Plugged In or Left Out? Navigating the Emerging Generative AI Gap

Generative AI is the buzzword of the moment. Attend any industry conference or skim through the latest Gartner hype cycle, and you’d be forgiven for thinking we’re on the cusp of a generative AI utopia. Everyone’s doing it. Everyone’s winning. Everyone’s transforming their businesses at warp speed.

Except, well… they’re not.

Here at Oakland, we’ve had some revealing conversations while compiling this report on the readiness of major organisations to implement generative AI. Spoiler alert: the reality isn’t quite keeping up with the hype.

Generative AI could be game-changing. But as this report reveals, getting there will require focus, courage, and a clear view of both the opportunities.

Read our full guide on generative AI below.

Generative AI has been billed as a ‘digital workforce that doesn’t need vacations or other benefits’ (Gartner, 2024). A new team member who can do things with or without us. Able to upskill your existing workforce and quite frankly do things that no human would have the time to do.

This all sounds like a productivity gold rush, but do business leaders, by now accustomed to the reality of generative AI, trust it to deliver these promises?

When asked to rate their overall trust in generative AI technology, almost 63% of leaders had either a moderate or high level of trust in the technology. This is largely down to a combination of technological advancements, use cases, and shifting perspectives about generative AI capabilities.

However, this wasn’t the case for all, and a significant proportion, 37%, still have a way to go before they can completely trust the technology.

”Generative AI is transforming the way we work, with data flowing from every direction—spanning business apps to previously inaccessible unstructured data. The key challenge is determining who controls and accesses this wealth of information, driving a boom in generative AI governance.” – Oakland Generative AI Report 2024

Overcoming barriers to adoption can be one of the toughest challenges when selecting and implementing new technologies within an organisation. Readiness is one of the most crucial to consider: an effective and beneficial transformation requires firm foundations or projects can easily overrun or underdeliver. After years of digital and data transformation, companies are questioning the return on their technology investments. Now, it’s up to generative AI to prove its value.

To work out how ready decision-makers feel, we asked them to assess their organisation’s readiness for generative AI adoption. On the whole, leaders took a pragmatic view of generative AI technologies. 5% reported being very prepared with a clear strategy and resources to hand (not surprisingly given the number of organisations without a data strategy) while 29% were somewhat ready, exploring options and building capabilities.

Just over 31% were in the research phase and were looking for information and guidance, which highlights the need for trusted advisors who put the customer before the tech vendors.
Almost a third (30%) said they were simply not interested in generative AI adoption at the moment, which is interesting given the amount of noise in the market.

Since the generative AI boom kicked into overdrive half a decade ago, many organisations have been experimenting. However, since the much more recent introduction of generative AI capabilities; are these same organisations successfully navigating the new challenges of implementing generative AI, or are obstacles slowing down adoption?

Asking respondents at what stage their organisation was on its “generative AI” journey, over half recognise they are already on the generative AI journey, whether that be utilising existing tools like ChatGPT and Co-Pilot (22%), exploring options (26%) or building custom solutions (6%). But, with this market changing so quickly, this is likely to change by the time you’ve finished reading this report!

With just over a quarter of businesses still exploring generative AI options, the complexity and choice between vendors, capabilities, and pricing appear to be barriers to confident adoption.
Many are dipping a toe in the water by using entry-level generative AI tools, such as ChatGPT or Microsoft’s Copilot, with few committing to custom generative AI solutions that deliver more transformative change. This is not unusual in our experience: larger, tech-centric businesses tend to explore custom solutions, while SMEs and non-tech businesses are more likely to wait and see what works for others.

Find what Oakland thinks about Gen AI, when you download the full report.

Whilst the newer generative AI can drive efficiencies and create opportunities to improve revenue, these will always require investment to make sure they have an impact. With the technology no longer in its infancy, are businesses investing in it? And if so, how are decision-makers carving out funds for initiatives?

When these questions were put to our respondents, almost two-thirds (63%) had not yet allocated any budget for generative AI development in their organisations.

Of those that had assigned a budget, nearly a fifth (17%) were using existing technology budgets, 5% had a dedicated generative AI budget, and another 5% were reallocating budget from elsewhere. The overwhelming majority had not allocated any specific generative AI funding which goes back to our earlier point around data leaders being asked to deliver more with existing budgets.

The hype surrounding generative AI has been at a fever pitch for some time now, but has the positive press and excitable generative AI evangelism on social media translated into increased investment? Have the benefits of generative AI earned the technology a right to a greater slice of the pie?

When asked, half of respondents had no specific budget allocated for generative AI.

A very small proportion of organisations (2%) had a significant generative AI budget (more than 20%), which aligns with what we are seeing in the current market.

For organisations looking to get started and begin to productionise generative AI solutions there are several ways forward. Developing in-house capabilities with existing teams, hiring in additional generative AI expertise, or using external consultancy services are all possibilities, each suiting different organisations and use cases. We asked organisations which they were pursuing.

Of the 54% of respondents in the process of developing generative AI, 24% had tasked an existing internal team with the project. 10% were using external consultancy services. And another 6% had hired generative AI specialists to guide development.

Rather than creating separate generative AI departments, most organisations are embedding generative AI capabilities within their existing data teams. This aligns with our belief that generative AI is most effective as part of a broader data toolkit.

However, a significant challenge arises from the shortage of generative AI experts, particularly for organisations looking to implement generative AI solutions. The rapid evolution of this technology has outpaced the supply of skilled professionals, creating a highly competitive market for talent.

Generative AI technologies and capabilities are developing at a breathtaking pace. What was only recently possible with generative AI only a few years ago has today transformed entire industries.

Do businesses intend to focus on generative AI over the near term to stay ahead of the curve? If so, what do they think this change will look like?

When polled, only a fifth (21%) were planning to use generative AI to either significantly or transformatively change their business, contradicting many online articles and publications intimating industry scale change is upon us.

The majority (37%) of respondents today said they were planning to implement minimal change using off-the-shelf tools like ChatGPT and Microsoft Copilot.

Finding the right use cases for generative AI is critical to ensuring success. Yet, while all businesses are unique, generative AI uses typically fall into a limited range of categories.
When asked which was their organisation’s primary use case, knowledge management was by far the most popular, chosen by 36% of respondents.

Operations and process optimisation (31%) was the second-most popular use case, followed by marketing and sales (30%), customer service (28%), and research and development (24%). 14% reported another use case.

Read our blog, on how to find generative AI use cases for enterprise.

Our research confirms the importance of clear use cases and capabilities in generative AI deployment, highlighting the need for organisations to enlist expertise that guides them towards specific, value-driven applications.

For once, companies with extensive historical unstructured data are well-positioned to leverage generative AI in knowledge management. Legacy data, previously viewed as an impediment, can now serve as a key differentiator for organisations in
traditionally non-digital industries.

As seen throughout the survey, many businesses have had trouble adopting generative AI or remain unconvinced. To understand what has been stopping them, we asked leaders what barriers they felt were holding them back the most.

A majority (41%) felt that a lack of expertise and skills was the main barrier to adopting generative AI in their organisation. Just over a third (35%) had data and privacy concerns, while 31% were concerned with a lack of proven ROI.

A quarter (25%) had encountered resistance to change within their organisations, while 22% saw high implementation costs as the greatest hurdle.

Many organisations find it challenging to start and implement generative AI initiatives. In our experience, 5 initial steps are crucial to create a strong foundation for responsibly launching and scaling generative AI in a way that is both strategic and impactful.

Find out more in our full Generative AI Guide above.

Right now, knowledge management is a key focus area for generative AI implementation. Within the topic, there are three key problem areas generative AI applications are particularly effective at solving:

1. Unlocking your document mountain
2. Organising the information tsunami
3. Expert shortage

Find out more in our blog, What is Knowledge Management?

Oakland is a consultancy focused exclusively on liberating and activating data. We help transform and grow businesses by giving them access to the latest skills and technology, leaving them free to grow with the confidence that their data is continuously working for them.

The speed of technology presents a dizzying array of choices and Oakland helps some of the UK’s largest companies navigate these in an informed and measured way. Importantly, we draw on our teams of strategy, governance, engineering, AI and analytics experts.

Engineers at heart, we are hands-on partners right through the data lifecycle, achieving powerful results for our clients and giving them the freedom to focus on their business goal.

• Data Strategy
• Data Governance
• Data Engineering & Platform
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• Generative AI consulting and solutions