The sensitive approach to AI business impact
The age of AI, the most disruptive technology we’ve seen in decades, is firmly upon us, rapidly enabling new efficiencies in businesses. In 2025, 19.95% of EU enterprises, and 55.03% of large EU enterprises used AI technologies, according to Eurostat. UK enterprise adoption stands at 16.67% (reported as ‘1 in 6’). AI is making concepts that felt out of reach just a few years ago practical and achievable.
Yet many still find the idea of AI adoption a daunting prospect. They anticipate long lead times, implementation complexities, lack of clarity around the business benefits to be gained, and stakeholder resistance.
At Columbus, we believe, and are increasingly finding in our collaborations with customers, that the opposite is true. Projects are coming to market with the speed that’s essential for sustaining the competitive advantage. The technical aspect of implementation is proving to be far simpler than many large-scale projects have been, traditionally. By developing clear AI business strategies, companies are creating business value that’s incremental to the main goal they sought to achieve. Stakeholders across these projects are supportive, but with the caveat that such support requires nurturing, empathy, and a sensitive approach.
AI is automation, but cleverer
A common hurdle companies look to overcome at the outset of AI adoption is pinpointing what it can do for their business. What tangible outcomes can they expect? Will it change their relationship with the existing technology they know and trust, causing upheaval, or will it augment it? Will it necessitate the reskilling of staff?
Most important of all, how will an AI business strategy drive a better connection with customers, leading to revenue growth?
To find an answer to these challenging questions, and to harness AI value creation, the start point is to demystify AI. Many businesses view AI programmes as a route to long-term transformation, yet there are many benefits to be gained along the way. AI is simply clever automation, a way of creating smarter workflows which, in themselves, empower people. At a basic level, it’s about making everyday work easier by reducing manual effort and improving research and analysis. That gives people more time to think, plan and innovate, instead of getting tied up in routine tasks.
AI complements people’s skills but it also liberates their talents. One of the key findings in a survey undertaken by McKinsey & Company, ‘The state of AI in 2025: Agents, innovation, and transformation’ identifies the redesigning of workflows as a key success factor: “Half of AI high performers intend to use AI to transform their businesses, and most are redesigning workflows”.
Why AI is the new driver of improved customer service
Companies often see the adoption of AI technologies as the latest big step in the ongoing saga of digital transformation. People commonly ask if AI is worth the fuss that surrounds it, or even if it has any relevance to their business. Surely it’s best left to early adopters so that a business can wait and see more potential benefits when it’s at a more mature stage?
The misassumption in this line of thinking is that AI is complex and requires years of investment before it can deliver meaningful results. This is one of the key aspects of AI we discuss with customers; AI is far easier and faster to adopt than many realise. Abundant capabilities can be exploited by the accessible, low-risk solutions achievable with AI that can deliver tangible value today, particularly in sales, inventory management, and real-time business intelligence. Companies grabbing the opportunities now are gaining the competitive advantage now.
Working alongside our customers we identify and agree opportunity areas that are there for the taking. They include demand forecasting, and customer service automation for handling product or service queries and frequently asked questions. They might cover making returns prediction and prevention easier.
When AI-powered reporting is introduced, customers find they have access to insights they can use in real time, to spot further areas for improvement. This level of detailed reporting also enables businesses to deal with problems as they arise, rather than having to respond to them once they have a chance to multiply. In short, they make it possible for our customers to serve their customers better.
The incremental value advantage of AI.
Incremental value can be defined in two ways, internal and external. Internally, by integrating AI into your workflows, you can look forward to improvements not just in your technology but in the talents of your people. This relates to the introduction of smarter ways of working. These might include the reduction in steps a user has to undertake to use an application, for example, where the reduction in time and effort required leads to improved productivity. It can relate to more intelligent ways of using your data, delivering more personalised propositions to customers or streamlining processes in your business operations.
From an external perspective, incremental value comes from creating and building more meaningful relationships with customers, anticipating their next requirement, cross or up-selling. New opportunities represent incremental value. Placing the creation of incremental value in your roadmap serves to amplify the entire value of the AI adoption project.
Jade Emmanuel, a Senior Data & AI Consultant at Columbus Global UK outlines the benefits of this approach as coming together in faster progress. “Gone are the days when an organisation might embark on the implementation of, for example, an ERP system, and then only start to see how effective it is, or not, 18 months later. Fast returns are critical in the business world. In our work in creating AI solutions for customers, we look for visible progress in weeks and months, rather than years.
Quick wins are there for the taking when an organisation clearly defines what the wins might be at the outset of the project and remains flexible as the project gets underway. All an organisation really needs, in order to make the most of these quick wins, is the preparedness and the ability to explore unexpected benefits as the project moves forward and not set them aside because they weren’t included in the original project scope”.
The four point planning framework for AI success
Here are four points you might wish to bear in mind when putting your AI business strategy together:
- Define what success will look like
Remove the emphasis of KPIs from the realm and the language of technology. The ultimate success of the solution (or solutions) you’re putting in place will lean heavily on user adoption. Paving the way for this to happen will depend on the degree to which you involve key stakeholders and decision makers from the start. Establishing shared goals helps ensure that every AI initiative aligns with outcomes that matter to the business.
- Establish benchmarks
Clarity of the practical, visible, and measurable results you’re setting out to achieve will help you measure the value unlocked by AI solutions. All the time, keeping the focus firmly on tangible results creates enthusiasm within the business. This ‘tangibility’ comes from looking at how long a certain process takes now (for example, reconciliations for financial reporting), and what your target timing is when AI is implemented.
- Clarify the desired changes sought in key timing milestones
Imagine the changes the business would like to see, with timelines attached; three/six months, or a year from now, demonstrating the business impacts as they materialise. If measurable business outcomes can be demonstrated, it means that ROI expectations can be confirmed, or at least will be once any adjustments are made that may come from laying bare the solution for all to see.
- Ensure your roadmap is phased and progressive
The essence of a roadmap that builds in high points of achievement as it progresses towards its overriding goal, helps address those ‘stubborn growing pains’ referred to in the McKinsey report. In a phased, progressive roadmap, each phase isn’t just about technology implementation, but also addresses people, processes, and culture—clearly outlining roles, accountability, timelines, and risks.
Breaking larger transformation goals into pragmatic steps, each with defined success criteria, maintains momentum and adaptability. Working with a manufacturing and retail customer, the Columbus team started with low-risk automation to reduce manual case handling, then built on that foundation with predictive capabilities to support proactive decision-making. In this way, the foundation was set for a broader adoption of agentic AI solutions to drive further efficiencies.
Validating the improvements made at identified stages can have one of two outcomes. Firstly, they may be validated, and the project continues. Alternatively, problems may be encountered at this stage, making it necessary to refine the system. This is agility in action, giving the technical team the correct steer to make the solution not just good, but the best it can be.
At this point, the team can pivot if needed, try a different approach and come back with a better solution. Because stakeholders have been involved throughout, they’ve seen the issue for themselves, understood the implications, and feel part of the solution.
AI value creation one step at a time
Businesses are seeing the benefits of AI in areas such as quality improvements, increased productivity, more responsive and meaningful customer service, and supply chain optimisation. They’re working, innovating, and growing through their AI investments and, what’s more, they’re doing it fast. Once you view AI as clever automation and embrace the idea that the creation of real business value is more easily within your grasp when tackled in incremental steps, you’ll see the change. Your stakeholders will welcome it. Your business success will validate it.
The data and AI team at Columbus frequently works with companies to build AI strategies designed uniquely to address their challenges when it comes to considerations such as the foregoing. We help address their data maturity and analytics strategies, with a focus on user adoption. Every business is different, meaning it’s unlikely that off-the-shelf solutions will work for all. That’s where ‘unique’ comes in, tailoring ways of achieving your goals that will sit comfortably in your business and with your users.