Balancing AI innovation with business as usual

Even so, this activity is still largely happening in pockets – an exploration here, a pilot there. These experiments aren’t yet integrated into organisations’ strategic heart.

Understandably, there’s apprehension about how much disruption AI could cause. There’s uncertainty about what to prioritise and what to focus on. And there’s wariness about the broader implications and how to navigate them. Not surprisingly, stakeholders from employees to shareholders want clarity and answers.

But, in feeling their way into an AI-led world, many organisations still separate AI’s role in improving business-as-usual activities from its potential in higher-level innovation. In effect, they’re using a revolutionary, transformational technology to create a faster version of today’s model and processes, while bolder steps are still theory, not practice.

In reality, small-scale, constant improvement and genuine innovation are two sides of the same coin. To bring them together, leaders need to do what amounts to changing the engine while still driving the car. It’s about re-examining what the business is for, and what it could be for with AI at its heart. This is a fundamental part of becoming an intelligent enterprise, and it means looking hard at capabilities, organisational structure, and the rise of power skills.

Uncover new purpose and capabilities

AI can disrupt existing capabilities, and already is in some cases. Take learning and development, for instance. Traditional digital learning requires significant upfront effort to prepare and develop online sessions, often months before they are used. With AI, learning can be curated on-demand, in real-time, and tailored to the learner's immediate needs. AI leverages internal knowledge, external best practices, and policies to create relevant learning experiences. This approach benefits organisations by eliminating upfront investment and ensuring up-to-date training. However, it poses a threat to many training providers who rely on traditional revenue streams.

This scenario, and countless others like it, show why all organisations have to examine the upsides and downsides of AI, and project what they mean at a strategic level for the organisation’s purpose. Will its capabilities and differentiators still matter in two or three years?

While one source of revenue could shrink or disappear altogether, combining analysis with innovation could reveal a new capability, and with it a fresh opportunity and revenue stream.

By harnessing expertise in new ways and combining it with AI, a training company, for example, could drastically cut its costs from say, the £30,000 it currently takes to create 60 minutes of learning content. It could build a different kind of proposition that’s more in tune with customers’ needs and set itself apart from its competitors to accelerate market share.

Match AI to your organisation strategy and people structure

An intelligent enterprise is fundamentally different to a digital business. In the former, you’ll no longer just be using data to sharpen up the processes behind your existing model. Instead, AI will power your whole organisation so that the line between business-as-usual and innovation becomes permeable. To support this strategy, you’ll need a workforce that enables making AI implementation real. Achieving it means looking at existing workflows to see how and where skills and tasks add value and generate revenue. You’ll then need to ask where AI can release value and let you redeploy people in new ways with newly-designed roles. In parallel, you’ll need to map the impact of this on your people’s career paths, the way you identify new roles, and how you recruit to fill them.

Winning the hearts and minds of the workforce is imperative. Buy-in from the entire workforce is crucial for success. When we worked with Unilever to design and build an AI platform called DelphiAI, the goal was to accelerate R&D initiatives and create market superiority. Co-creating a strategic narrative for AI adoption and scaling as part of an effective change management programme was central to its success. This collaborative approach ensured that the workforce was fully engaged, facilitating adoption to maximise the impact of AI in R&D.

Turn soft skills into power skills

AI can turn hard-won knowledge and analytical capability into commodities. It can even generate creative concepts. Little wonder that people, especially those with analytical skills who we think of as ‘knowledge workers’, are nervous about what AI means for them. But there are some skills where humans still have a monopoly, and always will - the ability to influence, persuade, and win over hearts and minds.

Even where advanced AI is able to replicate some soft skills, such as Chat GPT-4o with empathy, we will see the application of other soft skills turn into power skills to build and deepen relationships even further. Power skills will encompass greater creativity, adaptability, and innovation mindsets combined with many of the traditional soft skill attributes to give human interactions the winning edge. As organisations invest in large language models, use them to pass information between them, and build homogenous service experiences, the evolution of soft skills into power skills will be key to standing out and accelerating competitive advantage through innovation.

Digital organisations strove to move away from face-to-face interaction with customers in the search for efficiency. But the winners in the emerging world will be those who restore the personal experience by investing in their people’s uniquely human skills, in addition to AI competencies.

AI is undeniably ushering in a new age, and organisations who grasp the opportunity to transform – and not just finesse themselves – will be most likely to thrive. To get it right, leaders must bring innovation into the centre of their thinking while being ready to rethink their capabilities and invest in redesigning their people’s roles.