How your business can best exploit AI: Tell your board these 4 things

Emerging technologies promise big benefits on paper that can be tough to realize in practice. Research from the Capgemini Research Institute suggests the adoption of generative artificial intelligence (GenAI) is still at an early stage, with nine in 10 organizations yet to scale these nascent projects.

However, boards exert increasing pressure on CIOs and their teams to gain a competitive advantage from innovation. So, what is the key thing business leaders have learned about AI so far? Four business leaders share their tips.

Also: Time for businesses to move past generative AI hype and find real value

1. Ensure you keep a human in the loop

Miguel Morgado, senior product owner for the Performance Hub at Eutelsat Group, said his firm's use of AI and machine learning is related to outage predictions and root-cause analysis, such as the effect of weather on a satellite dish.

These explorations into emerging technology show the importance of high-quality information.

"We do lots of tests with real data," he said. "And validating the models is very important. Because if you don't have an accurate model and then use it, it will be a case of 'garbage in, garbage out'. It's important to have a good data set."

Morgado said his business is lucky -- the satellite company collects billions of rows of data daily for various use cases. However, the firm ensures this information is applied safely and effectively.

"We still need to test models and the results over and over again until we validate the approach," he said. "The results won't be perfect -- there will always be a degree of imperfection. But it is an indication."

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Morgado told ZDNET that other companies should ensure a skilled expert stays in the loop and communicates the significance of outputs to business colleagues.

"Then you can get that person to say these results indicate a particular value or could potentially be taken as guidance," he said. "So, it's always, in the end, the user who decides if they trust the AI or not. My advice to other people is to ensure there is always a human element to your AI results."

2. Get senior buy-in for organizational change

Ulf Holmström, lead data scientist at Scania Group, said his company is exploring how it might use AI for internal support processes.

The company is investigating how to make the most of Amazon Bedrock and is keen to explore how it can use some of Snowflake's tools, including Cortex AI.

Like other business leaders, Holmström pointed to the importance of underlying data and technology concerns.

"Call it whatever you want to call it, but you need to have trust in data and infrastructure and governance, otherwise you can never scale, and you'll only do proof of concepts. And like other organizations, we need to put stuff into production."

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Holmström told ZDNET the good news is the implementation of technology seems to get easier all the time. Access to technical knowledge has been democratized through technologies like the cloud and generative AI.

However, Holmström said new processes must be introduced for users to make the most of emerging technologies.

"If we're going to implement AI in production, that comes with implications -- and one of the big implications is that we need to change our way of working. It means new business processes and a new type of organization," he said.

"We need to have new skills and we need to change our way of working. That shift is difficult in all organizations, especially in legacy enterprises. But without that transformation, AI will never fly."

Holmström said senior executives must drive the move to this new way of working. "Top management commitment is crucial," he said. "You can never do an AI transformation bottom-up. It must come top-down."

3. Remember the existence of real-world biases

Anastasiia Stefanska, data analyst for analytics and AI at holiday firm TUI, said it's important to think about how we can turn the huge quantities of data we collect into high-quality information -- and that task requires a recognition of human biases.

Like other professionals, Stefanska recognized that ensuring your organization has high-quality data is a prerequisite to any successful AI project.

However, it's not the only key issue -- smart professionals will ally a focus on data-quality concerns with a consideration of real-world biases.

"AI is a simplified reflection of the real-world reality in which we live," she said. "On one hand, driving AI adoption with data quality in mind is paramount. However, a critical eye on the status quo of the real world can allow us to go beyond data quality. We can think in the direction of having an opportunity to solve the biases that are deeply embedded in the real world."

Also: 4 ways to help your organization overcome AI inertia

Stefanska told ZDNET how TUI uses the Snowflake platform to consolidate enterprise information and create a digital platform for data-led change.

As part of this work, Stefanska and her colleagues watch how data is used and exploited.

"That's why we say at TUI that the human eye is important. We acknowledge that the biases are there in the real world around us," she said.

"My main message would be, 'Yes, data quality is important, but have a holistic view on whether you have a chance to convert a quantity of the existing data into something with a new quality.'"

4. Use AI when it's right for your business

Richard Wazacz, CEO of foreign exchange specialist Travelex, advised other professionals not to walk before they can run. He recognized there's huge hype about AI. However, the fear of being left behind must not color professionals' judgments.

"At the moment, we're not going to be early adopters of AI," he said. "But we will use AI when the case study for how it's helped others has been proven."

Wazacz told ZDNET that his extensive business experiences, including as director at Octopus Energy, helped him develop a strong awareness of the times when emerging technology can play a key role.

"I worked at Octopus and they've successfully used AI to help improve customer services," he said. "A lot of customer questions are answered through AI. Do I think there's an option for us to do that now? Yeah, and because they've proven it can be done, it's less of a risk."

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Wazacz said his approach at Travelex is to draw on consultancy expertise from Mesh-AIto ensure digital investment is directed to the right places.

Mesh-AI has helped Travelex establish a cloud-based data platform, with an initial focus on real-time reporting. The company will move into other emerging areas when the time is right.

"I'm being very narrow in the scope," said Wazacz. "So, at the moment, that's what Mesh-AI is working on. They're excited about what they're doing. They're taking the approach of, 'We'll prove that we can make our customers self-sufficient because we'll win more business.' And that's what I feel like they're doing."