Why AI Adoption will increase your enterprise value
The more that things change, the more they stay the same.
Artificial Intelligence (AI) is not a new concept. All the way back to Alan Turing and the creation of the Bombe machine to break the Enigma code, computer scientists and mathematicians have been questioning whether machines could one day possess intelligence. Of course, since then the world has been through huge technological advancement, with computing embedded into everything we do.
As consultants in this space, we have been at the coal face of the changing discussions around technology – from the dotcom bubble in the late 1990s through to the data centric early 2000s – with Clive Humby (British mathematician) coining the phrase “data is the new oil” in 2006, right up to the present day of big data, advanced analytics and machine learning.
As technology has become more consumer friendly, and solutions have sought to speed up processes underpinning our economies, so the scale of data we produce has soared. Since the turn of the century, the focus for many businesses and tech professionals has been data – organisations have had to adapt to this new commodity – learning both to protect and manage it and leverage it for growth. Increased regulatory focus and better understanding of consumers and customers has meant businesses have quickly had to become data experts to get ahead of their competition. But where data ends and AI begins is not clear cut.
Many organisations have been thinking about advanced data competencies for many years – building teams of data scientists, considering solutions that automate processes or workflows, looking at predictive analytics to better manage their businesses.
We were already in a time where the concept of AI was no longer the preserve of computing specialists, but common parlance for leaders in all businesses, whatever the size, sector or geography.
Then everything really did change.
For AI (or machine learning) truly to break through into mainstream usability and subsequent adoption, three elements needed to be in sync – model, compute, data. Until now, these three components had always been slightly out of step, one sometimes racing ahead of the other leaving a gap behind them in the trifecta.
But in the last decade, they’ve all started to arrive at the same point.
Now, for the first time in the history of computer science, we have true resonance: models built on advanced machine learning algorithms are highly efficient, computer hardware that allows many processes to be run in parallel are powerful enough to process them extremely quickly, and there’s an abundance of data that’s of a sufficient volume and richness from which AI can be trained.
And then it happened …Generative AI (one particular branch of AI) fundamentally reshaped the conversation around Artificial Intelligence. The launch of ChatGPT and other Large Language Models (LLMs) have put AI in everyone’s hands – used for completing kids’ homework, creating new music and movie animations, finding answers to just about everything you want to know. The phrase “you’ve got to see it to believe it” has never been truer than in the age of AI – Generative AI tools mean everyone can see it working and get their hands on it. It is no longer just for data scientists who can speak the lingo. There is genuine and palpable excitement about what AI can do for all of us.
According to McKinsey research, Gen AI is expected to add $2.6-$4.4 trillion to the global economy annually and an additional $6.1 to $7.9 trillion through increased workforce productivity. Even if these numbers are wrong by a reasonable margin, they are still extremely significant.
The market reaction has been sharp and fast.
Markets have piled in on AI stocks and companies. Everyone has wanted to get a piece of the action. But this isn’t just the AI development firms like OpenAI and Anthropic – it’s the whole AI ecosystem that is benefitting.
AI Chip manufacturers such as Nvidia have seen huge rises in their share price, as have cloud compute providers such as Microsoft and AWS. As The Economist has remarked: “The world is still in the early days of the Generative-AI epoch. Even so, it has already been immensely lucrative. All told, the 100 or so companies that we examined have together created $8trn in value for their owners since its start…..At every layer of the stack, value is becoming more concentrated. In hardware, model-making and applications, the biggest three companies have increased their share of overall value created by a median of 14 percentage points in the past year and a half. In the cloud layer Microsoft, which has a partnership with ChatGPT’s maker, OpenAI, has pulled ahead of Amazon and Alphabet (Google’s parent company). Its market capitalisation now accounts for 46% of the cloud trio’s total, up from 41% before the release of ChatGPT.”
As at year end 2023, The Data City and the CBI calculated that, in the UK, AI companies accounted for £42.3bn turnover, £41.9bn of investment funding, £9.1bn in GVA – 0.5% of the total UK GVA – equivalent to the Travel Agency sector, £4.3bn generated directly by the activities of AI companies and £4.8bn attributed to the wider economic contributions of the AI economy (supply chain, employee spending etc).
It’s not just the market that is reacting, it truly is the whole economic ecosystem.
Whilst the curve may flatten, we are certain that AI will continue to be a core value driver for companies in all industries in the coming years.
Of course, many of these huge stock market valuations are for the companies directly involved in AI software, hardware, solutions, and services as the demand spreads. But we believe all businesses can benefit from leveraging data and AI solutions at the right time for them.
AI brings with it a huge opportunity for productivity gain – whether that’s in freeing up time for analysts where machines can do the heavy lifting in document analysis and review, software companies being able to use generative coding tools, or creative industries using image, music and video generation tools. Whilst adoption of new general purpose tech trends will take time, 65% of Chief Executives believe AI will have a significant impact on their business in the next three to five years – “AI is still likely to change the economy, even if it will not do so immediately.”
And it’s not just productivity – AI offers a real opportunity to increase revenues through a deeper understanding of your customers. For example, a blend of Generative and Traditional models allows a business to truly extract and understand sentiment from product reviews or social media (Generative), which can then feed your segmentation, acquisition, churn and offer models more accurately (Traditional), resulting in a highly personalised communication to increase the probability of that offer converting to new business (Generative).
Every company can realise value for their top and bottom line with AI capability available today.
The pace of change shouldn’t hold you back.
These advancements in data & AI technology are happening so fast which, perhaps counter-intuitively, offers even greater advantage to the companies prepared to use them in a business process right now. What we’re seeing is that the advances the innovators are making in breaking new barriers are rapidly maturing and being made more readily available. What was the domain of the AI Innovators 18 months earlier, is now freely accessible to the late majority.
In the SME sector where we focus, the opportunity is even greater. Getting things done in a large corporation demands significant political and financial capital. The time it takes to get everyone aligned is often the greatest barrier to starting. This is not true for smaller, more agile and entrepreneurial organisations who are ambitious enough to move at pace and gain real competitive advantage.
We see three core reasons for getting started on AI implementation sooner rather than later.
Investors will start to ask more questions.
Shareholders and potential investors in all companies are going to start asking questions about how AI is used in the organisation – and the answer of “it isn’t” won’t be palatable for long. Equally, point solutions with no real benefit in terms of how the company operates will become obvious very quickly. AI implementation needs to be about more than a snazzy chatbot or a strapline on the website. We believe AI will come under the same level of scrutiny as ESG when it comes to investor due diligence processes – with comparative scorecards on what different companies are doing in this space.
You need to stay competitive in your market: don’t get left behind.
Many companies have heard the horror stories of when people have got AI implementation wrong and have stepped away for now (https://www.wsj.com/articles/companies-increasingly-fear-backlash-over-their-ai-work-53aff47c). Or they’ve started using some of the LLM tooling but can’t get the results they’re anticipating. Don’t be put off. There is still huge white space here, and first mover advantage. Having a core understanding of AI concepts and how these can apply to your business will enable you to consider innovative solutions to challenges you might be facing.
It will improve your bottom line.
Spending on new technology and solutions always feels like a big upfront cost. But starting small will enable you to see the benefits before you scale. Think of the processes in your company that are repetitive and time intensive, yet critical to the way your business operates. These are the areas where AI solutions should be considered. For example – think of an investment analyst who has to do lots of research and reading on a company before meeting them. Take hours of effort away from the upfront research activity, and this frees up time to meet more companies, have more discussions, build stronger relationships.
The only true failure is failure to start.
Getting started isn’t as hard as you might think – here’s how we do it at twisted loop
Elevate your base understanding.
There is lots of terminology and language when it comes to AI that makes it sound more complicated than it is. Get your teams upskilled to understand the basics when it comes to data and AI, so they can help identify potential use cases for business improvement.
Decide on a business area where you want to realise greater value.
Think about the biggest challenges in your business: what takes the most time? What stops you being able to grow? Where do you see your biggest risks? How do you make your money and how could this be done better? Then go down to another level and ask How Might We. Start with a broad problem or insight but then focus on the desired outcome and build a positive value question. “How might we develop a loyal customer base with a low cost of acquisition so as not to compromise our service?”/ “How might we make clients feel more confident and informed about AI adoption?”. Empower everyone in your organisation to come up with ideas – leadership don’t always have all the answers.
Create a backlog of solution use cases.
Now get your business and technology teams together and begin to work through potential solutions for further analysis: which of these could benefit the most from an AI solution? What will deliver the most value to the business? What is the cost and effort associated with delivering each? What do we need in place before we begin? How can we prototype the solution in a small way? This backlog can be used to prioritise investment and get the board on board without a huge amount of debate and wasted time.
WHY AI ADOPTION WILL INCREASE YOUR ENTERPRISE VALUE
AI as a concept has been with us for a very long time. Today, the concept has become a useable and accessible reality – but don’t think of this just as a technology challenge; it’s really one of business integration.
If you’re ready to embrace it and the time is right, then – with a little preparation – the competitive advantage and business optimisation opportunities are there.
In the very near future, all investors will be looking at how “AI-enabled” a business is, but for now they’re taking a keen interest in the businesses that can demonstrate progress.
Until then, get things underway and you won’t be reducing your EV, you’ll be building it.