This September, MD Future 26 brings together leaders, innovators and experts from across the technology ecosystem to explore the emerging technologies and ideas set to shape our economy and society in the years ahead.
From AI and digital infrastructure to deep tech, cyber security, talent and innovation, the pace of change presents huge opportunities for organisations - but also raises important questions about where to invest, what to prioritise and how to prepare for what comes next.
Ahead of the conference, we asked contributors from across our membership to answer one question: What’s the next big shift in technology — and how should businesses prepare?
Here, they share their perspectives on the trends they believe will have the greatest impact, the opportunities and challenges ahead, and what organisations should be thinking about now to stay ready for the future.
Part Two, featuring more perspectives from across the ecosystem, will be published next week.
MD Future 26 is sponsored by Pulsant, Novacoast with Platform Markets Group and CTI Digital, with support from Department and UK Tech Cluster Group.
Pulsant
AI: predicting the next chapter
Mike Hoy, Chief Technology Officer
The first wave of generative AI was dominated by the race to build ever-larger models. But as AI moves into everyday business operations, the focus is shifting from training to inference. Put simply, inference is all about trained AI models drawing conclusions or predictions from data patterns and delivering real-time responses to users.
Most organisations will perform significantly more inference than training. From customer service, fraud detection and analytics to coding assistants or copilots, every AI interaction requires inference. As adoption grows, so too does the demand for the digital infrastructure that underpins it.
Researchers calculated that people already interact with connected devices at least once every 18 seconds – that’s nearly 5,000 data interactions per day per person, and it’s set to soar to tens of thousands by 2030. A further major study predicts the UK inference market will grow from $1.2 billion in 2025 to $7.5 billion by 2030.
We’re looking at rapid growth by any standard.
For businesses preparing for this next phase, proximity matters. Inference workloads, particularly those powered by small language models and edge AI, depend on low latency and secure connectivity.
Rather than concentrating infrastructure in a handful of hyperscale locations, organisations will need regional capacity closer to users and data sources. The future of AI will be defined by how effectively we can deliver intelligent services where they're needed most.
Find out more about Pulsant here.
Robiquity
The next shift isn't the technology - it's the operating model
James Procter, COO
Every organisation I speak to has now run an AI pilot. Very few have changed how they actually work. That gap is the real story of the next two years.
The shift I'd bet on isn't a single breakthrough, it's the move from AI as a tool people use to AI as capacity inside the process itself. Agents that don't just draft the report, but own the workflow end-to-end, escalating to a human only by exception. That's a different operating model, not a different piece of software.
Preparing for this looks less exciting than most people expect. It means getting your processes documented and your data trustworthy, because automation exposes mess rather than absorbing it. It means governance decided up front - who signs off, what gets logged, where the human sits. And it means investing in the scarce skill: not prompt-writing, but people who can redesign a process around a machine that now does part of it.
The winners won't be the fastest adopters. They'll be the best prepared.
Find out more about Robiquity here.
CGI
AI’s real opportunity: freeing people to focus on what matters
Sarah Cox, Vice President Consulting Services
AI is changing the IT and business consulting industry, taking on more of the routine and technical work involved in delivering services. The shift is not about delivering the same work faster. It’s about giving our consultants more time to focus on understanding what clients are trying to achieve, identifying their most pressing problems and ensuring we apply technology that creates genuine value.
How this looks in practice, however, varies from client to client. Organisations are adopting AI at different rates, with some already exploring how it can reshape established processes while others are still building their confidence and capabilities. This makes it increasingly important to understand each client’s starting point and what they actually need, rather than assuming a single route to AI adoption.
As AI becomes more widely used, we must think carefully about how it integrates into the way we work. There are opportunities to automate routine activities, support decision-making and make better use of information, but greater adoption also puts more emphasis on trust and accountability. The tools will continue to evolve, but strong governance, clear audit trails and human oversight remain essential. For CGI, it is about making practical use of AI while maintaining the judgement and trust our clients depend on.
Find out more about CGI here.
UK Biobank
Building security that enables innovation, rather than blocking it
Mark Conway, CTO
One of the biggest challenges for organisations working with sensitive data is how to enable innovation without compromising security or trust.
At UK Biobank, that challenge is particularly significant. We hold one of the world’s richest biomedical datasets, and researchers need to be able to work with that information while we continue to protect the trust placed in us by our participants.
For me, good security should not simply create barriers. It should help create the conditions in which innovation can happen safely.
That thinking is behind what we describe as a research “airlock”: creating a controlled environment where researchers can access and analyse data, while the protections around that data remain firmly in place.
The technology matters, but so does the mindset behind it. Security teams need to be involved early, understand what researchers and the organisation are trying to achieve, assess the risks and help find a practical way forward.
As AI, cloud technology and new approaches to data access develop, that balance will become even more important.
The organisations best placed to innovate will be those that can move quickly while building security, governance and trust into the foundations.
Find out more about UK Biobank here.
AJ Bell
Human where it matters: customer service in the age of AI
Mo Tagari, Chief Technology & Customer Services Officer
When people talk about the future of AI, the conversation often focuses on what technology can do. I think the more important question is what it allows people to do differently.
The next big shift in technology will be AI becoming the primary interface for customer service and digital experiences. Customers will increasingly interact through conversational or voice AI that can answer questions, complete tasks, provide guidance and orchestrate entire journeys in real time. Routine queries, administration and processing will become largely automated, available 24/7 and delivered with a level of speed and consistency that was previously impossible.
But that doesn't mean humans become less important. In fact, the opposite is true.
As AI takes on the volume, human interactions become concentrated around the moments that matter most such as complex decisions, vulnerable customers, unusual circumstances and situations where trust, judgement and empathy are essential. The competitive advantage will no longer come from how many calls an organisation can handle, but from how effectively it combines automation with meaningful human expertise. For AJ Bell, that means making our customers feel good investing.
To prepare, businesses should look beyond deploying chatbots. They need trusted data, redesigned processes, strong governance and teams equipped to work alongside AI. Those that succeed will use technology to remove friction while preserving the human connection that customers value most.
Find out more about AJ Bell here.
Marks & Clerk LLP
Preparing for a changing world – inspiration from the telecommunications industry
Tom Gregory, Associate, UK and European Patent Attorney
Like many others, the telecommunications industry is trying to understand the shape of challenges to come. However, there is a lot to be learnt from the impressive degree of forward-thinking shown by those involved.
Future use cases for telecoms are predicted to include network-enabled robotics, self-driving vehicles, and Internet of Things technology. These use cases will demand greater network access, lower latency, and higher reliability from the network.
Unsurprisingly, AI has an important role as an enabling technology. The predictive power of AI can help the network to proactively manage communication and free up spectrum. But it’s not just about AI, and telcos are innovating in many other ways to prepare for a changing future. For example, increasing signal frequencies for extreme data rates, and “repurposing” radio signals for detecting objects (e.g., in traffic monitoring), are being considered and tested.
Whilst these use cases are not yet widespread, the work to support them is already underway. Personally, I find this proactive approach to be inspiring. The aim is for minimal disruption and a smooth transition when change does happen. This benefits both businesses and the customers they serve. It would pay dividends for other industries, and indeed governments, to be similarly forward-thinking.
Find out more about Marks & Clerk LLP here.
Celerity
The Next Big Shift in AI: From Adoption to Governance
Tej Patel, Engineering Manager
Most organisations didn't roll out AI in one considered wave. It arrived tool by tool, team by team — Copilot through the Microsoft licence everyone already had, Claude because a developer found it useful. Alongside approved tools, shadow AI can emerge as employees adopt AI services without the organisation's knowledge or approval.
Multiply that across hundreds or thousands of employees and you get an uncomfortable truth: if organisations can't see which AI tools are being used, they can't effectively govern how they're being used. That creates real risks, from sensitive information being pasted into prompts to confidential documents being uploaded for analysis.
That's why we built WatchPoint, Celerity's managed AI governance service. It provides the visibility, governance and control organisations need to adopt and grow AI responsibly — discovering sanctioned and shadow AI, maintaining an inventory of AI tools and use cases, monitoring activity for policy violations, and producing evidence to support ISO/IEC 42001 readiness and ongoing governance.
For me, the next big shift isn't simply more AI. It's businesses moving from experimentation to adoption at scale. To prepare for that, organisations need governance to mature just as quickly.
Governance isn't what slows AI adoption down. It's what allows it to scale safely.
Find out more about Celerity here.
GlassAtlas
Machine-readable isn’t enough: why your products are still invisible to AI
Martin Corcoran, CEO, Summit & Productcaster
An invisible product isn’t necessarily one an AI shopping assistant can’t find. Sometimes it can find the product. It just can’t work out why it should recommend it.
I recently asked an AI assistant where I could buy the wire spool for a specific model of garden strimmer. That is how people are starting to shop: not by typing a product name into a search box, but by describing a problem and asking for a suitable answer.
My view is that product visibility now has a double lock. First, can the AI identify what you sell? Second, does your product information explain why it fits the shopper’s need?
Many retailers open only the first. Their feeds contain facts, but those details do not connect features to uses and benefits. “Two-metre handle” describes a garden tool. “Extended handle to reduce bending” helps a system understand who it could help and why.
Businesses should resist the temptation to rewrite an entire catalogue blindly. Start with one important category. Establish where your products appear in AI-generated answers, identify the questions customers ask, then audit the data, descriptions and imagery behind them.
Being listed is no longer enough. Your products need to be understood.
Find out more about GlassAtlas here.
Integrity360
You can’t predict the next security challenge - but you can prepare for it
Rich Ford, CTO
In my view, organisations do not need to predict exactly which emerging technology will create the next major security challenge. What matters more is having strong enough foundations to adapt when that challenge arrives.
AI is a good example. Many businesses rushed to adopt copilots and public AI tools before fully understanding where data was going, who had access to it or what new risks were being introduced. The same pattern will likely repeat with autonomous agents, quantum computing and other emerging technologies.
The practical starting point is not buying another tool. It is knowing what systems and data you have, reducing unnecessary exposure, strengthening identity controls and making sure critical vulnerabilities are addressed quickly. Organisations also need detection and response capabilities that work in the real world, not just on paper.
There should also be honest conversations between security, IT, legal and business teams before new technologies are widely adopted. Security cannot simply block innovation, but it should help the organisation understand the risks and introduce sensible guardrails.
The organisations that cope best will not necessarily be the most advanced. They will be the ones that stay disciplined, involve the right people early and treat security as part of adoption rather than something added afterwards.
Find out more about Integrity360 here.
