STILL BUSY TESTING AI? PLAYTIME IS SO OVER!
Yes, today I had the pleasure of giving a talk followed by a dialogue with an exclusive group of AI stars, hosted by the new and cool guys behind Align at Elite Palace in Stockholm, Sweden. The theme of the session was that, in what feels like a total digital deja vu around AI strategy, "playtime is so over." (for a contextualized version in Swedish, click here).
The grown-up in the room
The background was how, fresh out of my PhD program some decades ago, I stood there in a stiff suit among a sea of fleece jackets and leather pants, and said exactly this:
- Playtime is over!
A massive wave of investment in IT and the internet in the twilight of the last millennium had, by the dawn of this one, created some tons of mess in most companies. In the rush of digital technology - boosted by generous sprinkles of FOMO - many were swept up in a flood of investments in systems and applications, recruitment, and expensive IT consultants, with little clarity on why or how it would integrate with existing business or drive new innovation.
Shortly thereafter, I founded Sweden's pioneering company in digital Analysis & Strategy (Anegy, later sold to a listed company), helping 100+ companies use data and digital strategy to bring both order and success to the chaos.
This was later followed by the world’s first book and largest independent app in the space (plus a dozen more companies, a few cracked eggs, 2-3 "ok" exits, 15 million downloads, 3 Gazelle growth awards etc. :D)
Fast forward to deja vu - Things are moving fast :-O
With the current AI wave surging, during the presentation I couldn’t help but share how I’m now both feeling the exact same vibes, and seeing signs of the exact (!) same pattern. This time, it’s no longer about digital communication, but about the magic AI can create with digital data - everything from insights and automation to AI-boosted customer and business value.
Once again, the pace of development has exploded, with a 750% increase in AI investments over the past decade - to the point that AI now represents over 10% of IT budgets and increasing. And that’s just on the corporate side - for the startups and scaleups challenging our dinos, VC investments in AI have increased by 80% in just one year - the same year where total VC fell by 12%.
And the faster it moves, the messier it gets - again ;)
What we see now is how the explosive growth now - again - has led to the majority of companies lacking a clear view of how their AI investments connect or what impact they actually have.
Technically, companies face major challenges aligning AI with their existing stack - where API compatibility, data quality, and vendor lock-in make it hard to tie everything together, and 90% of enterprises have challenges integrating AI into their existing tech stack.
From a business perspective, a mere 26% have moved from PoCs and experimentation to launched services and products - internal or external - that show real ROI, with only 1% having reached AI maturity.
On the security side, most companies are dependent on a few major SaaS giants - with risks barely measured, where AI is plugged into sensitive systems with little visibility, and few know what access the tools actually have - technically, legally, or ethically.
How the first moves always need to be a little wobbly
But stack, structure and security are only half the story. The other - equally important - is about why we’re doing this in the first place.
In times of promising new tech, it can be perfectly reasonable to burn a bit of "funny money" and let a few trailblazers shake things up on idle time - where we run a few experiments, build some PoCs, launch some of them, fail fast and then chase the next shiny thing. Again, all of it driven by a healthy dose of FOMO ;-)
This isn’t necessarily wasteful - it’s what stirs the pot, gives us learning, momentum and positioning, and simply helps us get started.
The goal - Why are we doing all this in the first place?
But eventually comes the time when funny money becomes big bucks, and you hit a tipping point of signs - whether in your own organization or in the industry - that this is having dramatic impact on both brand and business. All even more critical if the impact is generated from self-disrupting collegues or aggressive scaleups.
This is when it’s no longer playtime - in the sense that we need to grow up from ad hoc plans that delivered quick wins in the earlier greenfield gymnastics.
It'll now be time to AI-upgrade the goals shaping our companies’ futures - always starting with "why" - cause it doesn’t matter how fast you run if you’re running in the wrong direction.
At a minimum, you need a clear direction for your AI investments. Ideally broken down into concrete sub-goals for customer value, business value, cost and competitiveness. And preferably supported with measurable KPIs for the outcomes of your AI initiatives.
The means - What is the best way to get there?
Only once your goals are getting clear is it fruitful to explore the best path forward. Should we for different divisions and use cases go towards AI, ML, GenAI - or even AGI? How should we balance build-or-buy between internal development and SaaS? Should we dive into broad ecosystems - or build best of breed solutions tied together with an api-first strategy and MACH architecture? Do we construct our workflows centrally as we done before - or go all in with a data mesh setup? And do we after that operate everything with LCNC tools added with internal training in "light AI," or recruit and maintain advanced systems run by hired or outsourced hardcore data scientists, engineers and architects?
Once all the options are mapped, it’s time to assess which path gets us to our goal fastest, with the least resources - and what other decision criteria beyond goal fit, time and resources should guide the choice. What investment in training and hiring will it require? What’s the probability of success - and the risk of failure? What additional risks exist around security, ethics and compliance? And if we succeed in isolated efforts, how scalable are these paths across the entire organization?
The means - The two strategic prerequisites
On top of that, two more strategic questions regarding scope needs to be in place at the end. The first is strategic alignment. Every solid AI strategy starts with the goal (TO-BE). Anything else gets stuck in the swamp of today’s limitations - something our scaleup competitors are free from.
But to realistically evaluate different paths, we must eventually in the end always account for our starting point (AS-IS) - how well do the alternatives align with the company’s current strategy, stack and structure, and how easy will it be to resolve dissonances? What is the propensity for change in the oganization, and if not, what measure needs to be taken, CAN be taken i combination with different change strategies to ensure that the different paths can be realized.
Connected to this comes perhaps the most critical question of all, the disruption ambition. I.e. should we use AI mainly to repair old processes (e.g. paving cow paths), or are we ready to use AI to innovate entirely new ones (e.g. AI-driven decisions, generative workflows with full hyperautomation, AI-innovation rendering pivoted portfolios and target groups etc)?
The final path is chosen based on all the above (ideally weighted) decision criteria, including strategic alignment and disruptive ambition, combined with data-backed analysis - choosing the alternative that reaches the goal with the least friction and resources in the most reasonable time.
In short: From "playful experiments" to "strategic playfulness"
So, it finally comes: is playtime really over? Of course it’s a provocation. Naturally, we should never (!) stop playing - the curiosity that generates innovation and gives life to the entire organization.
But more and more companies are reaching the AI tipping point - where costs rise, pressure from management, owners and the market intensifies, while coordination breaks down and the increased confusion around stack, security, structure and strategy implodes - making it critical to ask our two fundamental questions:
And that's when it's time to develop an AI strategy, a strategy that works as a compass for all AI initiatives - whether strategic, business-driven, technical or organizational - in the direction that is most advantageous for the company.
Unlike static documents or long-term "plans," the AI strategy needs to be built as an elastic framework for the big strokes, while the content is adapted agilely to changing needs, innovation and market shifts - that is, an AI strategy that provides direction and enables movement.
A strategey where perhaps the greatest gain is not always the "strategy" itself that becomes the deliverable, but perhaps at least as much that we, on several, many or perhaps even all levels in the company, are forced to start thinking in these terms, are forced to consider and prioritize different goals, gain an understanding of how different paths are better suited to achieving different goals based on the conditions the company decides on and is ready for.
Let’s get ready to rumble!!
Finally, it’s worth stating clearly - in most companies, AI is no longer a tech test in the innovation lab. It’s a strategic issue for the board and executive leadership - and then for the entire company. So - are you already doing this? Then it’s time to scale. Not there yet? Then it’s time to start.
We are all now gradually beginning to see the map. Time to set the compass.
PS: For ongoing discussions about AI and its impact on stack, structure, security, strategy and society, join the world’s first real (>100 members) forum on AI-strategy here: https://www.linkedin.com/groups/10070347/
Rufus Lidman, Fil. Lic.
Lidman started his first company at 19 and has since founded or co-founded ten ventures. He now chairs Northern Europe's fastest-growing digital/data talent acquisition firm, recently awarded Gasell and "Recruitment Company of the Year" for its effective use of AI while preserving human connection. This momentum is now accelerating with the launch of the world’s first product within AI-First Hiring™. Previously, Lidman drove a pioneering AI-powered EdTech company in Singapore, reinventing learning for millions in emerging markets. He has worked as a digital strategist across four continents for 100+ companies, including Samsung, IKEA, Mercedes, Electrolux, and PwC. As an entrepreneur, he’s led ten ventures with 2–3 exits, won three Gasell awards, and launched apps with 15+ million downloads. He founded IAB, advised the WFA, is a tech influencer with 50,000 followers, a speaker with 300+ lectures, author of four books, and created the world’s largest digital strategy learning app used by 200,000 people in 165 countries. Lidman holds dual degrees in business and data statistics from Uppsala University, with further PhD studies and data science.