24 01732 759725 guidelines on which tools are approved and for what; which tasks require human review; how to handle sensitive data; and how to share learnings. Find and scale the use cases that deliver ROI AI has proved useful across our business at two levels – everyday productivity and bigger, cross‑functional improvements. For small but frequent tasks AI delivered immediate gains. Drafting proposals and statements of work was faster because the ‘blank page’ disappeared; long documents became more workable thanks to quick summaries; and meeting transcriptions and summaries dramatically improved team outcomes. On the bigger, cross‑functional side, we now use AI to analyse consultancy documents for structure and clarity, to triage and enrich sales enquiries and, perhaps most impactfully, to spot trends in support tickets. We process thousands of customer support tickets every day and troubleshoot hundreds of different software and hardware solutions. Trend analysis means we can now see – across the whole company – which technologies generate recurrent issues, enabling us to guide proactive support, identify automation opportunities, steer future product recommendations and surface potential new solutions that we can take to market ourselves. AI has also reshaped our workforce plans. Initially, we anticipated hiring more data scientists, but in practice, better tooling and embedded capabilities have reduced the need for heavy bespoke modelling. As a result, we are now focusing on AI‑literate analysts, solution architects and product owners – roles that combine domain knowledge with the ability to wield AI responsibly and effectively. This shift has boosted productivity, allowing staff to spend less time on mundane tasks and more on tasks that require judgement and on developing relationships. Governance must evolve as fast as the technology Traditional IT policies can remain unchanged for years, but not AI policies. New features arrive relentlessly, and Early adopter In my role as CTO of Phoenix Software, I’ve spent the last two-plus years learning about AI and determining how we can implement it within our business. Since we’re also a major IT services provider that has implemented hundreds of AI projects for customers, I have approached the topic from the different perspectives of an adopter and a provider of AI. One of the advantages of being a major IT services provider and one of the most highly accredited resellers in the UK is that we gained private preview early access and hands‑on experience of generative AI before its general release to customers. This has enabled us to deploy AI deeply within the tools we already use and leverage that experience to provide practical guidance to our customers. Here’s what we’ve learned in the two-plus years since generative AI went mainstream with the launches of ChatGPT Enterprise in August 2023, Microsoft 365 Copilot in November 2023 and Google Gemini in December 2023. AI must be a discipline, not a side project One of the most important decisions we made early on was to treat AI as a core business discipline. To this end, we set up dedicated teams, effectively creating an AI Centre of Excellence to scan the fast‑moving vendor landscape, select the right tools and ensure every deployment had a business case attached to it. This has kept us focused on problems worth solving, avoided tool sprawl and created reusable patterns so that one team’s success can be adopted company‑wide. Encourage experimentation, but with direction and guard-rails We actively encourage staff to use AI wherever it helps them, for example in drafting emails, summarising meetings, synthesising research or building first‑pass analyses. However, leaving adoption entirely to enthusiastic individuals creates uneven quality and hidden risks. The task is simply too large for ad‑hoc efforts alone. What has made experimentation productive for us have been clear For the best part of two decades, since Steve Jobs took to the stage in 2007 to announce the very first iPhone and kickstart the smartphone revolution, Silicon Valley has been a seedbed of new, exciting and disruptive technologies. Silicon Valley has always been an engine for change, but I would argue that its current position as the preeminent driver of almost everything began with this now legendary keynote speech, since which we’ve experienced the transformative effects of social media, Big Data, the Internet of Things, cloud, digital transformation and AI, not to mention media streaming, cryptocurrencies, electric vehicles and virtual and augmented reality, all of which can trace their roots to the Valley. During this relentless period of innovation, Silicon Valley has become the world leader in another precious commodity – hype. Nowhere else can generate as much hype and mouthwatering investment as Silicon Valley, frequently to a level that far exceeds the real-world capability, or even potential, of the idea itself (NFTs anyone?). In the case of AI, I would argue that the hype, relentless as it is, may even be underselling the technology. Craig Taylor, CTO of Phoenix Software, outlines the lessons he has learned as both an early adopter and a provider of generative AI solutions Lessons from the AI frontline AI Craig Taylor
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