Generative AI – from experimentation to catalyst for growth

Generative artificial intelligence (GenAI) brings together the endless possibilities of data with the vastness of human thought. Artificial Intelligence is not a new phenomenon. Whether that’s looking back to 1941 with Alan Turing building an analogue computer to crack the Enigma cipher during the second World War, or IBM’s Deep Blue defeating reigning world chess champion Garry Kasparov in 1997. What is a new phenomenon is the advent of GenAI self-service tools, democratising knowledge for consumers.

Despite this, organisations are grappling with how to deploy the technology to drive their business forward, while also balancing the risks and responsibilities associated with it.

A recent Deloitte GenAI Pulse survey of over 2,800 business and technology leaders globally showed that 79 per cent of respondents expect GenAI to transform their business within three years. But in practice, they are most focused on gaining practical benefits today – improve efficiency and productivity (56 per cent), reduce costs (35 per cent), and improve existing products and services (29 per cent).

In my day-to-day work with clients, I am seeing many more organisations attempting to move from isolated testing and boardroom strategy to deploying GenAI models and products at scale into frontline systems, processes, and teams.

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The tech industry’s big players are investing heavily in research and development to improve the accuracy and effectiveness of GenAI, as there is a growing demand for these types of solutions across various industries. Increasingly major tech implementation programmes are now embedding GenAI in areas from code generation to automated testing procedures. Models are being integrated across the business. Some recent examples we in Deloitte have worked on include, in retail, developing creative tools to assist with content creation, in healthcare, to develop personalised treatment plans for patients, and in financial services to analyse market data for more informed investment decisions. This demand is driving innovation and competition, with many start-ups and smaller companies entering the market; further democratising the technology.

Looking beyond use-cases to value-cases

Businesses must look beyond ‘quick-win’ or ‘rapid-prototyping’ use cases to use the technology to drive value that is tied to key business objectives. For example, take a customer support or contact centre team. Some common use cases in this domain focus on call summarisation, transcriptions, order fulfilment, and customer query responses which can drive significant efficiencies and time savings. However, there is considerable added value when the operating model can be enhanced to improve customer experience. The same solution can drive outbound customer interactions such as generating drafts of new content personalised to their needs. So, the business goes from developing a use case to re-engineering how support is provided, to improving customer experience and subsequently driving new revenue streams. GenAI will deliver real value when the structures are set up to complement existing AI skills, capabilities, and tools.

Some questions for organisations to consider as they lay the foundations to drive value:

• Strategy – Are our AI ambitions aligned with our overall strategy? Do we have buy-in from senior stakeholders? Is there ownership on costs and outcomes?
• People – Are the right people leading and engaged across the organisation today? Do we have the specialist skills in the right areas? Are we educating, training, and developing our people for adoption? Are we adopting a human-centred design with effective change management in place?
• Data – Is data a real enterprise asset and do we manage it as such? Are we adopting the best data governance practices?
• Process – Are efforts being focused on the right projects to deliver the greatest value versus risk? Are we creating the right ecosystem (eg alliances, partnerships, R&D) to drive further innovation?
• Technology – Are current AI solutions in line with best practices? Are the technology capabilities enabled to deliver our ambitions? Do we have the technical ecosystem to move from proof of concept to an industrial solution?

Governance is more important than ever – risk, privacy & ethics must be front and centre

The Deloitte GenAI Pulse survey showed that only a quarter (25 per cent) of business leaders believe their organisations are “highly” or “very highly” prepared to address governance and risk issues related to GenAI adoption. Respondents’ biggest concerns related to governance and a lack of confidence in results (36 per cent); intellectual property concerns (35 per cent); misuse of client or customer data (34 per cent); ability to comply with regulations (33 per cent); and lack of explainability / transparency (31 per cent). Without effective governance guardrails, AI can’t scale. A governance framework should define the business’s vision, identify potential risks and gaps in capabilities, and validate performance. With the EU’s AI Act on the horizon, businesses must focus on readiness, and a governance approach that enables sustained adoption. This becomes even more important as organisations strive to ensure consistent end user experiences while adopting different approaches to regulations across the globe. Ensuring proper and compliant use of GenAI within the organisation is everyone’s responsibility.

Data remains the fuel that drives the Generative AI engine

Successful GenAI projects rely on large amounts of data to work effectively, so having a data strategy is imperative. Data must be trustworthy, secure, accessible, and organised so that your GenAI tool can produce meaningful insights and outputs. Organisations must ensure the collection and curation of proprietary data, as this is key for tailored use cases that provide a differentiator or competitive advantage. Organisations across all industries are now embarking on ‘data modernisation’ journeys as part of their AI transformation. This is a key trend across a lot of financial services institutions across Europe for example. Organisations are continuing to modernise their infrastructure and centralising data repository by collecting data from across the enterprise including customer, regulatory, financial, HR, operations, and other data (both internal and external).

Human augmentation vs. human automation

Humans and AI-based technology will work symbiotically where both groups influence control, creativity, and outcomes. Commonly I hear the fear that GenAI will reduce the need for (or perhaps more accurately, diminishes the worth of) human creativity. My observation is that the opposite is true; in an age of creative machines, creative humans matter more than ever. GenAI is not meant to replace humans, but to better unlock human potential – just as technology was always meant to do. The need for humans didn’t diminish with the invention of the personal computer. They got better and faster at accomplishing work.

Lack of technical talent and skills was cited as the single biggest barrier to GenAI adoption in the Deloitte GenAI Pulse survey, with only 22 per cent of business leaders believing their organisations are “highly” or “very highly” prepared to address GenAI adoption talent-related issues. Many are not yet focused on education and reskilling. Only 47 per cent of organisations agree that they are sufficiently educating their employees on the capabilities, benefits, and value of GenAI. Businesses must begin futureproofing the GenAI-enabled workforce because workers will need new skills.

GenAI is at an inflection point where business leaders now recognise its potential. However, real competitive advantage will only materialise when businesses transition from experimenting to scaling solutions, with GenAI becoming a catalyst for growth with trust and ethics at its core.

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