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What has changed is that growth is no longer necessarily something to be pursued at all costs. “Organizations have always sought to grow and provide shareholder returns,” says Chris Murphy, Chief Client and Revenue Officer, ������. “But they are doing so in a much more focused and intentional way now than three or four years ago.”��

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This trend is changing approaches to investment in technology, ��which is increasingly viewed as vital to expansion, but is also being assessed more rigorously.��

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“We're coming off a post-pandemic technology surge,” Murphy notes. “There was a rush to reorganize business towards a more virtual model, which had the effect of bringing forward a lot of technology spend. But with the costs of capital rising, many of the business cases that previously made a lot of sense now don’t. Companies have had to rapidly reorganize around the question: What does return on investment look like in a new world where economic uncertainty is higher and money is suddenly more expensive?”

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“People are still cautious with where they're spending their money, and it's hard to get businesses to commit to longer-term projects because of that,” says Andy Nolan, Director of Emerging Technologies, ������. “They want to see results early and to prove value quickly.”��

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In this edition of Perspectives, ������ experts set out the blueprint for a growth-supportive tech strategy that keeps spending and ambitions in check while delivering value where it counts, enabling the business to strike what Murphy frames as a delicate balance.��

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“Almost no organizations have the luxury of ignoring cost and complexity,” he says. “But at the same time, not one has the luxury of stepping back, of not focusing on the customer, or of not driving new growth opportunities.”��

“Some of the numbers that we're seeing thrown around give businesses a false sense of AI’s potential benefits,” Nolan says. “We're in a window of opportunity now where organizations need to quickly demonstrate tangible value. In the next six to 12 months, we’ll probably start seeing the tide turn a bit on AI adoption. Funding may dry up for some of these initiatives, or attention will pivot to other areas of the organization.”��

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Extracting value from AI depends above all on ensuring AI and tech investments are linked directly to business goals. “Technology strategy needs to be at the heart of your business strategy, and they need to be co-created,” notes Mason. “Every organization is trying to bring those things together to get better alignment, and also better insight from technology as to how it can be applied to their business.”��

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In the most effective organizations, this alignment is reflected in leadership. “Technology essentially requires two functions,” Murphy explains. “The first is strategy -�� linking the technology landscape with the business landscape, so that you're building the right digital products and assets to achieve your organizational goals. The second is execution; that is, having the capability and the knowledge to deliver those high-quality digital products and assets to market quickly. In businesses that achieve both, you often see technology leadership on the board, reflecting the importance of understanding the changing technology landscape.”��

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When clients ask for help applying AI to their business, ������ “tries to understand the business strategy first and foremost, and based on that, identify opportunities to leverage AI to accelerate that strategy,” Nolan explains. ”Looking at the organization with an AI lens can be helpful, but it also can be dangerous, because you might chase after things that are distractions. If you can tie your AI project back to strategy, it will get the visibility, the support from leadership and the funding that you need to make it happen.”��

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AI initiatives tend to fare poorly when the integration with business strategy is absent, and “everyone’s running off and doing their own thing,” as Nolan puts it. Such projects may not only fail to support growth; they can actually cause more friction.��

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“AI on its own is not necessarily going to solve an organization’s problems – but it will push on the sore spots,” says Mason. “If you don't have scalable platforms or clean data that's able to move between systems, AI is unlikely to fix that for you. You can get teams across a company all spinning up small AI experiments, pulling in different directions, not sharing learnings, maybe even spending a lot of money through duplicated efforts. That’s a technology governance problem, made worse by the excitement around AI.”

This is feeding a new focus on data strategy and governance – or “understanding how you can ensure the integrity and the origins of data, right through to modern mindsets around treating data as a first-class product that can be enabled as an accelerator for a company's progress,” as Murphy puts it. “All that supports the rise of data platforms as a foundational organizational asset.”��

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“There's a renewed question about who should be in charge of data, whether it’s a chief data or chief AI officer,” Mason notes. “It’s definitely not one size fits all; the important thing is to have clarity around who is going to be driving data initiatives.”��

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Nolan has witnessed a welcome rise in businesses setting up data and AI governance groups to ensure new tools are adopted “in a sensible way, so that they don't have issues with ethics and bias, or front page newspaper articles written about them.”

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To enhance both access and transparency, ������ advocates a decentralized approach to data “that maps to the reality of data within a business, which is that it is owned by different parts of the organization, with different kinds of expertise,” says Mason. “The democratization of data access, thinking about data as a product with a set of consumers, is increasingly a critical way to succeed.”��

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However, achieving this requires a similarly integrated and decentralized approach to infrastructure, which for businesses running on aging legacy systems is easier said than done.��

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“Technology is a key enabler for most organizations in growing and achieving their business objectives,” says Murphy. “But it can also be a key bottleneck. Decisions over decades have added cumulative accidental complexity. Adding new functionality and bringing new products to market that are critical to achieving growth can be a time-consuming process because you’ve got to integrate with and remain reliant upon this miasma of legacy applications.”��

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While turning technology into an accelerator isn’t an overnight process, Murphy notes ������ has seen many clients achieve success through a ‘thin slice’ approach. This entails “building out slices of new functionality that realize value for the organization, that show return on investment while also modernizing key components of the legacy estate,” he explains. “That's particularly relevant given the normalization of the cost of capital.”��

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Emerging tools like ������’ CodeConcise allow organizations to harness AI to assist with a modernization process that has become both urgent and continuous.��

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“We’re working with a number of customers who have large legacy code bases, combining GenAI with more traditional analysis techniques in order to build knowledge graphs that represent and enhance understanding of the legacy system,” Murphy explains.��

One of the most important, yet rarest, skills ������ experts see is the ability to position and explain technology in a way that makes clear to the business how it can support growth strategies.��

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“There's a big distance between seeing an ad for a solution and actually getting it to work within your organization, so that actual business people who understand your business process get value out of the tool,” says Mason.��

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Having technologists who can serve as a bridge with the business is especially important in what Nolan cites as the toughest aspect of talent development – uplifting skills at the C-suite and board levels.��

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“Not having someone in the room that really gets the technology and how it works can be dangerous, because people buy into the hype and problems start to bubble up,” he explains. “It doesn’t have to be a technologist, but you need someone with an intuitive understanding to help set up a sustainable AI strategy, explain the importance of data governance and ethics and bias, and get things on the right track.”��

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Setting the tone from the top can encourage the rest of the organization to learn about and experiment with AI in a responsible and safe way.

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“That means actually providing AI tools, not walling yourself off from ChatGPT and insisting no one’s going to use it,” Mason points out. “It’s better to provide approved tooling where you have an idea of what’s happening with data, and there are guarantees that if someone uploads a company PowerPoint, it's not going to be used to train a future version of an LLM.”