To do this you first need to understand enough about AI capabilities to be able to apply any judgement, and to have a solid awareness of the direction of travel of this technology. A great deal that cannot be done today WILL be possible within 12-18 months. So one needs to be thinking about modularity and continuous ‘unbundling/re-bundling’. There can be no fixed five year plan - flexibility and adaptability need to be built into any new business design.

Given the effort required for this, one can see why so many just add a sixth bullet point. But, as I repeatedly say, the complexity is the opportunity. The Bug is the Feature!

The most sophisticated will be looking for what’s known as AI Synergy. Mostly, AI can enable you to do more with less, faster, and better than you previously could, but such ‘Augmentation’ tends not to enable breakthroughs, in the sense of performing better than a top performer could by working alone. AI Synergy though is where ‘human + machine’ acting together can achieve things neither could better alone. Where 2+2 really can make 5, or more.

Evidence for the existence of ‘Synergy’ came from a late 2024 Meta study of 106 studies, conducted by MIT, on AI and humans working together. They found that there are four scenarios where ‘Synergy’ is possible.

These are:

1. When Tasks Have Both Pattern and Exception

For example, CRE workflows where Generative AI can handle standard patterns, such as drafting documents or creating templates, while humans manage exceptions or unique cases.

2. When Scale Meets Judgment

Where Generative AI can produce scalable outputs, like generating personalised communication or high-volume content, that humans then refine with contextual judgment or specific expertise.

3. When Creativity Needs Structure

Where Generative AI can generate multiple creative options, such as space layouts or branding concepts, which humans curate and align with strategic goals.

4. When Analysis Meets Intuition

Where Generative AI generates scenario options or storytelling elements that humans enhance with intuitive insights, contextual understanding, or strategic planning.

The key principle is finding workflows where AI and humans play to their strengths while compensating for each other's weaknesses. This is where true transformation lies.

Which of course sounds easy, but is not. Whilst on a day to day basis, within one’s existing workflows, there is already a huge amount of utility that LLMs can bring to bear (content creation/modification, language understanding, data synthesis/summarisation, creative/design tasks, routine communications and so forth) really making it count at a company level IS going to require major reconstruction and redesign.

There’s the famous saying about ice hockey star Wayne Gretzky ‘skating to where the puck is going’ but even that isn’t the right analogy. Because AI most likely will be changing the game itself. Figuring out what the new rules are, how to win, and how to play offence and defence is going to require more than a bit of tinkering and some bolt-on features.

I think you need to be thinking along these lines;

Overall: What will the best company in my sector look like in 3-5 years?

(bearing in mind that might mean we have 100X more AI compute power by then)

Step 1: Unbundle the Current Company:

• Deconstruct your current value chain.
• Ask: Which activities, products, and customer interactions are based on historical constraints rather than today’s possibilities?
• Identify legacy structures that can be entirely removed, not just improved.

Step 2: Imagine the 'Super Company'

• If you were building a competitor today from scratch using today’s and tomorrow’s AI capabilities, what would it look like?
• Think in terms of:
  • Instant data-driven decision making
  • Hyper-personalised products and services
  • Continuous capability reinvention
  • Operating models that assume abundant intelligence rather than scarce expertise

Step 3: Rebundle with New Capabilities

• What new business models, customer experiences, and operational practices become possible?
• Example:
  • AI-native companies treat information flow as a live system - insights update operations in real time.
  • Sales, support, R&D, and supply chain are interconnected and autonomously optimised.
  • 90% of internal processes could be automated or augmented.

All of which is quite discombobulating. Unless you are living/breathing ‘Planet AI’ much of this no doubt ‘feels’ a bit hyperbolic. This much change in this short a period of time seems to be over-egging it.

But we are beginning to see early signs of what ‘Second Curve’ companies might look like - even in the traditionally cautious professional services sector. Take, for instance, Unity Advisory…

Last week the Times newspaper in London carried this story:

‘Ex EY and PwC executives launch Unity Advisory to challenge Big Four in UK. Backed up by up to $300 million from Warburg Pincus, the venture is due to commence operations by June'

A small team, led by two top tier ex ‘incumbents’, with money to grow and a remit of ‘completely different’.

Maybe the future IS arriving faster than we thought? AI-ing the past is no longer enough.

Checklist: Are You 'AI-ing' the Past?

• Are you simply automating current processes without questioning them?
• Are your AI initiatives tied to old KPIs and success metrics?
• Have you mapped what a 10X better product or service might look like?
• Are you structured to adopt new AI capabilities rapidly as they emerge?
• Is someone in your company thinking 2 years ahead, not 2 quarters?