Getting value from AI investments
At the recent Bosch AIcon , Volkmar Denner , the CEO of Bosch, made some interesting statements about the AI strategy of Bosch. He confirmed the goal that by 2025 all Bosch products will either have AI inside or be created using AI. Very clear strategy and committment from the CEO. He also said that Bosch will continue to invest in AI although they ,too , are going through tough times. In fact they put even more money on the table: A new AI research center in Tübingen will be financed with 100M€.
He was also open to say that beside the successes they see with some of the initial AI projects he is still waiting for AI products with big impact.
Bosch is clearly a front runner when it comes to AI adoption, at least in Germany. Which shows that creating value with AI on a larger scale is not easy (yet).
At the recent IT Symposium Gartner focused on the topic of AI adoption. They discussed interesting results from their CIO survey. Only 19 % of the CIOs are saying their company has deployed AI now. This is 4 % more than last year – but last year 23% of the companies said they would roll out AI in 2019.
So what is the problem with AI adoption ? According to Gartner the 3 main challenges are
- the lack of skills and experts ,
- the quality of the data companies have available, and also
- understanding the real benefits and use cases of AI.
Due to Gartner one way to address these challenges is to set up an “AI Center of excellence” in the organisation. A recommendation which I very much support and you can also see at Bosch as an example.
Another really interesting report on AI came out recently from MITSloan and BCG: the Artificial Intelligence Global Executive Study and Research Report. They surveyed 2,555 executives representing 29 industries and 97 countries on the adoption of AI and did a deep dive with 17 executives from leading AI initiatives in large organisations.
This is a highly recommended read.
Some key findings:
More than 90% of the executives worldwide expect to get some value from AI. But 65% report that they are not yet seeing value from the AI investments they have made in recent years. And even 40% of organisations making significant investments in AI do not report business gains from AI yet.
Getting value from the AI investments is clearly a challenge for many organisations.
What are the lessons learned from those organisations that do see value from there AI Investments:
- Integration of their AI strategies with their overall business strategy is critical.
- They often take on large, and risky, AI efforts that prioritize revenue growth over cost reduction.
- They align the production of AI (the implementation of use cases) with the consumption of AI (internally and externally), through thoughtful alignment of business owners, process owners, and AI expertise to ensure that they adopt AI solutions effectively and pervasively.
- They unify their AI initiatives with their larger Digitalisation and Business Transformation efforts.
- They invest in AI talent, data, and process change in addition to (and often more so than) AI technology. They recognize AI is not all about technology.
There is another interesting finding :
Executives increasingly perceive AI as a competitive risk, not just an opportunity. In 2019, 45% of survey respondents perceive some risk to their business from AI, up from 37% in 2017. In China, the perception of risk from AI is even more dramatic: 71% of Chinese respondents view AI as both a risk and an opportunity to their enterprises. Which might be one reason why many Chinese companies are aggressively adopting AI (Chinese companies distinguish themselves by investing more than their global counterparts in AI overall: in AI talent , technology , and the data and processes required to train AI algorithms.)
The executives see different forms of risk: Existing competitors that use AI to work smarter and faster may exacerbate existing threats. Or nontraditional competitors that use AI to disrupt adjacent industries and unsettle otherwise stable market environments may create new threats.
But why is it so hard to realize value from AI?
Scaling solutions beyond the proof-of-concept phase to outperform previous approaches in day-to-day operations turn out to be surprisingly challenging. Generating business value with AI depends on having access to data that meets certain quality and quantity requirements. For many AI use cases it is necessary to source and integrate data across organisational silos. Likewise, the consumption of AI solutions and value achievement often require cross-functional organisational behaviours. It is not only important to anchor the AI initiatives in the business strategy, but also to approach the use of AI as an organisational initiative, in which data and technology are foundational but organisational behaviours and ways of working make the difference in generating business value.
This all is true mostly for AI cases focusing on existing processes and products. But even more importantly in the long run will be to consider how to reinvent and reimagine many of those processes in the context of what AI enables. This is where AI’s true potential will emerge: not in doing the same thing better, faster, and cheaper but by doing new things altogether. This is where AI will disrupt industries the most. This is where organisations can realize the highest value.
Recommended readings on the implementation of an AI strategy
- appliedAI : The elements of a comprehensive AI strategy.
- Andrew Ng / landing.ai : AI Transformation Playbook