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

September 5, 2021

5 min read

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.

Real Ways AI Can Support Your Design Workflow

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.

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.

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.

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.