technologyreseller.co.uk 25 At this point, patterns will start to emerge and you can experiment with automations. Your Centre of Excellence should capture and codify the best approaches so they can be deployed more broadly. Next, start to look for data‑driven improvements that cut across functions. The nature of this analysis will be specific to the sector in which you operate, but there are some data types that are more universal, like sales transactions, delivery timescales and client servicing figures, that can be consolidated and analysed to discover trends around profitability, waste, productivity etc.. At some point, the conversation will change from ‘how do we use AI here?’ to ‘how does AI reshape the way we operate?’, which is a cue to examine processes end to end. If incident patterns can be predicted, redesign processes to prioritise prevention over reaction; if drafting and analysis have become faster, shift time allocation towards discovery, advisory and creative problem‑solving; if every team has an assistant, define which steps are AI by default, clarify responsibility boundaries and adjust incentives to reward outcomes rather than effort. Common pitfalls to avoid Inevitably, there will be pitfalls. Ones that show up frequently are: Tool sprawl, which wastes money and fragments knowledge. A curated catalogue and shared patterns help keep efforts aligned. Over‑trusting outputs, leading to brand or compliance risks. Keeping a human reviewer in the loop for sensitive work preserves trust. Copying confidential content into unapproved tools, putting sensitive data at risk of exposure. Ensure you only use approved tools with clear do/ don’t guidelines for your data. Chasing the wrong AI talent or tools, which will inflate costs without improving outcomes. Prioritise AI‑literate analysts who can integrate tools into real workflows. Only use proven AI products or limit the usage of untested tools until they have proven their value. Treating adoption as a one‑off project is perhaps the biggest pitfall of all. AI is not something you simply cross off your to do list. AI is a capability that compounds over time. Your organisation will get better at using AI, and get more value from it, the longer you use it. supported by a dedicated team and can help guard against Shadow AI. Be vigilant. Bear in mind that tools differ in their maturity, and even the best can be wrong. Always be on the look-out for ‘hallucinations’, where the AI model confidently generates incorrect or misleading information. Teach people to verify claims, check sources and watch for signs of manipulated content or fabricated references. Embrace the idea that you will ‘fail your way to success’. The pace of change in AI is so rapid that if your strategy is to wait for perfect information you will quickly fall behind. Begin with low‑risk, high‑frequency tasks: meeting summaries, email and document drafting, light analysis with human cross‑checks. Run short pilots and keep what works, drop what doesn’t. The goal should be momentum, learning and measurable outcomes, not a grand plan that goes nowhere. Borrow patterns from larger organisations, then simplify. Enterprises will often publicise what works well. Your CoE can translate these into a right‑sized set of guardrails, avoiding reinvention while minimising overhead. Finally, understand how disruption varies by sector and by task. Legal, marketing, customer service and elements of finance may see faster automation than asset‑heavy manufacturing for example. Map your work by tasks and assess the predictability of each task, the level of document/data involved, its frequency, risk levels and visibility to customers. This will give you a heat map showing where AI can create value quickly and where extra controls are essential. A natural adoption journey Given these guidelines, what might a sustainable journey to achieving maximum value from AI look like for an SME? Its starting point should be where friction is lowest, i.e. within the tools people already use, as this will generate immediate payoff and generate curiosity to go further. Begin with AI assistants for everyday tasks, like meeting transcription and summaries. Then, as confidence grows, move from isolated tasks to small workflows: link an AI‑assisted summary to a follow‑up plan, for example, or pair a proposal draft with a review checklist. the way models behave changes all the time. We have learned to update our AI policy frequently and to treat it as a living document. The substance of that policy is straightforward: data access should follow least‑privilege principles; sensitive outputs require human review; confidential information must stay within approved environments; logs should provide auditability for significant automated actions; and adoption should be measured alongside outcomes so we can course‑correct. From small wins to reimagining the business At Phoenix Software, we deliberately started with many ‘small things’, e.g. agents that summarise emails and meeting notes, assistants that draft content, tools that accelerate analysis etc., because they deliver value quickly and build confidence. However, the real inflection point came when we zoomed out and started to think how work could flow across the organisation if everyone had AI assistance. This meant asking different questions: If support can predict incident patterns, how should services be designed? If sales can automatically triage and enrich enquiries, how does the funnel change? If data analysis is faster and richer, where can you shift time towards discovery, customer conversations and creative problem‑solving? Tips for SMEs considering AI SMEs often tell us they want to move forward with AI but feel overwhelmed at the prospect. Based on what we’ve learned and what we’ve seen work in our business, we offer the following advice: Start by putting simple governance in place. Clarify which tools are approved, which data they can touch and which tasks need human oversight before anything leaves the building. This makes early experimentation productive rather than risky. Create a lightweight AI Centre of Excellence. In many SMEs, two or three people are enough: one with security or IT responsibility, one with data or analytics experience, and one from a business function such as operations or finance. Give them a clear remit to monitor developments, publish standards, coach teams, track outcomes and keep adoption aligned with business goals. This will empower employees to drive AI innovation AI continued...
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