The early move advantage in AI: leaders vs laggards vs aspirants
Are you a leader or a laggard or an aspirant in AI?
This is a subject close to my heart because I focus my teaching / research and consulting towards the leader end or AI – where the competitive advantages create exponential gains for companies and people
There is a great article from Mc Kinsey called Tipping the Scale of AI: How leaders capture exponential returns which makes this point eloquently
Here are the key takeaways from this article that resonate with me
If you want to be an AI leader, you should be paying attention to this.
Also, if you are working for a company, you should try and see if they aspire to be a leader or an also ran.
I think in a decade, just like we are seeing with the retail industry, many of these also-rans who do not invest in AI for competitive advantage will not exist.
- Where many companies tire of marginal gains from early AI efforts, the most successful recognize that the real breakthroughs in AI learning and scale come from persisting through the arduous phases.
- Key lessons from AI leaders: Fund aggressively when conditions for success are in place; Build density in domains; Bring a rounded set of skills and invest in productivity; Speed execution with iterative releases; Win the front line
- Many organizations underestimate what it takes to sow true gains, be it selecting the right seeds, apportioning the right investment, or having a mindset willing to put up with the vagaries of the crop cycle.
- But for those that persevere, the rewards can be huge.
- McKinsey research finds that leading organizations that approach the AI journey in the right ways and stick with it through the tough patches generate three to four times higher returns from their investments.
- These AI leaders get on a different performance trajectory from the outset because they understand that AI is about mastering the long haul.
- They prepare for that journey by anticipating the types of things that will make it easier to navigate the ups and downs, such as feedback loops that allow data quality and user adoption to compound and AI investments to become self-boosting.
- Where some companies tire of marginal gains from weeks of effort, leaders recognize that the real breakthroughs in AI learning and scale come from working through those small steps.
- But only a small number of businesses (10%) have figured out how to make AI work in these ways. The rest remain mired in the low to middling stages of maturity, with laggards making up 60 percent of the population and aspirants 30 percent
- Top performers recognize that most of the impact comes from the last 20% of the journey
- Leaders get disproportional impact from their AI investments.
- The window of opportunity for underperformers is
- Rather than dabbling in lots of different areas, they build strength and density in one or two domains, then expand from there. That approach allows them to deepen their use and application of unstructured data, access more sophisticated use cases, and layer in the necessary operational underpinnings—the investment, talent, data management, production, and other techniques that allow AI-enabled practices to become embedded into everyday routines
- Moreover, as leaders build domain strength and reach a certain threshold in AI performance, their rate of learning and productivity increases, allowing them to progress through other domains faster and tackle problems of ever-increasing difficulty.
- They recognize that scaling AI solutions to deal with increasingly sophisticated problems is hard, but necessary to capture value. Teaching a machine to identify human faces is one milestone, for instance, but getting the machine to recognize particular faces and only those faces is a far more complex undertaking.
- Once solved, companies gain compounding benefits quickly.
While some of this can be seen as consultants prodding companies to action , to many of us, none of this is new. We have already seen how companies like Amazon are reaping exponential gains due to their investment in technology and AI
There is an early mover advantage in AI and companies who aspire to take a leadership position will gain exponential benefits in comparison to the laggards and the aspirants
Image source pixabay ninikvaratskhelia