Business Leaders Share Strategies for Successful AI Integration and Data Management

Your AI transformation depends on these 5 business tactics

Five business leaders explain their best-practice tactics for managing artificial intelligence projects effectively. Aiming for a successful AI transformation is great, but if you can't lead the initiative effectively, you won't deliver the results the business demands. With experts suggesting increasing numbers of companies are turning their attention from digital to AI transformation, here are five ways to ensure your organization reaps the rewards from emerging technology. Also: 4 ways to turn generative AI experiments into real business value Gabriela Vogel, senior director analyst in the Executive Leadership of Digital Business practice at research firm Gartner, said digital leaders must focus urgently on exploiting emerging technology effectively. "CIOs who don't understand the focus on value -- and make promises about AI without thinking about what they are getting involved with -- might not stay at the top. They will lose power, they will lose inference, and potentially even lose their jobs." Vogel told ZDNET that CIOs often lead AI initiatives, but other executives are interested. "The CFO is taking this technology seriously and trying to understand, 'How are we going to make money out of AI?'" she said. "They're spending a decent amount of time trying to understand this area, more than many other executives within the C-suite, and so they're going to have an influence." Also: The secret to successful digital initiatives is pretty simple, according to Gartner Vogel said CFOs don't want to be the executives responsible for emerging technology, but they want to be known as the individuals who helped deliver its benefits. "So, the CFO is a great person to partner with if you want to make that shift into the business," she said. "I would say they're the best partner you could have now." James Fleming, CIO at Francis Crick Institute, said the digital leader's role in AI is to provide oversight within the IT department and across the business. "There's got to be a degree of leading from the front and making it OK for your team to think about these questions and become experts in them, and then you've got to guide the rest of the organization along that journey." Fleming told ZDNET his world-leading research institute established a working group to assess AI. This group includes representatives from across the organization, including science, operations, legal, and HR. "We posed several questions to that group, 'How should we use it? What restrictions should we put on it, if any? Is there a case for investment in any of this? Should we buy enterprise licenses for ChatGPT tomorrow, for example?'" he said. Also: Technologist Bruce Schneier on security, society, and why we need 'public AI' models Fleming said the group didn't find a killer use case that justified a huge investment in generative AI (Gen AI). Instead, the group saw smaller, point use cases for new best-practice processes. "We continue to pose those questions to the group about potential applications as we go," he said. "So, for example, the Crick is hugely multinational, and many researchers are writing grant applications in English, which is not their first language. Writing fluently can be an incredible boon when trying to get your ideas across." Bruno Marie-Rose, chief information and technology officer of the Paris 2024 Organising Committee for the Olympic and Paralympic Games, said the key to leading AI transformations is turning new personal habits into business benefits. "Maturity is crucial for the International Organizing Committee," he said. "Having an approach where you can say, 'I'm four years ahead of the Opening Ceremony and need to progress, what do you advise?'" Marie-Rose told ZDNET that one area where AI can prove beneficial is using data to help optimize the use of resources across the Olympics. Also: Generative AI doesn't have to be a power hog after all Proof-of-concept studies during the Games examined how emerging technology could be applied, including using data to optimize on-site resources. "For the Media Center, do you need a 200-square-meter room permanently? Or will this room only be fully utilized for the finals of the 100 meters? If that's true, how could we optimize the number of journalists and the space?" said Marie-Rose. "So, it will be key for the future to optimize the resources that we provide as an Organizing Committee, with all the difficulties we face, and having insight from emerging technology to enable that flexibility is a crucial part of the approach." Ollie Wildeman, vice president of customer services at travel specialist Big Bus Tours, said executives who explore AI will discover three types of people are worried. "It will be the people on the front line who think their jobs will be replaced, it will be the people who manage those guys, and stakeholders will worry about the money you're putting in and what will happen to the customer satisfaction scores in the long run," he said. Wildeman told ZDNET how Big Bus Tours uses Freshworks' Customer Service Suite omnichannel support software, including AI-powered chatbots and ticketing. As the executive leading the implementation, he has proven that emerging technology can have a positive impact. Front-line staff have seen how AI makes their work easier. "We're using our agents for more things, more value," said Wildeman. "Rather than using an agent to respond to a query that could be read on the website, we're using them to make a sale or respond in a personalized manner to reviews. The agents can see there are more diverse things to do." Also: AI is making us smarter, says AI pioneer Terry Sejnowski Managers, meanwhile, have seen a well-trained AI can be trusted to push high-quality answers to customers. "The people managers I spoke to at first said, 'Ah, yeah, but if you get an AI, it's just going to push out the same canned response to every customer. It's going to seem robotic.' However, in reality, Gen AI varies the language, so you have a better customer-facing product." Wildeman said happy customers mean happy stakeholders. His organization ensures customers know they're interacting with a bot to keep satisfaction levels on track. "We always indicate that we're using a generative system. We don't pretend it's an agent." Jon Grainger, CTO at legal firm DWF, said successful AI transformations ensure the data that feeds IT systems is well-managed and trustworthy. "You can't do stuff without your data being right," he said. "You can go much deeper -- and be much more solid on your outcomes and what you're trying to achieve -- as your data gets better." Microsoft Copilot is available to DWF employees. The rollout process starts with Teams and proceeds to Office once checks are complete. Grainger told ZDNET the technology is used to transcribe meetings and provide meeting recaps, which can be particularly useful if you return to a conversation after an event. Also: 3 ways to build strong data foundations for AI implementation, according to business leaders He said organizations can already do some cool stuff with Gen AI, even when data is unstructured -- but AI leaders should create as much structure as possible. "Generative AI might not necessarily know the difference between two identical documents," he said. "If one document has poor content and the other one has good content, the basis of probability means it might select either of those documents for your results." Grainger said placing data quality at the heart of the firm's business strategy has helped to bring structure to information management processes. "We talk about not poisoning the well," he said. "You want to have a very well-curated, contained set of content. Then you say to your Copilot, 'Don't look anywhere else apart from what's in that well.' And that approach is part of our strategy."

Integrating AI starts with robust data foundations. Here are 3 strategies executives employ

Explorations into artificial intelligence must first establish a strong base. Business leaders share three ways to build a great data strategy. Business leaders recognize that strong foundations are essential for any company exploiting artificial intelligence (AI). Your business could jeopardize the whole project if it doesn't sort its data strategy before explorations begin. In short, if you put garbage in, you'll get garbage out. Also: How to level up your job in the emerging AI economy So how can professionals create the foundations to help their organization use AI safely and successfully? Three business leaders detail their top tips for building an effective strategy for exploiting emerging technology. Claire Thompson, group chief data and analytics officer at insurance giant L&G, said a strategic approach to information is crucial for any company that wants to innovate: "I always say data foundations are important for whatever you do next." She told ZDNET that strong foundational elements link rules and regulations to dollars and cents. "Make it clear how the data strategy will drive tangible value -- why is it important, for example, that your email addresses are up to date and accurate so that you can do targeted digital communications?" Thompson recognized that many people don't want to get bogged down in a long-term strategic plan that defines the technology, processes, people, and rules required to manage information assets. However, she said the planning stage is critical to reaping the benefits of technologies like AI. "I can understand why people might say governance is boring," she said. "But in today's digital organizations, where people want to do straight-through processing, it becomes even more critical that your data is good quality. So, all roads are leading to governance." Also: How your business can best exploit AI: Tell your board these 4 things One key element of Thompson's strategy at L&G is a close working relationship between her data team and the IT department. Effective collaboration relies on clarity about the skills each party brings to the relationship. "You need a hand-in-glove partnership. Technology is hugely important to what we do in the data space, and we can't do our work without the cloud environments, the data warehousing, and the tooling. Data is held in all the applications that the IT team maintains," she said. Also: 3 ways to help your staff use generative AI confidently and productively "We're trying to ensure we do data quality by design. That's about ensuring we embed the design philosophy into our core systems. The more you can do that work, the more it stops the ripple effect of poor data quality further down the line and prevents any remediation effort." Thompson said the data they collect will push customer experiences in new directions: "How do we start to build personalization into our mobile applications? How do we start to build that into our asset management? How can you automate trades and use AI to support that process?" Jon Grainger, CTO at the legal firm DWF, said there's no time like the present when it comes to creating a data strategy. Smart business leaders focus on the foundational elements for data use long before they think about how to exploit AI and machine learning. "I always say the best time for a data strategy is four years ago," he said. "It's a supertanker piece of work. Ultimately, there aren't many shortcuts. There is a view that says, 'Well, if it's going to take that long, why bother?' And I think that's why many folks haven't been able to get to grips with their data." Grainger told ZDNET he wants his firm to build a reputation for delivering great experiences through a digital transformation -- and a data strategy is a crucial component of that approach. Also: What is digital transformation? Everything you need to know He joined DWF in late 2022 and enacted a new strategy based on cloud-based software-as-a-service (SaaS) products and open application programming (API) interfaces. Data at the firm covers a range of entities, such as cases, partners, clients, and internal business processes, including billing and financials. "The data strategy is all about ensuring transactional data -- the source of truth -- is mastered in those sections." Also: 98% of small firms are using AI tools to 'punch above their weight' The aim is to help the organization move quickly but not to the detriment of quality or cost. "Each SaaS product has a clear identity on the enterprise map," Grainger said, explaining the fine points of his data strategy. "That identity is driven by the data you master in each area." He said the "absolute minimum requirement" to get onto the firm's target architecture is well-developed APIs that DWF can access and use. Grainger said SnapLogic technology ensures a solid and reliable connection between services, API, and users. Also: Agile development can unlock the power of generative AI - here's how "Invariably, you'll get 15 different spellings of a particular address, and the technology can see that pattern and correct it," he said. "It can also do something called enrichment. So it might take someone's reference, go off to an API, come back, and say, 'This is the right information.'" Grainger said the data strategy also focuses on the models DWF creates to answer its key business questions. In combination with the firm's concentration on SaaS products and APIs, the business has solid foundations to explore emerging technology. "It turns out you're setting yourself up pretty well for generative AI if you've got all those elements in your data strategy." Nic Granger, director of corporate and CFO at North Sea Transition Authority (NSTA), said a great data strategy goes beyond internal working practices and spans organizational boundaries. NSTA collects data from the oil and gas sector. Granger's team has created digital platforms that allow industry, government, academia, or other interested parties to access data openly. As part of that work, she chairs the Offshore Energy Digital Strategy Group (DSG), a specialist body formed in late 2022 to create a collaborative effort across UK public bodies that deal with data collection in oil, gas, and renewables. Also: The future of computing must be more sustainable, even as AI demand fuels energy use "It was recognized that we needed a cohesive digital data strategy across the offshore energy sector," she told ZDNET. "There were good pockets of excellence across the industry in data management and digital technologies, but they weren't necessarily talking together. So that was a big priority for us." In addition to UK government departments, the DSG is supported by other contributors, including the Open Data Institute and Technology Leadership Board. Also: The fall of Intel: How gen AI helped dethrone a giant and transform computing as we know it Granger said this collaborative approach has paid dividends: "We've got the data strategy now, and it's about working on three key streams of work." The first stream focuses on data, standards, and principles: "Making sure the underlying quality of the data is good because we're all working on the same basis." The second stream looks to create common data toolkits and interoperability, said Granger. "It shouldn't matter if you're working in an offshore energy company or on a project in an oil and gas company, you should have data that's useable across the platforms. That work is all about, 'How do you get that data from A to B without duplication?'" Also: The best AI for coding in 2024 The third workstream focuses on cross-sector digitalization: "That's about ensuring the data and digital skills are there across the industry, and ensuring the sector complies with cybersecurity best practice." With these data foundations in place, it's much easier to start thinking about how to make the most of emerging technologies. "Our focus is on ensuring we're making the data accessible and in the right formats for others to use AI and machine learning," said Granger.