Customers demand always-on business. How does yours fare?

Gone are the days when customers accepted that ‘phone lines are now closed', or a response to their query will take ‘2-3 working days’. The customers of today demand always-on service. It’s an expectation that failing to meet drives customers to more tech-savvy competitors.

According to Microsoft, 96% of consumers say customer service is an important factor in their choice of loyalty to a brand. Yet according to Salesforce, only 34% of companies are implementing “customer journey mapping” into their customer service.

Becoming an always-on business doesn’t require the expense and complexities of hiring people around the clock to answer the phone. Instead, it’s automation, AI and self service solutions that fill the customer service gaps. Implementing these not only gives customers the always-on service they expect, but also transforms businesses into super-scalable, more profitable growth machines.

Business owners of today should be asking themselves if their business is really ‘always-on’. Can their customers get answers to their queries instantly, or serve themselves out-of-hours? Could a chatbot or self-service portal reduce the burden on their teams?

If you would like to understand how your business can make the transition to always-on, feel free to get in touch for a chat.

Using AI as a Research Assistant

AI is especially useful for spotting trends and patterns in large datasets. Such as survey responses, user feedback, or customer support tickets. It can help to group information into common themes. This makes it easier to extract useful insights from large amounts of data.

Example tools: ChatGPT, Gemini, or DeepSeek

Method: Begin with a spreadsheet where each row represents a single data point. This could be a user comment, survey response, or support ticket. Using your chosen LLM, you can prompt it to:

• Identify recurring themes across the entries.
• Categorise feedback into relevant groups (e.g. usability, features, bugs).
• Highlight common issues, concerns, or positive points.

Limitations: AI can mislabel data. You’ll need to check its output and refine prompts as needed. While the AI can speed up analysis, it doesn’t remove the need for human judgment.

User Interview Practice

An underrated use of Large Language Models or LLMs is to simulate customer interviews. They can help you ask better questions and get up to speed on industry-specific knowledge.

Example tools: ChatGPT, Gemini, or DeepSeek

How it works:

• Set the context: Give AI a persona (e.g. "You are a 32-year-old tech-savvy project manager who uses productivity apps daily".) and product background.
• Run a mock interview: Ask open-ended questions as if you were interviewing a real user. The AI will answer in character.
• Analyse the conversation: Check if your questions are strong enough. Do they reveal blind spots, or surface needs and objections you hadn’t thought of?

Limitations: AI is not a replacement for real customer research. Real customers will surprise you in ways AI can’t fully simulate. It can, however, help you arrive prepared and empowered so you get the most out of (often time-constrained) customer interviews.

Refining Designs with Predictive Heat Maps

Heat maps are visual representations of user attention, showing where people are most likely to focus on a page. In traditional usability testing, they are generated by tracking real users’ eye movements. Predictive heat maps use AI to show where users focus based on design patterns. This helps you assess your layout before testing it with real users.

There are various tools available to analyse landing or marketing pages before spending on user testing. Many of them report over 90% accuracy compared to real (non predictive) heat maps.

Example Tools:

• Attention Insight: Predicts user attention based on design structure. Offering clarity scores and benchmark comparisons.
• Heatmap.com: Simple predictive heat maps—great for quick checks.
• VisualEyes: Combines heat map prediction with emotional scoring (e.g., clarity, attention, engagement).

Best for:

• Ensuring the visual hierarchy supports your page’s primary goal.
• Stress-testing landing pages. In scenarios where small tweaks can really impact conversions.

Not good for:

• Data-heavy applications or complex UIs highly dependent on user context.
• Replacing usability testing. It’s better viewed as a high-level QA tool rather than a user validation method.

Limitations: These tools use visual pattern recognition to predict where users are likely to focus. They don’t account for authentic user intent or content clarity. Think of them as a “visual hygiene” check—ideal for catching flaws before moving to real testing.

Image Generation: An Alternative to Stock

Finding the right stock photo can take longer than expected. They can also be pricey, generic, or not quite fit your brand. AI-generated imagery gives you options. There are many AI image generation tools available, two of which have made it into my workflow.

• Midjourney
  • Best for stylised, artistic imagery. Can be used on mood boards, concept exploration, and general visuals.

• Adobe Firefly
  • Trained on Adobe Stock so the output can be used for commercial use. (This is often overlooked, but not all AI-generated images can be used commercially without copyright risks.)
  • Offers powerful editing features within Photoshop. These include, Generative Fill, Distraction Removal, and Generative Expand.
  • Firefly includes lots of helpful inputs to allow you to create solid prompts. Even with limited prompt writing experience.
• Google Imagen
  
  • Unlike the others mentioned Google Imagen is free to use. It offers many of the same features as other tools but with really easy to use UX.

Limitations: AI-generated photographs don’t always look right. Hands and faces often look strange, and you can't reliably recreate the same person. In high stake marketing campaigns or UX, AI photos can feel “off.” Real photos or edited stock images are often better for creating trust and relatability.

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AI Wireframing and Prototyping tools

There are too many new AI wireframing and prototyping tools to list them all. The practical impact (so far) is extremely mixed. The tools worth noting, integrate with Figma or are inbuilt and can provide responsive design ideas.

Magic Patterns (Application and Chrome extension)

• Can directly export components with a Figma plugin
• Can produce react code
• Capable of responsive mockups

Figma AI Beta

• Accelerated Design with AI Prompts: Figma can now generate UI designs, text content, and even images from simple prompts — ideal for fast prototyping and ideation.
• Smarter Workflows: Features like one-click prototyping, asset search via image input, and auto-renaming layers streamline collaboration and reduce manual effort.

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Final Thoughts

If used with intention, purpose and sufficient preparation AI tools can help you in a number of ways. They can help you to prepare for interviews. Test designs early, find patterns in feedback, and create cool visuals. Ultimately, having this help at hand can speed up your creative process.

In the same way the industrial revolution changed manufacturing, AI agents are poised to revolutionise how office work gets done. Businesses that aren’t investing in this technology today risk being left behind in just a few short years.

AI agents are more than chatbots. They’re autonomous, context-aware digital colleagues that can take initiative, understand your business, and interact with your systems - often in natural language. Whether augmenting human workflows or handling entire processes end-to-end, they offer real, scalable productivity.

So how do you prepare your business to benefit from AI agents?

Here’s a practical roadmap to help you stay ahead:

1. Set Up Their Environment

Before an AI agent can get to work, it needs a digital space to live and operate - much like setting up a new workstation for a team member.

• Deploy them to the cloud so they can run continuously, without manual restarts or human babysitting. Containerised environments or serverless platforms like AWS Lambda or GCP Cloud Run are ideal for this.
• Connect them to key communication channels like email, calendars, and messaging apps such as WhatsApp. This allows them to interact with your team, customers, or even vendors in a natural and timely way.
  

This environment forms the foundation of a reliable and responsive AI assistant.

2. Train Them

Like any new employee, AI agents need training. They won’t magically understand your industry jargon, your policies, or how your workflows operate - unless you show them.

• Share your business processes, terminology, documentation, and compliance standards.
• Use structured inputs (like policy manuals or SOPs), and unstructured ones (like meeting transcripts or knowledge bases).
• Define clear expectations: what they should do, when to escalate, and how to respond in edge cases.
  

This ensures your AI agents act as true extensions of your team - not rogue operators or generic models.

3. Give Them Access to Tools

Once your AI agent knows what to do, it needs the means to actually do it.

• Integrate your internal tools - such as project management software, customer relationship management (CRM) systems, ticketing platforms, and HR databases.
• Assign appropriate permissions, starting with basic updates, comments, or suggestions - and expanding to more autonomous actions as confidence grows.
  

Think of them like a junior staff member: start small, monitor performance, and expand scope based on trust and results.

4. Provide Access to Data

To make smart decisions or even basic recommendations, AI agents need access to the same data your team uses every day.

• Grant read-only access to relevant databases, dashboards, or APIs so they can query information, generate reports, or answer internal questions.
• With natural language querying layered on top, business intelligence becomes far more accessible - enabling staff to ask, “How did we perform last quarter?” and get a real answer in seconds.

This isn’t just automation - it’s intelligent assistance.

Final Thoughts

AI agents aren’t the future - they’re already here. The difference between those who thrive and those who get left behind will come down to one thing: who integrates them first, and who integrates them well.

Whether you’re a startup founder or leading innovation inside an enterprise, now’s the time to start building your AI agent strategy. Deploy them, train them, give them tools, and feed them data. If you do, you won’t just keep up - you’ll lead.