Although growing numbers of organisations are working with artificial intelligence (AI) software in some shape or form, very few are generating significant financial benefits when rolling it out in a serious way, according to new research.
A study conducted by the MIT Sloan Management Review and management consulting firm the Boston Consulting Group revealed that as many as 57% of the 3,000 managers, executives and academics questioned were currently either piloting or deploying the technology. A further 59% had devised an AI strategy and 70% believed they understood how the software could generate business value.
Despite this situation, the report, Expanding AI’s impact with organizational learning, indicated that just one in 10 organisations were deriving significant financial value from the technology.
When exploring the reasons why, researchers found that simply getting the basics right – that is, having an appropriate strategy with the right supporting data, technology and skills in place – was not enough. Only one in five organisations gained major financial benefits that way.
Getting the basics right while also building AI systems that the business actually wanted bumped success figures up to 39%, but to truly generate financial value, the secret appeared to be threefold:
- Ensuring machines were in a position not only to learn autonomously, but also for humans to continuously teach them and for machines to continuously teach humans.
- Developing multiple ways for humans and machines to interact based on context.
- Introducing extensive process change in response to what has been learned across the organisation as a result of using AI.
David Semach, a partner and head of AI and automation at Infosys Consulting for Europe, the Middle East and Africa (EMEA), agrees with the researchers that satisfaction with the technology in a financial sense is often quite low, partly because organisations “are mostly still experimenting” with it. This means it tends to be deployed in pockets rather than widely across the business.
“The investment required in AI is significant, but if it’s just done in silos, you don’t gain economies of scale, you can’t take advantage of synergies and you don’t realise the cost benefits, which means it becomes a cost-prohibitive business model in many instances,” says Semach.
Another key issue here is the fact that most companies “mistakenly” concentrate on using the software to boost the efficiency of internal processes and operating procedures, rather than for generating new revenue streams.
“Where companies struggle is if they focus on process efficiencies and the bottom line because of the level of investment required,” says Semach. “But those that focus on leveraging AI to create new business and top-line growth are starting to see longer-term benefits.”
A problem, however, is that people both in IT and the business are “restricting their thinking due to their resistance to change” as well as “concerns over their own jobs and being replaced”, he adds.
“So, it’s not just about inadequate human-to-machine interaction – it’s about companies not adopting the right strategic mindset and approach. The issue is that people are not actually truly understanding what AI can enable in order to support the business strategy, business change and potential disruption for good.”
Another consideration, says Angela Eager, research director at TechMarketView, is that adopting AI involves a steep learning curve, but most UK organisations are “fairly early on in the maturity curve”.
One of the main challenges they face relates to data, and how clean, accurate and “aligned to your purposes” it is. A key issue here is that it takes time and effort to develop and train suitable data models, especially given that there are currently few tools to help – although MLOps (machine learning operations) is starting to prove its worth here.
“A big stumbling block today is how to operationalise AI, get it into production and keep it relevant once it’s in production,” says Eager.
She explains that when creating a data model, it is important to ensure the data is “fresh and appropriate” and suitably cleansed.
“But you also need to know how to train the data model, change it in-flight and manage the lifecycle once it is deployed – and not just as a one-off,” says Eager. “Data changes all the time, so you have to constantly ensure it is generating the right business outcomes, and change things quickly if it isn’t.”
Doing so requires not just having access to the right data, but also the right skillsets, both at the technical and more general data analysis level. This means upskilling may be needed in parallel with any technical initiatives, not least to educate business users and help them understand possible use cases.
But such activity also needs to take place as part of a wider change-management initiative to help employees address their fear of, and resistance to, transformation. Just as important is creating a centre of AI excellence, or AI function, with an enterprise-wide remit to oversee the creation of synergies between different business functions that lead to economies of scale.
“Ultimately, this isn’t just a technical project,” says Semach. “It’s about generating cultural change, which means that putting people first is absolutely key.”
