Blog: Digital Transformation Beyond 2025 – The New Operational Paradigm (Part 3)
In this series, I am exploring what the near-future of digital transformation is going to look like.
Major shifts in business are on the way, largely thanks to AI and the tech underpinning it, and while transformation is already a space that is notorious for getting a regular refresh, we are in the midst of a major evolution with extensive implications.
Photo by Mike Kononov on Unsplash
In Part 2, I discussed hyper-personalisation and the ways that businesses must gear up to surprise and delight clients through emotionally responsive experiences.
In this final post, we will delve into the foundational technology underpinning these advancements and underscore the critical need for investment in resilient infrastructure. After all, the software being used to power new operational paradigms needs robust, efficient tech to drive it forward.
Let’s get into it.
New Frontiers in Computing
Something I keep repeating in private but also in conversations with clients is that, from the start, the point of computing was always automation – we just didn’t have the processing power to achieve it. Now, we do – or at least, we are very much on the pathway there.
This is an idea that is regularly missed in conversations about the potential of AI and the future of the business landscape – ideas and the computing power to execute on them must come hand in hand. The sentient, hyper-personalised enterprise cannot be built upon yesterday's infrastructure. The sheer volume of data, the computational demands of AI and the need for real-time responsiveness require a fundamental re-thinking of digital infrastructure.
A significant part of this is moving computing power to “the edge”. This is where centralised cloud computing models are giving way to a “decentralised” pivot point close to the physical source of the data. This a direct response to the limitations and inefficiencies of the cloud-centric model – the high financial cost of transmitting, storing and processing massive datasets in the cloud, coupled with the physics of latency, makes a centralised approach untenable for a growing number of critical applications that depend on real-time data processing. As AI, the Internet of Things (IoT) and autonomous systems are required to respond to real-time data, the data must be processed as close to real time as possible – it’s scary to say, but even delays of a few microseconds can disrupt experiences.
Where This Matters – and Why
By processing data locally, business can enable very low-latency, real-time decision-making that is crucial for a new wave of innovation. The logical question to this is: Why does this matter?
To begin to answer this question, let’s look at where these changes matter across a few industries:
Healthcare: In hospitals and clinics, the ability to process data at the edge is critical. It supports real-time analysis of diagnostic imaging, enables continuous patient monitoring via wearable devices and provides instantaneous responsiveness. This is a core requirement for critical AI-assisted applications, both for in-person and for remote procedures, all while helping to maintain compliance with strict data privacy regulations.
Retail: As retail tries to keep up and align with the digital shopping experience, the in-person experience is increasingly powered by the edge. It is the technology behind automated checkout systems, real-time inventory tracking, in-store loss prevention and the delivery of hyper-personalised shopping experiences to customers as they move through a physical store.
Industry: In factories, edge computing enables real-time predictive maintenance by analysing data from machinery to verify processes, anticipate failures and monitor safety. It powers systems for automated quality control and drives workflow optimisations that increase efficiency and reduce downtime.
The examples from these industries should paint a compelling picture of why real-time processing is critical – delayed outputs and decisioning could mean anything from a missed sales opportunity to untracked unsafe working practices or even a risk to someone’s wellbeing.
Powering Real-time Tracking
As I mentioned, the reason we are bearing witness to this revolution is we now have the processing power to make dreams into reality.
This new capability is being powered by key enabling technologies, such as the global rollout of 5G networks. They provide the high-bandwidth, low-latency connectivity necessary for robust edge deployments. Platforms like Google’s Kubernetes and AWS’s Elastic Container Service provide a standardised way to manage complex, distributed application workloads.
In terms of security, Zero Trust Architecture (ZTA) is fast becoming a prerequisite for any organisation embracing edge computing. ZTA requires continuous verification for every user, device and application attempting to access resources, and is currently the only viable security strategy for a distributed, edge-native enterprise.
This highlights a new security challenge: Decentralisation makes the idea of security perimeter a slippery concept. Instead of securing a handful of centralised data centers, security teams are now faced with protecting a distributed network of potentially thousands or even millions of edge devices. This renders the traditional "castle-and-moat" security model, which focuses on defending a hardened perimeter, completely obsolete. New ways of securing data and processing power is required.
Sustainability
Finally, any discussion of the future of computing must also discuss its sustainability. The AI revolution comes with a significant and growing environmental cost. The computational power required to train and run large AI models is immense, leading to a surge in energy consumption. The power demands specifically for AI infrastructure are expected to triple by 2030. This creates a paradox for business leaders: the very technology being deployed to drive operational efficiency is creating massive energy inefficiency at an infrastructural level.
This makes sustainable IT a core strategic imperative for the next few years, not merely a corporate social responsibility initiative. It is a matter of operational resilience, cost management and regulatory compliance, and organisations must now integrate sustainability metrics which go beyond mere symbolism into their technology strategy.
In the past, IT procurement and architecture decisions were driven primarily by technical performance and financial cost. Now, these decisions are directly and inextricably linked to a company's ability to meet ESG targets and comply with emerging regulations — for example, the EU's Corporate Sustainability Reporting Directive (CSRD). The choice of a cloud provider, a hardware vendor or even a specific AI model is no longer just a technical or financial decision; it is an environmental one. This new reality necessitates strategic alignment across the C-suite; going forward, the Total Cost of Ownership (TCO) for any IT system must be evaluated alongside its Environmental Cost of Ownership (ECO).
Conclusion
If it wasn’t clear before this series, hopefully it is clearer now: the next few years are going to be a wild ride with substantial, sweeping changes in the digital transformation space.
The digital landscape of 2026 and beyond will be one of fundamental, systemic transformation. The era of treating digital as a separate initiative or a bolt-on to the core business is definitively over. The trends outlined in this series are not isolated. They are deeply interconnected pieces that are collectively reframing the nature of competition, value creation, risk – even, potentially, of business in general.
So while the path forward is complex, I believe the goals are clear. It is important to make the right strategic decisions in the present to build a resilient, adaptive and competitive organisation capable of thriving in it. The companies that embrace this comprehensive reframing of the digital landscape will be the ones that define the next decade of business.
Thank you for reading.
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