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The UK often talks a big game about being a digital hub for the world — but there is a widening gap between the use of artificial intelligence (AI) in the country, compared to the U.S. or China.
Is there a risk of countries around the world falling behind? IBM’s latest Global AI Adoption Index reveals the disparate pace of AI adoption worldwide.
Techopedia sits down with Dr. Nicola Hodson, IBM UK and Ireland’s Chief Executive to talk about skills gap, AI around the world, and how AI is going to be “a partner” in jobs going forward.
About Dr. Nicola Hodson
Nicola Hodson was appointed Chief Executive, IBM UK and Ireland, in January 2023, where she is responsible for the company’s business operations.
Dr Hodson held senior positions at Microsoft for 14 years, including as a global Vice President of Transformation for the commercial business, as UK Chief Operating Officer, and as head of its UK Public Sector business.
Dr Hodson is also Deputy President of techUK, the trade association, and has a wide breadth of experience, serving on the boards of Beazley plc, Drax Group plc, Bramble Energy Ltd, and governor of Bradfield College.
She formerly worked in Siemens’s IT and business services division, at CSC (now DXC), as a management consultant for EY, and at BNFL. She was a non-executive director at energy regulator Ofgem and a board member at the UK Council for Child Internet Safety and CEOP, the UK’s Child Exploitation and Online Protection group. Her qualifications include an MBA and a PhD in Engineering.
Key Takeaways
- UK Enterprise AI adoption lags behind India, UAE, Singapore, and China.
- Skill shortages, cost, and data complexity hinder UK AI integration.
- Ethical AI development is crucial for trustworthy and practical applications.
- IBM aims to train 30 million people globally by 2030, including training two million in AI skills by 2026, emphasizing inclusivity in tech education.
- Early AI adopters are reinvesting due to tangible benefits and competitive advantages.
- AI and automation are critical for the UK to close the productivity gap and enhance global competitiveness.
AI Distributed Unevenly Around the World
Q: Is the UK falling behind in AI adoption? IBM’s latest Global AI Adoption Index reveals a stark contrast between the AI adoption rates in the UK and countries like India, China, and Singapore. What are the main challenges holding UK businesses back?
A: The study’s output showed that around 30% of enterprise companies with more than a thousand employees in the UK have actively deployed AI. This compares with almost 60% in India, 58% in the UAE, 53% in Singapore, and 50% in China.
However, we also saw in the UK that around 41% of large enterprises actively explore or experiment with AI. So, we will see AI adoption grow this year as they move from experimentation to production.
Q: The skills gap in AI, and tech in general, is a significant challenge. How do you envision the future of tech education and workforce training in the UK, and what role can IBM play in making tech more inclusive and accessible?
I listened a while back to a podcast on BBC Radio 4, and they were talking about the advent of the washing machine, which was expected to change housework forever and give us all more free time. Of course, what happens is that you find more tasks to do in that time that’s been freed up.
The same thing is happening with AI — if a software developer becomes 30% more productive, that doesn’t mean they do 30% less work. This means they have more time to do work of higher value.
And when businesses are more productive, they’re more likely to grow and create more new jobs.
Many jobs will evolve as we increasingly see AI and humans working hand-in-hand, and there will be new roles created that we haven’t even thought about yet.
We all must stay sharp and build our skills. There’s a significant skills gap in the UK. From a tech UK perspective, around two million jobs went unfilled last year. So, we do need to work on the skills challenge. IBM has a program called Skills Build, and we’re looking to train 30 million people globally in tech skills by 2030.
This includes things like our AI Fundamentals course. Anyone can log into the portal to take that course and start thinking about how they might apply AI.
As part of Skills Build, we’ve committed to training two million people in AI [globally] by 2026, giving them new skills to find different work opportunities. We partner with many schools of higher education, colleges, universities, etc., to implement that program.
We also partner with people like the National Cyber Security Center. They have a cyber-first program, and we work with them as a component of skills built to help educate girls. For example, it focuses on girls in Manchester, and we’re working with over a thousand schools.
A significant benefit of generative AI is that it makes tech tools easier for everyone to access. Now, anyone can check in on the internet and use AI tools firsthand to see what they’re like.
Coding is now in natural language, in plain English. So, you don’t need to know a detailed coding language to use AI.
For example, if you’re a consultant, you should examine the users of the service you’re building. These are called personas. That tool allows you to create personas in minutes, which used to take days of research.
We also use AI and Gen AI in our HR service, Ask HR. As an employee, you get a full range of HR and employee services with super simple, plain English prompts, and results come back immediately. The percentage of first-time resolved queries is super, super high. There’s super high satisfaction. These are just a couple of examples of how technology is becoming more accessible and making life easier.
The Barriers to AI Adoption
Q: What are the main barriers to the adoption of AI?
A:? We are seeing three primary issues holding enterprises back. One is around skills and the need for more skilled people. The second is around costs. And then the third is data complexity.
However, another critical area that businesses and governments need to address in order to use AI is governance. They need AI that produces accurate results, free from bias and toxicity, that complies with regulations, and that can transparently show what data was used to generate outputs.
Those challenges must be addressed proactively and quickly if UK companies want to stay ahead of their global competition.
Q: The Index highlights barriers such as skill gaps and data complexity in AI deployment. What are IBM’s approaches or solutions to these challenges, and how can they be applied across industries?
A:? If you take skills as an example, companies must invest now in training their workforce to use AI because the teams who can use AI effectively will outperform those who can’t.
Sometimes, it’s better to start by working with an experienced partner who already has the right expertise and can help get you off the ground. At IBM, we’ve trained over a thousand of our consultants to form the IBM Center of Excellence for Generative AI. They can help clients develop strategies to upskill employees and examine where GenAI can add the most commercial value to the business.
We also have an extensive client engineering team that can work with companies to develop pilots and use cases where they can test and try AI, familiarize themselves with it, and then determine which ones will be most productive.
Data complexity is another significant obstacle. AI is as good as the data it’s working across. So, companies must build an organized, secure data platform. This works best on a hybrid cloud IT model, which unifies the different cloud platforms and data sets a business is using so that you can run and govern AI applications right across the organization.
We are working on these things with our clients as we help them upskill and ensure they have the data in good shape to make the best use of AI.
Jumping the AI Ethics Hurdle
Q: Given the growing concerns around AI ethics, how does IBM approach the development of responsible AI? Moreover, how can businesses ensure their AI initiatives are ethical and practical?
A: The IBM Global AI Adoption Index found that almost 25% of UK respondents said they have ethical concerns that are a barrier to AI adoption. We’ve all seen examples of AI-producing outputs that are incorrect, inappropriate, or potentially harmful. Businesses must have accurate and trustworthy AI that can explain how they arrive at their decisions.
Last year, we launched a platform called watsonx, an enterprise AI and data platform with a built-in governance layer that serves as an AI nutrition label. It allows enterprises to show transparently where the data in their AI models comes from and which pieces of data are being used to help drive the AI decision; it flags any inaccuracies or biases and can automate compliance with regulations and policies.
It’s also important to embed ethics into ways of working. At IBM, we created an AI Ethics Board that ensures that any AI product IBM has created adheres to our ethics and trust principles before it’s released into the market.
Examples of AI Transformation in the Workplace
Q: Can you share any examples of how early adopters of AI have transformed their operations? What lessons can other businesses learn from the early movers?
A: Here’s a fascinating stat: 60% of early adopters who have overcome some of those barriers we discussed and deployed AI are now making further investments. They’re already seeing benefits. There are sorts of use cases.
A great example of how AI and automation are being used to increase productivity and efficiency can be found in the NHS. IBM Consulting worked with University Hospitals Coventry and Warwickshire NHS Trust to cut the number of appointment no-shows from 10% to 4%, allowing doctors to see more than 700 additional patients each week without needing extra staff. Estimates show this solution could reduce the overall waiting list backlog of this Trust by 10-15%.
If you bank with NatWest, you’ve probably seen and interacted with Cora, the bank’s digital assistant. We announced late last year that GenAI from watsonx is now being infused into Cora so that it becomes an intelligent, more conversational virtual assistant that can add more value for customers.
Last year we worked with The All England Lawn Tennis Club to create a new AI-generated commentary feature using watsonx for match highlights videos on the Wimbledon digital platforms.
This is a great example of how generative AI is being used to help increase audience engagement digitally, and you could apply the same principle in different industries like retail.
In Norway, IBM worked with a media company to save investigative journalists time researching and prioritizing stories to pursue, using generative AI so they can spend more time producing important stories.
Q: In terms of technology strategies, what are your insights on how UK businesses can leverage AI and other technologies to close the productivity gap and compete globally?
A:? I mentioned a hybrid cloud earlier, and having that as an intentional strategy so that you can make your data properly accessible from wherever it sits is a top priority. But AI and automation have enormous potential to drive productivity there.
We’re seeing that AI use cases typically fall into three big buckets. One is around customer service. The second is around digital labor or automating repetitive, manual tasks. The third is around application and code modernization. These are the three where we’re seeing lots and lots of traction.
Most importantly, getting a clear strategy around where AI will help you move your business’s commercial needle is essential. Then, ask what small number of use cases you can experiment with the pilot, then deploy and get some confidence that they will give you that commercial advantage.
What Comes Next?
Q: Looking ahead, how do you see AI and automation shaping the future of work in the UK? What should businesses do now to prepare for these changes?
A: We’ll see lots more AI assistants working alongside us, so it’s important that companies have a plan in place for training employees to work with AI and get the most value from it.
There are so many tasks that generative AI can handle that would give professionals weeks of time back each year to focus on things that will move the needle more. It could be tasks like summarising lengthy reports and meetings, drafting letters, researching, or creating presentations.
We’re already seeing big productivity gains at IBM from using AI to automate repetitive, admin-oriented tasks.
Our HR team has saved 12,000 hours in the last 18 months by automating systems that previously required back-and-forth exchanges between managers and employees.
IBM Consulting has used AI to reduce the promotion cycle from 10 weeks to 5 weeks by automating a lot of the admin work involved.
For IT teams, generative AI is going to massively increase productivity and help reduce costs. For example, we’ve created a tool called Code Assistant that can convert old, legacy COBOL code — still used by many older businesses — into modern Java code, which creates substantial cost savings, as the few remaining experts in COBOL are expensive, and really accelerates IT modernization.
AI-infused software can also play a huge role in reducing operating costs from cloud usage and energy consumption in IT estates through constant, automatic monitoring and data analysis.
For marketing and communications professionals, we’ll see more AI assistants being used to develop first drafts for different types of content, generate ideas for campaigns, and so on, freeing up more time to focus on strategy and creative work.
The Bottom Line
The path to fully harnessing AI in the UK is fraught with complexities ranging from skill shortages to ethical concerns. Dr. Nicola Hodson’s insights underscore a pivotal moment for UK businesses: the need to pivot from hesitation to action in AI adoption.
With a clarion call for proactive investment in workforce training, ethical AI development, and strategic technology integration, the dialogue with Hodson charts a course for overcoming the current impediments and paints a vision of a future where AI and automation are keystones of global competitiveness and innovation.
As early adopters reap the benefits of their foresight, the message is clear: the time for UK enterprises to deepen their commitment to AI, bridge the adoption gap, and embrace this technology’s transformative power is now.
