As artificial intelligence (AI) continues to advance, UK businesses are grappling with the question of how to integrate these tools into their company effectively. They’re excited about the potential to gain a competitive advantage, but overwhelmed with questions such as ‘where do we start?’ or ‘what will have the biggest impact?’ or ‘are we already behind?’
AI is not only transforming technology, but fundamentally transforming how we work by creating hybrid teams made of humans and machines working together. This means it’s not just about adopting the technology but managing change and uncertainty among team members.
We sat down with our Head of Technology, Chris, who shares his perspective on the state of AI adoption among businesses, the challenges companies face, and how UK companies can successfully navigate the AI revolution.
The State of AI Adoption in UK Businesses
AI adoption varies across industries. Some companies are already seeing the benefits, while others are still in the early stages of integration. ‘AI presents immense potential for revenue growth, especially when integrated into marketing, sales, and customer service,’ says Chris, Head of Technology at Innovation Visual. Recently, we helped a client save 93 hours a month and double sales velocity by implementing low-code and AI integrations, demonstrating the significant ROI and efficiency gains possible.
Other research has suggested that organisations that have successfully deployed AI agents over the next 12 months will achieve a competitive advantage, with AI agents projected to generate $450 billion in economic value through revenue uplift and cost savings.
Despite the benefits, many companies hesitate to adopt AI. As Chris points out, ‘many businesses are unsure how to take the first step or how to measure success effectively.’
Beyond the Chat Interface: Understanding True AI Integration
The most common misconception about AI adoption centres on what it actually means to ‘use AI.’ True integration requires adoption at an organisational level by embedding in company processes, rather than individuals in companies adopting AI at a personal level.
This distinction is crucial for revenue operations. The real value lies not in ad-hoc usage but in systematic integration across three key areas:
- People: Aligning individual AI usage with team objectives
- Processes: Identifying and automating friction points that have plagued operations for years
- Systems: Creating deterministic agents that handle background tasks seamlessly
The goal isn't to replace human judgement but to eliminate the manual, repetitive work that consumes valuable time.
Key Challenges to AI Adoption
Source: HubSpot
So, what are the barriers to AI adoption?
- Lack of strategic alignment and silos – Companies are adopting AI in silos rather than aligning it to broader business goals and processes. ‘Most companies have early adopters within their teams,’ Chris observes, ‘people who are tech savvy, people who are willing to experiment and explore new technologies.’ However, organisations are failing to harness this energy strategically. For revenue leaders, this represents both a challenge and an opportunity – the drive to experiment exists within certain team members, but it needs to be channelled effectively.
- Security/governance – Some businesses hesitate to grant AI systems access to sensitive data, fearing breaches or misuse. Companies must establish clear policies and controls to ensure responsible AI use.
- The fear factor – Leaders are managing teams that may be resistant to change and potentially worried about how AI is going to change their job role or the industry they operate in.
- Upfront investment - While the long-term ROI of AI can be significant, the initial cost of AI technology and the need for specialised expertise can be a barrier.
AI Adoption Roadmap for Revenue Leaders
For organisations ready to move beyond experimentation, Chris outlines a clear path forward:
Step 1: Tackle Change Management with Communication
AI is exciting for some and scary for others. As an organisation, your leadership needs to provide clear communication around it. Share your plans with employees and be transparent on how AI will be used and what you want to achieve. This technology is fundamentally changing the way we work, so change management is critical. This isn’t a one-off memo; you will need to keep communicating to maintain alignment across the organisation.
Step 2: Harness Existing Energy
‘Form an AI task force,’ Chris suggests. Bring together those early adopters who are already experimenting. This creates knowledge sharing and prevents duplicate efforts where "you'll have people in the business who are essentially tackling the same problems with different tools." Try to gather team members from different departments to benefit from cross-functional experimentation.
Use these champions to then train the broader workforce and upskill them. You cannot expect everyone to learn or solve problems by themselves, so ensure you have the right programmes in place so everyone can benefit from AI in their day-to-day job.
Step 3: Establish Clear Governance
Develop explicit policies around data usage, content generation, and tool adoption. ‘You want to be able to take people with you on the journey so that they're part of the shift. They're using the AI tools, not being replaced by them.’
Step 4: Focus on Friction Points
Rather than adopting AI for its own sake, identify specific pain points and map out processes that can be done with automations and AI. ‘It's about what problem we actually want to solve?’ This prevents the common mistake of ‘just checking a box to say, yep, we've got AI now.’
Step 5: Think Holistically
Avoid siloed adoption where ‘your marketing and sales team are both going off and buying very similar tools, doubling your expenses." Instead, approach AI adoption as an organisational capability that spans departments.
Audit your Tech Stack to be AI-Ready
This holistic approach is precisely why now is the ideal time to conduct a comprehensive tech stack audit.
Before adding AI tools to an already complex technology ecosystem, it’s crucial to understand your current tech stack’s strengths, gaps, and redundancies. Our tech stack audits can reveal:
- Where existing tools already have AI capabilities you’re not leveraging
- Where you may be able to consolidate tools to reduce costs and future proof your tech stack
- Which processes create the most friction between platforms
- Where data silos may prevent effective AI implementation
Chris emphasizes that friction often ‘might live in that grey area where one platform stops and another one starts, and maybe no one quite has ownership over that process or that connection.’ These are exactly the areas where AI can deliver immediate value – but only if you identify them first.
Measuring What Matters: The ROI of AI Adoption
For revenue leaders pressed to justify AI investments, the metrics are surprisingly straightforward. The primary value drivers include:
- Time efficiency: Reducing hours spent on manual data entry and administrative tasks
- Headcount optimisation: Scaling operations without proportional increases in staff
- Speed to market: Accelerating campaign development from weeks to hours
- Sales productivity: Increasing actual selling time versus administrative overhead
Chris emphasises the importance of baseline measurement: ‘We're very lucky as a business, we do a lot of time tracking, so we know where we spend our time and it makes it very easy for us to then benchmark against historical time.’
Source: HubSpot
The Emergence of the Hybrid Team
Looking ahead, Chris predicts a fundamental shift in how we interact with AI. ‘I think it is moving away from AI being just thought of as a chat interface,’ he explains. The future lies in deterministic agents working seamlessly in the background and the emergence of truly hybrid teams where AI agents function as colleagues rather than tools.
This evolution demands new management approaches. ‘If these are, for all intents and purposes, part of your team because of their workload and their output, then that does need management,’ Chris observes. The implications for organisational structure are profound – imagine senior roles without human reports, managing AI agents instead.
The Imperative for Leadership
For revenue leaders, the message is clear: the question isn't whether to adopt AI, but how to do so strategically. The organisations that will thrive aren't necessarily those with the most advanced AI tools, but those that successfully bridge the gap between individual enthusiasm and organisational strategy. Those who choose to lead this transformation – creating policies, aligning initiatives with business objectives, and taking their teams on the journey – will find themselves with a significant competitive advantage. Those who don't risk becoming increasingly irrelevant in a rapidly evolving marketplace.
As Chris concludes, ‘You're better to be riding the wave than just getting swamped by it.’
