5 ways to achieve AI transformation that works for your business

The experts have declared that we are leaving the age of digital transformation and entering a new era of AI transformation.

The good news is that the groundwork from earlier digital change programs, from cloud computing implementations to forays into machine learning, will provide a strong bedrock to build new AI transformations.

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However, the shift to an age of AI will involve overcoming significant technological and cultural challenges.

Here are five ways business and digital leaders can embrace those challenges and deliver successful AI transformations.

1. Don’t work in a silo

Helene Kollnig, Freshworks global applications lead at recruitment specialist Hays, advises professionals to seek advice from as many experts as possible.

“Look at what other people are doing,” she said. “Never work in a silo and prepare to be wrong in terms of how you’ve set the technology up.” Kollnig and her colleagues have implemented the Freshworks Customer Service Suite, an omnichannel support software with AI-powered chatbots and ticketing.

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She told ZDNET that working closely with the technology partner has helped her team to deliver a successful AI transformation. “So, for one of our AI projects, we established our basic set-up and said, ‘Freshworks, come in and audit it. Tell us, are we doing this right? Would you do it differently?'” she said.

“They came in, looked at our set-up, and said, ‘This area is great, but you need to consider these things.’ That type of support helped us. So, my advice to other professionals is to ask for help.”

2. Build the confidence of others

Nick Woods, CIO at airport group MAG, said leading an AI transformation isn’t straightforward, and executives must ensure their organization makes a successful cultural shift.

“There’s a big hearts-and-minds piece to this work,” he said. Woods explained to ZDNET how he’s working with startups and combining insight with sensor data to develop the AI-enabled future of air travel.

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He’s exploring how the group’s technology can assist with seasonal planning for the airfield. This task is traditionally a manual process, so employees must be shown how automation benefits staff, the organization, and passengers. “People have worked in this area for many years and done things in a certain way for a long time. We must take the internal customer on a journey and prove the benefits,” he said.

“The journey is about building their confidence and showing them how this technology can help improve their jobs and deliver outcomes. We’re making good progress.”

3. Get the business to generate ideas

Anastasiia Stefanska, data analyst for analytics and AI at travel giant TUI, recognizes anyone can suggest great ways to exploit emerging technology.

“Everybody at TUI, not just everybody in IT, is enabled to work with AI at the level their role expects,” she said. “We’ve been working on that approach for the past year. We want to bridge the gap between the business professionals and our technological knowledge in the data team.”

TUI uses generative AI (gen AI) for data analysis and chatbots in training programs. The company also uses Cortex AI, Snowflake’s large language model (LLM).

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Stefanska told ZDNET that TUI had run cross-business initiatives in a gamified format to help surface new use cases. She said the best ideas from these sessions have been selected to be implemented and will produce significant time savings for the organization.

“As part of the project, I was astonished to see how deeply the business users have thought their ideas through — they just needed to be asked,” she said. “When we asked them, and they were comfortable answering, the ideas started popping up like mushrooms in the rain. It was hard to choose what to prioritize.”

4. Be sensible

Dave Moyes, partner for information and digital systems at architecture firm SimpsonHaugh, said every business and digital leader must prepare for AI transformation.

“It’s coming,” he told ZDNET. “You can’t bury your head in the sand and ignore AI.”

Moyes said professionals in all sectors should take some sensible steps, including working with people who know more about AI.

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“Within every organization, there are groups of technology leads who are interested and want to innovate, evolve, and push,” he said. “Lean on them. Learn from those at the coal face who want to do AI. There are no guarantees that the technologies you introduce will be the next best thing, but at least you’ll be aware of the potential.”

Moyes said SimpsonHaugh is looking at how AI can reduce time-intensive tasks, such as summarizing text, and help staff find images to create early-stage design proposals.

The firm also is considering how gen AI can support the production of parametric designs, where buildings are shaped using algorithmic processes. Across all these areas, the security of client data will be key.

“For us as an organization with sensitive data, the use of any tool needs to be framed in terms of, ‘Actually, that project is covered by an NDA, so don’t put it anywhere near the public cloud,'” he said.

“We’re going to manage the sensitive information carefully. We’ll set the boundaries rather than throw a model in and let it sniff everything.”

5. Work within your constraints

Roger Joys, vice president of enterprise cloud platform at Alaskan telecom company GCI, said his organization is eager to explore AI in the right circumstances.

“It’s everything from simple use cases, such as using chatbots to help reduce our call center costs by helping people self-serve, to more sophisticated data analysis in customer demographics, merging many different pieces of data, and being able to answer questions about, ‘Who are the best candidates to throw a marketing campaign at?'”

However, Joys told ZDNET, it’s important not to rush into AI transformation. Yes, the benefits can be great, but so are the risks if you don’t prepare for data-led change.

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Joys is using VMware Cloud Foundation private cloud technology and a host of other services to create a scalable and safe platform for business innovation. “The data scientists have moved some of the data that is OK to be in the public cloud into Databricks on Azure, but we can’t upload all our customer information,” said Joys.

“There are regulations about the data we can store in the public cloud safely, securely, and privately.”

Joys said his organization faces many regulations, particularly regarding call data and customer information. Any decisions on AI must be made with these concerns in mind.

“Those are things that will be determined,” he said. “Where is public AI okay? We’ll be working on that issue over the next 18 months.”