Paddling or taking the plunge? How UK manufacturing can exploit AI’s full potential

UK manufacturers are amongst the most enthusiastic in Europe when it comes to AI. However, where adoption is concerned, a recent survey reported in The Manufacturer suggests we’re behind our European counterparts. How can UK manufacturing go from paddling in the water of AI to taking the plunge? Our Chief Delivery Officer, Hugh Simpson explores UK manufacturing’s AI dilemma.

A survey published in The Manufacturer earlier this year revealed that less than 40% of UK manufacturers, engineers, and product developers are planning to implement AI technology within their businesses; well behind their European counterparts. Those who are using AI are mostly using it for a single business process, such as administration, providing only incremental benefits.

For UK manufacturers, AI adoption isn’t a question of will; they are generally more enthusiastic about the potential of AI than their European counterparts. Rather, it’s a question of way.

Manufacturers understand - perhaps better than any other industry - the art of problem solving. So let’s explore how UK manufacturing can turn its enthusiasm for AI into ROI.

Think carefully about your AI approach: product not software

A common misconception about AI is that it is yet another technology which needs to be baked into your business processes. AI, however, is not simply another system or software update to be rolled out across your estate. AI requires a very different mindset and a broader skill set. Your entire organisation - not just the IT department - need to be fully onboard for the journey if you really want to reap the benefits, as some sectors are already doing. It’s vital to have a clearly thought through strategy to implement the significant - but achievable - changes that your organisation is going to need on its AI journey.

Companies that take the right approach to organisational changes can scale AI and its adoption, delivering meaningful business value, repeatability, and longevity. They use an implementation strategy that is planned and developed with a product-based—and not a software development-based—approach. In practice, this means that developing AI is focused on the customer (or user’s) need and the outcomes it can produce rather than the individual technical tasks to develop the AI. A product-based approach is about not getting seduced by the technology, breaking down barriers to scaling in small steps, and reducing the time to value. We call this the our ‘Route to Go-Live’ approach. This roadmap to live implementation defines the steps needed to take AI projects from PoC to production, successfully and with expedience.

Let’s take a look at what these are.

Building your AI Route to Go-Live

There are three clear stages to a successful ‘Route to Live’ AI implementation approach. The approach is characterised by accelerating AI development from pilot to production, and aligning AI with business value in strategic ways.

Stage 1: Set Your Vision with an AI & Data Strategy

AI implementation success means aligning your business, technology, and AI & data strategies. Start by identifying your current state by drawing on pre-defined use cases or sector experience. Then, use a structured AI ‘discovery’ approach that defines and prioritises only the right use cases—ones that augment humans to create business value.

Stage 2: Start Small with Proofs of Concept

As you prepare your data and infrastructure for AI, don’t get bogged down by technology at the start. You can reduce your time to value by establishing the right governance, data preparation techniques, and both flexible and scalable architecture blueprints first.

Your next step is to get the right people and capabilities. Leverage experienced AI engineers and data scientists who have successfully scaled AI before, then integrate them seamlessly into your existing team to share knowledge.

Stage 3: Launch Fast and Scale in Months, not Years

Finally, move quickly beyond proof of concept by aligning success to business value. Taking a product-led approach to scaling AI effectively enables you to fail often, fail fast, yet fail forward enabling you to reduce time to value and increase ROI.

As you move forward, ensure you scale with teams tailored to your needs. With the right resources, you can build a fit-for-purpose team around your solution—including QA, DevOps, data engineering, and core development right through to project and product management experts.

Getting your business AI ready

A pilot AI implementation will only demonstrate the value of AI in a limited capacity. A product-based approach to AI development can make the difference in realising a truly business-driven solution. Bridging this gap is where the ‘fail fast, fail often, fail forward’ product-driven approach makes a difference.

But making a difference requires companies to transform their organisations as well, with the same level of commitment they might assume during a new product launch. That includes realigning corporate strategy, introducing employees to new skills, and scaling capabilities.

These AI high performers are “more likely to apply core practices for using AI to drive value across the organisation, mitigate risks associated with the technology, and retrain workers to prepare them for AI adoption,” according to McKinsey.

But AI requires specialist skills and experience not readily available in most in-house data and analytics teams. Top AI engineers are a mix of data engineer, data scientist, researcher, and business analyst, all with a product-centric mindset. Ciklum’s AI ‘Route to Live’ approach helps companies position themselves with the tools and expertise they need to carry out their product-driven approach to AI development in this way.

Fail Often, Fail Fast and Fail Forward

A product, rather than software driven approach allows you to accelerate AI implementation and scale as you support the role and processes required to keep it running successfully.

The vast majority of companies plan to either increase their AI investments in the coming years or adopt AI for the very first time. Ensure you are putting your AI to work not as an experiment, but as a true source of ongoing business value. The foundations you put in place today, will ensure your business can not only embrace AI, but the explosion of new technologies and innovations which AI will spark in the years ahead.

This article first appeared in The Manufacturer