The new frontier of agility - how data, cloud & delivery are combining to accelerate business value.

Digging the crates

Death and taxes. The only certainty in business is change. The one constant we can rely upon - nothing will be tomorrow as it was today. But what separates the good from the great, is how we adapt to change. Successful companies are not only measuring the right things, they are acting on the insights rapidly.

The triggers for change can come in many forms and from many sources, internal or external. Some of these triggers will be obvious and demand immediate action, while others will be hidden in the detail, only surfacing after deep analysis and asking the right questions.

Either way, you can only manage what you observe. Be it subtle shifts in customer behaviour or emerging market trends, you will be powerless to act unless you are actively observing, analysing and acting as a matter of course. Without access to the right data and the right tools, important signals will go unnoticed until it’s too late.

Finding the hidden

The problem with data, however, is there tends to be rather a lot of it. And whilst you might be able to extract some information relatively easily with a simple query, drawing inference from large datasets across multiple data points is an entirely different proposition.

This is the realm of machine learning (ML) - a branch of artificial intelligence that leverages specialist algorithms to detect patterns, trends and risks within datasets. Unlike generative AI, ML and data analytics have been delivering bottom-line impact for organisations for years, helping them understand their customers and markets more deeply than ever.

This shift has been particularly evident in the insurance industry, where data-driven approaches are enabling more accurate pricing, proactive risk management and greater operational efficiency. In a sector where profit margins can often hover around 2–3%, any insight that can reduce risk exposure can deliver meaningful financial impact.

Improved decision making

More specifically, ML and AI are transforming insurance underwriting - the craft of assessing and pricing risk. Historically, underwriters spent a disproportionate amount of their time on low-value tasks such as manual data entry and paperwork - upwards of 35%, by some calculations - leaving limited time for deep risk evaluation.

Machine learning is reversing that balance. By automating routine tasks, underwriters are not only reclaiming time for high-value decision-making, they are also enhancing those decisions through data-driven insights surfaced by ML. The result is more accurate pricing, better risk selection, faster application processing, more granular customer segmentation, and lower operating costs:

• More accurate pricing: ML models can analyse far more variables, like driving behaviour, property features, or health data, than traditional formulas. For example, auto insurers are now using telematics to adjust premiums in real time based on actual driving habits, improving fairness and aligning price to a driver’s true risk.

• Better risk selection: ML is helping underwriters identify which risks to accept or decline with greater precision. By detecting hidden patterns in large datasets, models can flag subtle risk factors that might otherwise go unnoticed, leading to smarter pricing and more consistent decisions.

• Faster quote turnaround: Automating data collection and analysis is allowing underwriters to issue quotes faster than ever, improving customer experience and conversion rates. With AI pulling in relevant data instantly (e.g. claims history, credit, sensor info), underwriters can issue bindable quotes in minutes rather than weeks.

• Customer segmentation: ML is enabling highly refined segmentation based on predicted risk or value. Insurers can tailor products and pricing more effectively e.g. grouping drivers by braking habits or homeowners by wildfire risk. Some health insurers are even using wearable data to personalise premiums and wellness programs in real time.

Acting in real time

But, it’s not just underwriting. Insurers are leveraging machine learning for real-time operational decisions – acting on signals that protect the business or serve customers at times of increased risk.

In the past, such decisions often relied on human judgement or lagging metrics, but today they are increasingly data-triggered, automated and enabled through simple API calls and built-in system configurability. Examples include:

• Proactive risk mitigation: When data shows a sudden spike in risk or costs, insurers can react immediately by pausing or delaying policy binding in a specific region or state until the risk as passed. This might feel controversial, but it is protecting insurers from undue risk and costs.

• Automated claims triage and routing: ML systems can now automatically assess incoming claims in real time, fast-tracking low-value claims for instant approval, flagging complex cases for expert attention, and even predicting repair costs based on customer-supplied images.

• Fraud detection and prevention: Fraud costs insurers billions, so any improvement to detection rates has huge business value. This is where machine learning excels, flagging anomalies much better than traditional rules-based detection systems.

Agility, more than a technology shift

This is just one example. Machine learning is being used across multiple verticals with similar effect, from financial services to logistics, medicine and pharma. But while algorithm insights have become essential for business, they only matter if they can act on the information quickly.

If you think this is just about technology, then think again. Agility is about reinventing how you deliver value. Old systems might die hard, but old ways die even harder. Unless you change both, you will never realise the full value of modernisation - reduced costs, reduced risk and, crucially, improved business agility.

Today, modern cloud native architectures, supported by continuous delivery and engineering excellence, are enabling teams to deploy software updates multiple times a day. This kind of agility is only possible when technology, people and process work in harmony. These activities are not mutually exclusive, they're tightly interwoven:

• Cloud Modernisation: Moving to the cloud isn’t just about reducing costs or improving efficiency. It’s about building on modern architectural patterns - APIs, microservices, serverless, event-driven - that enable flexibility, modularity and adaptability. These patterns allow teams to break complex systems into discrete components so that they can be iterated upon more quickly, independently and confidently with less risk.

• Continuous Delivery: For teams still operating in legacy environments or following agile practices in name only, the move to continuous delivery is a leap. CD is built on principles of autonomy, trust and shared ownership. It requires extreme engineering discipline and rigour. Done well, it will deliver more than speed - it will enhance reliability, elevate engineering standards, and boost team morale.

• Exceptional talent: Technology and process mean nothing without the right people. A house built on autonomy and trust requires highly capable individuals who understand their craft. These are A-players who can balance speed with discipline, and experimentation with accountability. If you want to modernise, then start by hiring better, upskilling where necessary, and creating a culture where curiosity, ownership and critical thinking are central to everything.

Sum of its parts

Business agility doesn’t happen by chance. It’s the result of intentional investment across multiple interconnected pillars - data analytics (and machine learning), modern cloud-native architectures, disciplined continuous delivery practices, and exceptional people to fuel delivery excellence. Miss one and you undermine the others.

Ultimately, agility is a human capability, not just a technical one. But it requires a systemic approach where modernisation is not seen as a one-time upgrade, but as an ongoing journey of continuous improvement, driven by insight, powered by technology, and delivered by high-performing teams built for change.