Why and in what do I invest in AI?

In the continued conversations around Artificial Intelligence, a common topic that is being expressed by business people I talk to is the uncertainty on why and what the business should invest in #ArtificialIntelligence.

It is a valid point, because -after all- there are limited funds available for each business at any given time. But, against the backdrop of the consequences of the covid pandemic and the huge impact of Russia's war of aggression against Ukraine, the current investment climate is even more challenging than usual.

Let's have a look at what leaders in the global market as a whole are doing and what we can learn from them for our own businesses.

Why to invest in AI now (and not tomorrow)?

IBM published its IBM Global AI Adoption Index 2022 in May this year. Amongst its interesting statistics and data, it published a view of how AI is being deployed and explored in different parts of the world.

These are high numbers, and certainly higher than what I would have guessed myself. It's conceivable that many businesses may also have underestimated how far the world is progressing already and may have a false sense of security.

Whilst these percentages show the penetration of AI in businesses, we need to also consider whether this investment in time and resources makes business sense.

McKinsey published a very insightful report in December 2022 on the state of AI in 2022. Amongst the data produced in the report was an interesting picture showing the benefits already being produced in various business areas. These are impressive numbers.

These penetration percentages combined with the business value delivered, show that if at this stage you are not yet using AI in any meaningful way, you should strongly consider to increase your investments significantly not to miss the boat.

In What to invest now (and not tomorrow)?

It should be evident by now that your business may well need an active investment plan to address (generative) AI.

That opens up the question of where that investment should go into.

In yesterday's interesting article in Forbes magazine, Accenture's CTO Paul Daugherty drew attention to his observation that companies mainly use generative AI to drive their growth ambition, rather than filling gaps in talent. He augmented that by saying that, in Accenture's view, 80% of value that will be derived from generative AI will come from models customised to using a business's own data.

This leads us to the first investment channel to consider. We need to ensure our people are 'au fait' with what's happening in the AI sphere in their business area to create, what Daugherty calls 'digital fluency' as a key new language skill to learn.

'Digital fluency' becomes a key skill in any company's talent management strategy and we need to actively invest in it.

Further, building on the article I published a couple of weeks ago regarding navigating the AI Tools landscape, it is a good idea to review where the leaders in the world are in AI technology.

Analytics Insight produced a useful overview on the best companies investing in AI. All the usual suspects, like Meta (Facebook), Alphabet (Google, YouTube), Microsoft and Amazon are in there, but also some specialist providers like C3.ai and People.ai that are worth checking out. Also, look at Salesforce that has acquired some fascinating AI businesses whose tools can help you get up the AI maturity curve quicker. Focusing on these already established, trusted, providers significantly lowers the risk of your AI investment.

Look at larger, already established, AI technology providers to accelerate your investment.

Whilst we build our AI technology portfolio with these proven technologies, we can start to build our own internal capability ('digital fluency') at the same time as learning and understanding more where the business opportunities lie.

To determine that, we need to be very clear on what the business processes are that our business needs to excel in order to differentiate ourselves in the marketplace.

A simple model that can help is to map the key processes in your business on a 'Boston Square', where we map our processes amongst 2 dimensions

• The level of differentiation of our processes compared to the market (how different are our processes compared to others?)
• The value add of those processes to our business

It is evident that in the Low differentiation, Low value (red) box customised investment in AI technology is not the right way forward, but here we need to look at the already established, proven, technology that everyone can access. This, after all, is not where we will get our competitive edge, but it is to ensure that we adapt our own organisation to what others will also do. Think about process areas like financial processing, etc.

On the other hand, once we have the requisite level of maturity to progress any customised AI development, we need to focus on the High Differentiation, High Value (green) box.

So, when to develop our own, customised, AI technology that gives us that competitive edge and helps us to run away from our followers in our chosen markets. For instance, developing Machine Learning applications to forecast the footfall in our shops or restaurants, or the demand for our high value products or services during the year. These are high levels of investment, so we only want to do this for where it makes long term, strong, business sense. Therefore, we need to be sure we know what we are doing first before embarking on that journey.

Only once we have confidence in our the internal capability can we invest in more customised, dedicated AI technology in the High Differentiation, High Value add business process areas.

Conclusion

Looking at the things we can pick up above, it seems clear that

1. We do need to be investing in generative AI technology in our businesses now
2. This investment expressly focuses not on technology only, but significantly also in developing internal digital fluency that enables the organisation to engage meaningfully with the AI challenges
3. Start to use already established AI technologies in the areas where there are common ways of working, like financial processing
4. Once you have the level of required maturity, focus on developing customised applications in our own specialist process areas

How are you seeing your investment in AI technology over time?

Do you know what your high value, differentiating business processes are?

Have you familiarised yourself with AI supported providers in your low value add business processes that are common across industries?

What are the leaders in your chosen areas with respect to the use of AI?

What else do you think about when considering your AI Investment portfolio and initiatives?

#artificialintelligence #investing #businessprocess #training #valueadded #maturity