AI Guides for UK SMEs
Why I am creating these guides
Throughout 2026, I will be producing a series of practical AI guides. These guides save you time, reduce risk and help you make better decisions.
Each guide will show you exactly what AI could mean for your business, which opportunities are real, where the dangers are, and how to get your people working confidently with new tools. You get practical steps you can use right away, without hype or technical jargon.
The result is simple: clearer strategy, faster progress and fewer costly mistakes.
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Who these guides are for
Business owners
Who want to understand AI before committing budget or time
Senior leaders
Who need to brief their board or make strategic decisions
Team managers
Who want their people working confidently with AI tools
Dive in
Everything you need to get started
Jump to any section. Each one stands alone.
What is the current state of AI adoption in UK businesses?
Only 31% of small UK businesses currently use AI. Real data showing where UK SMEs stand today, and the opportunity gap for those who act now.
Current AI Adoption
Source: ONS Business Insights Survey, 2024
Barriers to AI Adoption
Source: YouGov/ANS Research, 2025
Which AI tool should your business use?
ChatGPT, Claude, Gemini, Perplexity, NotebookLM, DeepSeek and Mistral each have different strengths. Here's how they compare for UK business use.
My recommendation for UK SMEs
Start with a free version of ChatGPT or Claude for general business use, they offer the best balance of capability and ease of use. Once confident, upgrade to the paid version and ideally the Teams subscription, but don't overstretch on licences. Add Perplexity for research tasks requiring current information and citations. If you're a Google Workspace business, Gemini integrates seamlessly. If you're a Microsoft 365 business, Copilot brings AI directly into Word, Excel, PowerPoint and Teams. For UK businesses with strict data compliance requirements, consider Mistral (EU-based) or enterprise tiers of the major providers. We cover practical use of relevant tools in our workshops.
Why upgrade to paid subscriptions? The difference between free and paid tiers is substantial. Paid subscriptions give you access to significantly more capable models that produce better quality output, handle complex tasks more reliably, and work faster. Equally important: paid and business tiers include stronger data security commitments in their terms of service. Your conversations and uploaded documents typically aren't used to train their models, and you get clearer guarantees about data handling. For any serious business use, the subscription cost pays for itself quickly in both quality and peace of mind.
AI Glossary: Essential terms explained
Understanding AI doesn't require a computer science degree. Here are the key terms you'll encounter, with both technical and business definitions.
How AI systems work together
This diagram shows how AI transforms your questions into useful responses. Each component plays a role in making AI practical for business.
Large Language Model (LLM)
Think of it like: A well-read colleague who's absorbed millions of books, articles, and conversations, and can help you write, research, and think through problems.
The engine behind ChatGPT, Claude, and Gemini. When you type a question, the LLM predicts the most helpful response based on patterns learned from vast amounts of text. It doesn't "understand" like a human, but it's remarkably good at generating useful, coherent answers.
Real Business Example
A recruitment agency uses Claude to write initial job descriptions. The hiring manager provides bullet points about the role; Claude drafts a polished description in their brand voice. What took 45 minutes now takes 5, and the manager's expertise shapes the final version.
Hype vs Reality
Hype: "AI will replace all knowledge workers." Reality: LLMs are brilliant first-draft machines and thinking partners. They speed up work dramatically, but your expertise, judgement, and final review remain essential. The best results come from human + AI collaboration.
Opportunities
First drafts of any document in minutes. Instant summaries of long reports. Brainstorming on demand. Customer query responses 24/7. Translation and localisation. Research synthesis.
Considerations
Can produce confident-sounding errors. Needs human review for anything published. Data privacy varies by provider and plan. Team training ensures consistent quality.
Prompt
Think of it like: Briefing a skilled freelancer. The clearer your brief, the better the work you get back.
The instructions you give to AI. A prompt can be a simple question or a detailed brief including context, examples, format requirements, and constraints. Learning to write clear prompts is the single most valuable AI skill, and it's much simpler than the hype suggests.
Real Business Example
Weak prompt: "Write me an email about the project delay." Strong prompt: "Write a professional email to our client Sarah at Acme Corp. Explain that our website redesign will be delivered 2 weeks late due to unexpected technical issues with their legacy CMS. Apologise sincerely, offer a 10% discount on the next phase, and propose a call to discuss the revised timeline. Tone: warm but professional. Keep it under 200 words."
Hype vs Reality
Hype: "You need to master complex prompt engineering formulas." Reality: Good prompting is just clear communication. Include context, be specific about what you want, give examples if helpful. No magic words needed, explain what you want as you would to a capable colleague.
Opportunities
Transform output quality instantly. Build a library of reusable prompts for common tasks. Share winning prompts across your team. Reduce revision cycles dramatically.
Considerations
Takes a few iterations to find what works. Different AI tools respond differently. Overly complex prompts can confuse. Start simple, add detail as needed.
Context Window
Think of it like: The AI's working memory, the size of desk it has to spread out your documents.
The amount of text an AI can "hold in mind" at once. Measured in tokens (roughly 1 token = ¾ of a word). Claude can handle 200,000 tokens (about 150,000 words, a thick novel). GPT-4 handles 128,000. This determines how much you can paste in and how long a conversation can continue coherently.
Real Business Example
A property company uploads their 80-page lease agreement to Claude and asks: "What are all the break clauses, rent review mechanisms, and obligations on the landlord?" Claude reads the entire document and provides a structured summary in seconds, work that would take a junior solicitor hours.
Hype vs Reality
Hype: "Bigger context window always means better answers." Reality: Large context windows are genuinely useful for analysing long documents. But AI attention can fade in very long inputs, information at the start and end gets more focus. Quality of input often matters more than quantity.
Opportunities
Analyse entire contracts, reports, or policy documents at once. Compare multiple documents. Maintain coherent project conversations over time.
Considerations
Larger inputs cost more tokens/money. Break very long documents into focused chunks for better results. Start new conversations when topics change.
Hallucination
Think of it like: A confident colleague who occasionally makes things up, but sounds completely certain while doing it.
When AI generates plausible-sounding information that is factually wrong. It might invent statistics, cite non-existent research papers, or state falsehoods with complete confidence. This happens because LLMs predict likely-sounding text, they don't "know" what's true.
Real Business Example
A marketing manager asks ChatGPT for UK social media statistics. It responds with precise-sounding figures: "47.3% of UK SMEs use LinkedIn for B2B marketing (Ofcom, 2024)." The manager checks, Ofcom published no such study. The number was invented. She now treats all AI statistics as "indicative only" and verifies before publishing.
Hype vs Reality
Hype: "AI is unreliable and dangerous." Reality: Hallucinations are a genuine limitation, but they're predictable and manageable. AI excels at structure, tone, and ideas. Humans verify facts. Build this into your workflow and AI becomes remarkably reliable.
Opportunities
Build "trust but verify" into your workflow. Use AI for drafting, humans for fact-checking. Use RAG to ground AI in your own data. Ask AI to cite sources (then check them).
Considerations
Never publish AI-generated facts without verification. Extra caution for legal, medical, financial content. "Made-up" doesn't mean "wrong", just unverified. Train your team on this.
RAG (Retrieval Augmented Generation)
Think of it like: Giving your AI assistant access to your company's filing cabinet before it answers questions.
A technique that fetches relevant information from your own documents and feeds it to the AI along with your question. Instead of relying on its general training, the AI answers based on your specific policies, products, and data. Dramatically reduces hallucinations and makes responses genuinely useful for your business.
Real Business Example
An accountancy firm uploads their 200-page employee handbook to NotebookLM (Google's free RAG tool). New staff can ask questions like "What's our policy on working from home?" and get accurate answers that quote the actual handbook, with page references. HR enquiries drop by 60%.
Hype vs Reality
Hype: "You need expensive custom AI to use your own data." Reality: Free and low-cost RAG is here today. NotebookLM is free. ChatGPT Plus lets you upload documents. Claude handles 150,000 words in a single conversation. Enterprise RAG exists for complex needs, but most SMEs can start immediately.
Opportunities
Customer service chatbots using your actual FAQs. Internal knowledge search. New employee onboarding. Compliance Q&A. Product information for sales teams.
Considerations
Garbage in, garbage out, document quality matters. Needs updating when policies change. Consider data security before uploading sensitive docs. Start with non-confidential content.
AI Agent
Think of it like: A capable assistant who can not only write the email, but also send it, book the meeting, and update the spreadsheet.
AI that can take actions, not just generate text. An agent can browse the web, use software, send emails, update databases, and complete multi-step tasks autonomously. Where ChatGPT writes a draft, an agent can send it. This is the fastest-moving frontier in AI.
Real Business Example
A sales manager tells an AI agent: "Find the 20 largest accounting firms in Manchester, get their contact details, and add them to our CRM with 'cold outreach' tag." The agent searches, extracts data, creates CRM entries, and reports back, a morning's work done in 15 minutes.
Hype vs Reality
Hype: "AI agents will run your entire business autonomously." Reality: Agents are genuinely powerful for defined, repeatable tasks. But they need clear boundaries and human oversight. An agent that sends wrong emails to all your clients is a crisis. Start with low-risk tasks, build trust gradually.
Opportunities
Automate research and data gathering. Lead qualification and enrichment. Routine communications. Report generation. Meeting scheduling. Data entry and CRM updates.
Considerations
Errors can cascade quickly. Set clear permission boundaries. Review outputs before they go external. Build in "human approval" checkpoints. Start with internal, low-stakes tasks.
MCP (Model Context Protocol)
Think of it like: A universal adapter that lets AI plug into any software, like USB-C for AI connections.
An open standard (created by Anthropic) that lets AI tools connect securely to your business software, CRMs, databases, file systems, calendars. Instead of building custom integrations for each AI tool, MCP provides a single, standardised way to connect. This makes AI agents dramatically more useful and secure.
Real Business Example
A consultancy connects Claude to their Notion workspace via MCP. Now team members can ask: "What did we propose to Acme Corp last quarter?" and Claude searches their actual proposal documents, securely, without data leaving their systems. Previously impossible without expensive custom development.
Hype vs Reality
Hype: "MCP will immediately transform how you work." Reality: MCP is genuinely important, it's the infrastructure that makes AI agents practical for business. But it's early days. Some integrations exist today (Google Drive, Slack, databases); many more are coming. Worth understanding now, implementing over the next 12 months.
Opportunities
AI that works with your actual business data. Secure connections without custom development. Switch AI providers without losing integrations. Future-proof your AI investments.
Considerations
Still emerging, not all tools support it yet. May need technical setup initially. Security review still essential. Check what integrations exist before planning around MCP.
Fine-tuning
Think of it like: Training a new employee on your company's specific way of doing things, except it's permanent learning for the AI.
Teaching an existing AI model your specific patterns by showing it examples of what "good" looks like for your business. The model adjusts its behaviour to match your style, terminology, or specialised knowledge. Different from RAG (which gives AI information to reference) because fine-tuning changes how the model responds.
Real Business Example
A legal firm fine-tunes a model on 10,000 of their past client letters. Now when drafting correspondence, the AI automatically uses their exact tone, structure, and legal terminology. New associates produce client-ready drafts faster because the AI already "sounds like the firm."
Hype vs Reality
Hype: "Every business needs a custom AI model." Reality: Most SMEs get excellent results from standard models with good prompting and RAG. Fine-tuning is powerful but complex and costly. Exhaust simpler options first. Fine-tune when you have thousands of examples and a clear, repeatable use case.
Opportunities
Consistent brand voice across all outputs. Domain-specific expertise. Faster responses for common patterns. Competitive advantage through customisation.
Considerations
Needs thousands of quality examples. Technical expertise required. Ongoing maintenance as base models evolve. Cost and complexity, try prompting and RAG first.
Tokens
Think of it like: The "words" AI counts when billing, but roughly 1 token = 3/4 of an actual word.
How AI measures text. LLMs break text into tokens, chunks that might be words, parts of words, or punctuation. "Understanding" might be one token; "misunderstanding" might be two. API pricing is based on tokens used (input + output). Most subscription plans (ChatGPT Plus, Claude Pro) give you unlimited tokens for a fixed monthly fee.
Real Business Example
A content agency builds a custom tool using the OpenAI API. They estimate costs: analysing a 2,000-word blog post (≈2,700 tokens input) and generating a 500-word summary (≈670 tokens output) costs roughly £0.01 with GPT-4. For 1,000 summaries per month, that's £10, less than one hour of a writer's time.
Hype vs Reality
Hype: "AI is too expensive for small businesses." Reality: Subscription plans (£16-20/month) offer extraordinary value, unlimited use for the price of a business lunch. API costs are fractions of pennies per request. Token economics strongly favour adoption.
Opportunities
Predictable budgeting with subscriptions. API for high-volume automation at low cost. Choose models based on cost/quality tradeoffs. Scale usage without scaling costs proportionally.
Considerations
API usage can add up at scale, set spending limits. Subscriptions have usage caps during peak demand. Monitor usage in early weeks to understand patterns.
Multimodal AI
Think of it like: An AI that can see, hear, and read, not just process text.
AI that works with multiple types of input: text, images, audio, video, documents. You can show GPT-4 a photo of a whiteboard and ask it to transcribe and organise the notes. Or upload a PDF with charts and ask questions about the data. This dramatically expands what AI can help with.
Real Business Example
An insurance claims handler photographs vehicle damage and uploads to Claude. "Assess the damage severity, estimate repair categories, and flag anything suspicious." The AI analyses the image and provides a structured preliminary assessment, streamlining the claims process.
Hype vs Reality
Hype: "Multimodal AI can understand anything you show it." Reality: Image and document understanding is genuinely impressive. But accuracy varies, handwriting recognition is imperfect, complex diagrams can be misread. Best for structured documents and clear images. Always verify important interpretations.
Opportunities
Analyse charts and graphs in reports. Extract data from images of forms. Describe photos for accessibility. Process mixed documents (text + tables + images). Voice input for hands-free use.
Considerations
Image quality affects accuracy. Handwriting and complex layouts can confuse. Privacy concerns with photos of people/sensitive content. Verify extracted data.
API (Application Programming Interface)
Think of it like: A waiter in a restaurant, you tell them what you want, they take your order to the kitchen, and bring back the result.
How software talks to other software. An API lets your website, app, or internal tool send requests to AI and receive responses automatically. Instead of copying and pasting into ChatGPT, you build AI directly into your workflow. APIs enable automation at scale.
Real Business Example
An e-commerce company builds a "product description generator" using the OpenAI API. Upload a product image and bullet points; AI generates SEO-optimised descriptions automatically. What took their team 20 minutes per product now takes 30 seconds, and 50 products can be processed overnight.
Hype vs Reality
Hype: "You need developers to use AI APIs." Reality: Traditional API integration does require coding. But no-code tools like Zapier, Make, and n8n now offer AI integrations without programming. For custom solutions, APIs are increasingly accessible with AI-assisted coding.
Opportunities
Embed AI in your existing systems. Automate repetitive processes end-to-end. Build custom AI tools for your specific needs. Scale without manual intervention.
Considerations
Custom development has upfront costs. Pay-per-use can add up at high volume. Need error handling for when AI fails. Monitor and test automations regularly.
Temperature
Think of it like: A "creativity dial" for AI, turn it down for predictable, factual responses; turn it up for more varied, creative outputs.
A setting that controls how random or deterministic AI responses are. Temperature 0 = always picks the most likely next word (consistent, predictable). Temperature 1 = introduces randomness (varied, creative). Most business use cases work best at low temperatures (0.3-0.5).
Real Business Example
A compliance team uses AI to draft policy documents. They set temperature to 0.2, they need consistent, accurate language every time. The marketing team uses the same AI for social media ideas at temperature 0.8, they want varied, creative suggestions they can choose from.
Hype vs Reality
Hype: "Temperature dramatically changes AI quality." Reality: Temperature affects style more than quality. Low temperature = more predictable. High temperature = more varied (but not necessarily better). For most business tasks, default settings work well. Adjust if you need more creativity or more consistency.
Opportunities
Low temp for: data extraction, code, compliance, customer service. High temp for: brainstorming, marketing copy, creative exploration, generating alternatives.
Considerations
High temperature increases unpredictability (and potential errors). Most users never need to adjust this. Focus on prompt quality first, temperature is fine-tuning.
Generative AI
Think of it like: AI that creates rather than just analyses, a tool that produces new content rather than just sorting existing information.
AI that generates new content, text, images, code, audio, video, based on patterns learned from training data. ChatGPT, Claude, Midjourney, and DALL-E are all generative AI. This is what most people mean when they say "AI" in 2024-25. It's different from traditional AI that classifies, predicts, or analyses.
Real Business Example
A small marketing agency uses generative AI across their workflow: ChatGPT drafts blog posts and social captions, Midjourney creates concept visuals for client pitches, and Claude writes first-draft proposals. Output that once required a full day now takes an hour, with the team focusing on strategy and refinement.
Hype vs Reality
Hype: "Generative AI will make human creativity obsolete." Reality: It's an amplifier, not a replacement. Generative AI is brilliant at first drafts, variations, and exploration. Human creativity, judgement, and taste remain essential, the skill is knowing how to direct and refine AI output.
Opportunities
Content creation at scale. Rapid prototyping and ideation. Personalised communications. Code generation and debugging. Design exploration. Translation and localisation.
Considerations
Copyright questions around AI-generated content. Quality varies, always review outputs. Can feel generic without good prompting. Overuse can dilute brand distinctiveness.
AI Bias
Think of it like: AI inheriting the prejudices baked into its training data, if it learned from biased examples, it will produce biased outputs.
When AI systems produce unfair or prejudiced results because of biased training data, flawed assumptions, or poor design. An AI trained mostly on American business writing might miss British English nuances. An image generator trained on limited demographics might struggle with diversity. Bias isn't malicious, it's usually accidental, but the impact can be serious.
Real Business Example
A recruitment company tested an AI CV screening tool and discovered it was ranking male candidates higher, because it learned from 10 years of hiring data that reflected historical gender imbalance in their industry. They now use AI to generate shortlists but require human review before any candidate is rejected.
Hype vs Reality
Hype: "AI is objective and neutral." Reality: AI reflects its training data, including society's biases. The major providers work hard on bias reduction, but no system is perfect. The solution isn't to avoid AI, but to use it thoughtfully with appropriate human oversight.
Opportunities
Awareness enables better deployment. Bias testing before launch. Diverse perspectives in AI oversight. Use AI to augment decisions, not automate them entirely.
Considerations
High-stakes decisions (hiring, lending, legal) need extra scrutiny. Regulatory requirements around AI fairness are growing. Document AI usage for accountability.
Chatbot / Conversational AI
Think of it like: An automated receptionist that can handle routine questions so your team focuses on complex issues.
Software that simulates human conversation, either through text or voice. Ranges from simple rule-based bots ("If customer says X, respond with Y") to sophisticated AI that understands context and nuance. Modern chatbots powered by LLMs can handle surprisingly complex conversations, but the quality gap between good and bad implementations is enormous.
Real Business Example
A 50-person insurance broker added an AI chatbot to their website. It handles 70% of initial enquiries, qualifying leads, answering FAQs, booking callbacks, without human involvement. The team now only speaks to genuinely interested, pre-qualified prospects. Lead-to-meeting conversion doubled.
Hype vs Reality
Hype: "Chatbots can replace your customer service team." Reality: Chatbots excel at handling routine, predictable queries. Complex issues still need humans. The best implementations are honest about limitations and escalate gracefully. Customers hate being trapped with a useless bot.
Opportunities
24/7 availability. Consistent first response. Lead qualification. FAQ handling. Appointment booking. Internal helpdesk. Reduced support costs per query.
Considerations
Bad chatbots damage brand perception. Need clear escalation paths. Require ongoing training/refinement. Not suitable for emotionally sensitive situations.
Sentiment Analysis
Think of it like: Automatically reading the mood of thousands of customer comments at once.
AI that determines whether text expresses positive, negative, or neutral emotion. Can analyse customer reviews, social media mentions, survey responses, and support tickets at scale. More sophisticated systems detect specific emotions (frustration, delight, confusion) and identify what's driving them.
Real Business Example
A restaurant group runs 2,000+ Google and TripAdvisor reviews through sentiment analysis weekly. AI flags emerging issues ("slow service at Birmingham location," "portion sizes complaints increasing") before they become crises. Managers get a weekly digest with specific, actionable insights, not just star ratings.
Hype vs Reality
Hype: "AI understands exactly how customers feel." Reality: Sentiment analysis is very good at broad patterns and trends. It struggles with sarcasm, cultural nuance, and context. Best used for "directional signals" rather than precise measurement. Human review of edge cases still valuable.
Opportunities
Brand reputation monitoring. Customer feedback analysis. Social listening. Support ticket prioritisation. Campaign performance tracking. Competitor perception analysis.
Considerations
Struggles with sarcasm and irony. Cultural context matters. British understatement can be misread. Best as a trend indicator, not a precise measure.
Training Data
Think of it like: The textbooks and examples an AI studied before it graduated. It can only know what it was taught.
The information used to teach an AI model patterns and relationships. ChatGPT was trained on billions of web pages, books, and articles up to a cutoff date. The quality, diversity, and recency of training data directly determines what AI knows, and its blind spots. "Garbage in, garbage out" applies absolutely.
Real Business Example
A UK law firm asked ChatGPT about recent case law and received confident answers citing cases that either didn't exist or had different outcomes. The AI's training data had a 2023 cutoff. Now they use ChatGPT for legal research structure but verify all citations against Westlaw, and they've started using Perplexity for current information.
Hype vs Reality
Hype: "AI knows everything and is always current." Reality: AI has a "knowledge cutoff date" and may not know recent events, new regulations, or current prices. Some tools (Perplexity, ChatGPT with browsing) can search the web for current information, but this adds latency and cost.
Opportunities
Use RAG to supplement with your own current data. Choose tools with web access for time-sensitive tasks. Combine AI speed with human verification for accuracy.
Considerations
Check knowledge cutoff dates. Verify current information independently. Be cautious with regulations, pricing, and recent events. AI doesn't know what it doesn't know.
Model Drift
Think of it like: A map that was accurate when printed but hasn't been updated as the landscape changed.
When an AI model's accuracy degrades over time because the real world has changed since it was trained. Customer behaviour evolves, markets shift, language changes. An AI trained on 2020 data may underperform in 2025. Production AI systems need monitoring and periodic retraining to stay effective.
Real Business Example
An e-commerce company's product recommendation AI was trained pre-pandemic on in-store shopping patterns. Post-pandemic, customers browsed differently online. Recommendations became less relevant, conversion dropped 15%. They now retrain quarterly and monitor recommendation click-through rates as an early warning.
Hype vs Reality
Hype: "Deploy AI once and it works forever." Reality: AI models aren't "set and forget." Performance degrades as the world changes. For most SMEs using ChatGPT or Claude, the providers handle updates, but if you build custom AI solutions, budget for monitoring and maintenance.
Opportunities
Early detection prevents major failures. Continuous improvement mindset. Competitive advantage through maintained accuracy. Better forecasting through updated models.
Considerations
Custom AI needs ongoing maintenance budget. Monitor key metrics that indicate performance. Plan for retraining cycles. Using major providers reduces this burden.
Computer Vision
Think of it like: Teaching computers to "see" and understand images the way humans do, recognising objects, reading text, and understanding scenes.
AI that processes and understands visual information from images and videos. Can identify objects, read text (OCR), detect defects, recognise faces, count items, and analyse scenes. Now built into multimodal LLMs like GPT-4 and Claude, making it accessible without specialist expertise.
Real Business Example
A building materials wholesaler photographs damaged delivery items for insurance claims. Previously, staff wrote descriptions manually. Now they upload photos to Claude: "Describe this damage for an insurance claim, include dimensions, type of damage, and affected materials." Processing time dropped from 20 minutes to 2.
Hype vs Reality
Hype: "Computer vision is only for tech companies with huge budgets." Reality: Basic computer vision is now accessible through ChatGPT and Claude for £16-20/month. Upload a photo, ask questions. For specialised applications (quality control, medical imaging), custom solutions still require investment, but the entry point has dropped dramatically.
Opportunities
Document processing (receipts, invoices, forms). Quality inspection. Inventory counting. Damage assessment. Accessibility (image descriptions). Visual search.
Considerations
Image quality affects accuracy. Privacy concerns with photos of people. Handwriting recognition still imperfect. Verify important extractions.
Automation vs AI
Think of it like: Automation follows a recipe exactly every time. AI can improvise when the ingredients change.
Traditional automation follows fixed rules: "If X happens, do Y." It's predictable and reliable but can't handle exceptions. AI can understand context, handle variations, and make judgement calls. The most powerful solutions combine both: automation for predictable steps, AI for decisions requiring understanding.
Real Business Example
A property management company handles maintenance requests. Automation: New request arrives → create ticket → assign to category → notify tenant of receipt. AI: Read the request, determine urgency (leaking pipe = emergency, squeaky door = routine), identify the right tradesperson, and draft a personalised response to the tenant.
Hype vs Reality
Hype: "AI replaces automation." Reality: They're complementary. Simple, predictable tasks? Automation is cheaper and more reliable. Tasks needing understanding, judgement, or natural language? Add AI. Many of the best solutions use automation for the workflow, AI for the decisions within it.
Opportunities
Use automation for: file transfers, scheduled reports, data syncing, notifications. Add AI for: classification, summarisation, drafting, prioritisation, natural language understanding.
Considerations
Don't over-engineer with AI when simple automation works. AI adds cost and unpredictability. Build in error handling for both. Test edge cases thoroughly.
More terms you might encounter
Embeddings
Like a fingerprint for meaning. Converting text into numbers so AI can compare similarity. "Happy customer" and "satisfied client" have similar embeddings. Powers smart search and recommendations.
Vector Database
Like a library organised by meaning, not alphabet. Stores embeddings for instant similarity search. Ask "complaints about delivery" and it finds all related documents, even if they don't use those exact words.
Inference
Like making a phone call. Every time you ask AI a question, that's an "inference", a single request/response. API pricing is often per-inference.
System Prompt
Like setting ground rules before a conversation. Hidden instructions that tell AI how to behave. "You are a helpful customer service agent for Acme Corp. Always be polite. Never discuss competitors."
Agentic AI
Like promoting a chatbot to an assistant. AI that can decide what to do next, not just respond. Can plan steps, use tools, and complete multi-part tasks with minimal guidance.
Grounding
Like fact-checking in real time. Connecting AI responses to verifiable sources. Perplexity does this by searching the web and citing sources. Reduces hallucination.
Guardrails
Like safety barriers on a road. Rules that keep AI within bounds, preventing harmful outputs, staying on topic, respecting privacy. Essential for business deployment.
Zero-shot / Few-shot
Like learning from examples. Zero-shot: AI completes a task with no examples. Few-shot: you provide 2-3 examples of what you want. Few-shot often dramatically improves results.
Machine Learning (ML)
Like pattern recognition on steroids. AI subset where systems learn from data without being explicitly programmed. Powers recommendation engines, fraud detection, and predictive analytics.
Deep Learning
Like ML with many layers of learning. Uses neural networks with multiple layers to model complex patterns. Powers image recognition, speech-to-text, and modern LLMs.
Neural Network
Like a simplified model of the brain. Computing system with interconnected "neurons" that process information in layers. Foundation of all modern AI. You don't need to understand how it works to use AI effectively.
Natural Language Processing (NLP)
Like teaching computers to understand English. AI's ability to understand, interpret, and generate human language. Powers chatbots, translation, summarisation, and voice assistants.
Human in the Loop (HITL)
Like having a manager approve AI decisions. Keeping humans involved in AI workflows to verify outputs, catch errors, and maintain accountability. Essential for high-stakes decisions.
Explainable AI (XAI)
Like showing your working in maths. AI systems that can explain how they reached a decision. Important for compliance, auditing, and building trust. Opposite of "black box" AI.
Black Box AI
Like a magic trick you can't explain. AI systems where the decision-making process isn't transparent. You see the input and output but not why. Problematic for regulated industries.
Narrow AI vs AGI
Like a specialist vs a polymath. Narrow AI: excellent at one task (all current AI). AGI: theoretical human-level general intelligence. Don't wait for AGI, narrow AI solves real problems today.
Responsible AI
Like corporate social responsibility for AI. Designing, developing, and deploying AI ethically, considering fairness, privacy, transparency, and societal impact. Increasingly important for compliance.
Workflow Automation
Like connecting apps to work together automatically. Using tools like Zapier or Make to link software without coding. Add AI to transform simple automations into intelligent workflows.
Your next steps
Use these tools to get your organisation moving.
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In 20 to 30 minutes we will:
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- ✓ Decide whether a workshop or programme is a good fit.