Imagine we’re a large multinational, customer-facing company under intense competitive pressure. We’ve invested heavily in AI & there’s pressure to demonstrate a rapid ROI. Our team members, though loyal & capable, are hesitant to apply AI tools that might eliminate their jobs. What to do?

What’s In It for Them?

Job One is to answer the question: ‘What’s in it for them?’ Please refer to my earlier articles for my thoughts.

Some AI basics

1) AI can be a fine servant but is a dreadful master, 2) AI’s challenge is not unprecedented and mirrors the challenges posed by breakthrough technologies since the Industrial Revolution. In fact, the challenge is not so dissimilar to that posed by Robotic Process Automation (RPA) a decade or so ago in industries like Financial Services.  3) RPA is deterministic AI (If X, then Y) and excels at automating data entry, invoice processing, or high-volume, routine tasks. 4) Agentic AI is non-deterministic (If X, then Y or Z or maybe W) and excels at complex, cognitive tasks like customer support, strategic planning, or managing end-to-end IT operations. Agentic AI learns, which is disturbing to many.

So, let’s imagine we’re the company described above, and our goal is to improve both Customer & Employee Experience (CX & EX) in a critical customer-facing process, while reducing Lead Time & Cost. How do we proceed?

Overall Approach

1)    Begin every AI use case by clearly defining the problem. This is harder than it sounds. Too often, I hear problem statements like: “We need everybody to use AI this year so we can show the Board we’re getting a good ROI.”  Good luck with that.

2)    Lay a solid foundation by taking waste & variation out of the process. I remember our RPA partner telling me, “If you bring us garbage processes, you’ll spend a fortune & create garbage at the speed of light.” This also means levelling up your data. Is it high quality, complete & end-to-end? If not, fix it (with APIs, RPA & advanced analytics)

3)    Look for both analogue & Digital remedies for Value Stream & Customer Journey hot spots. Early on, analogue remedies will be most effective. Let’s say your goal is to reduce cycle time. Start with OpEx/Lean fundamentals like wringing out waste, addressing bottlenecks, level-loading, and creating extra capacity by cross-training.

4)    Agentic AI remedies come into their own after we fix our process & data gaps.

Key Enabler – Ambidexterity

Your Centre of Excellence (innovation support team) must become ambidextrous – in other words, adept at both OpEx/Lean & Digital remedies. In the age of AI, the ambidextrous win.

More to come.

Best wishes,

Pascal Dennis         E: pascal.dennis@leansystems.org

How Do We Win in an AI World? (Part 1)

 

Is the bloom off the AI rose?  AI optimism seems to be collapsing under a black wave of AI gloom.  To be sure, the effects of AI are everywhere, both good and bad, though the bad tends to grab the headlines. My practice is focused on improvement & innovation– both Digital & analogue.  What am I seeing & what does it mean?

Companies I work with are facing the following conundrum: How do we harness the power of AI while avoiding the jagged rocks dominating the headlines? The pushback against AI is real & growing.  People are rightly worried about their jobs, as industry after industry comes under attack. (And are humans not increasingly repelled, especially by AI in disguise, pretending to be human?)

Let’s imagine we are a large multinational customer-facing company. We have an honorable history of service to our communities, team members and shareholders. We’re under intense competitive pressure & are facing daunting Innovation, Lead Time & Cost challenges. Like so many of our peers, we’ve invested heavily in AI and know the Board expects a rapid ROI. And yet, our team members, though loyal & capable, are understandably hesitant to apply AI tools to improve productivity, reduce & otherwise improve our business. What to do?

AI’s challenge is not unprecedented. In fact, it reminds me of the challenge presented by Robotic Process Automation (RPA) a decade or so ago when it began to be applied at scale in industries like Financial Services. RPA is deterministic AI (If X, then Y) and has been used to automate data entry, invoice processing, and other high-volume, routine tasks. Agentic AI is non-deterministic (If X, then Y or maybe Z or maybe W) and excels at complex, cognitive tasks like customer support, strategic planning, or managing end-to-end IT operations. Unlike RPA, Agentic AI makes decisions based on your needs & the situation on the chessboard. Moreover, and this is perhaps what scares people most, Agentic AI learns through repetition. This is why chess programs like Leela and Alpha Zero continually get stronger, so much so, that human World Champions struggle to secure even a draw against them.

Many of the lessons we learned with RPA apply to Agentic AI. So, how do we win in our brave new world? Let’s begin with the most basic point: Agentic AI, like RPA, is a tool. As such, it can be an good servant but is a dreadful master. Let’s therefore, in every use case, define the problem we are trying to solve, and only then consider what tools might suit. As we’ll learn in coming articles, Agentic AI is rarely the first too you reach for.  Stay tuned.

Best wishes,

Pascal Dennis         E: pascal.dennis@leansystems.org