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Businesses and Employees Must Master Marketing the Difficult "Last Mile" to Survive the AI Transition

ended 11. November 2025

Q3 2025 unemployment has climbed to 5.0%,  its highest level since May 2021, sparking fresh fears about artificial intelligence displacing workers. But while headlines focus on job losses, a different reality is emerging: AI systems are spectacularly good at 95% of complex tasks, and dangerously inadequate at the critical remaining 5%. That is where the new opportunities lie.

The UK has attracted £39 billion in AI investment over the past six months, with a 58% increase in AI companies creating over 6,500 new jobs. Yet 1.33 million people now juggle second jobs, many scrambling to keep the lights on as traditional roles fragment.

AI could boost the economy by £400 billion by 2030, but every organisation faces the same challenge: making sophisticated technology work within messy human systems.

The gap is the “last mile” of tasks,, where generic AI crashes into an industry's reality. UK companies finding success focus on niche, industry-specific applications because off-the-shelf AI misses unwritten rules, edge cases,, for example how site conditions affect construction safety.  AI can't train for that as well as a human. Knowledgeable oversight correfting AI approximations is the new gold..

Healthcare data is fragmented. Construction demands coordination across drawings, legal docs, resources, and safety. The “dead interne” is drowning in AI-generated slop and fake content, poisoning future training data. Human expertise is needed to cleanse, contextualise, and curate AI  “brains”. becomes more valuable, not less.

Growing roles in AI ethics, algorithm testing, and training material curation offer workers a bridge. The biggest risk isn't failing to learn AI,  it's discarding decades of industry knowledge when lived experience of edge cases is what AI cannot replicate.

We'd like your views:

  • How are you AI-proofing your business through “last-mile” expertise?
  • Are second jobs survival or strategy in your sector or for.your cl8ents?
  • Should small businesses prioritise mastering AI tools or selling domain expertise to the system builders?
  • What does AI consistently get wrong in your industry where humans will.always excel?

Sources:
ONS: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/bulletins/employmentintheuk/october2025
Gov.uk: https://www.gov.uk/government/news/uk-ai-sector-attracts-200-million-a-day-in-private-investment-since-july
AI Sector Study: https://www.gov.uk/government/publications/artificial-intelligence-sector-study-2024

 

4 responses from the Newspage community

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Colette Mason
AI Ethics Consultant at Clever Clogs AI
The defence isn't learning more AI tools. It's adding more human to everything you do.

Your clients are frightened. They're drowning in generic AI outputs that miss the mark. Pick up the phone instead of emailing.

Explain the 'why' behind your recommendations. Show you've caught the three things the AI got wrong for their specific situation.

When AI does their first draft, you become the person who makes it actually work for them.
That's your shield — caring about the details that matter.

Don't let external consultants step in to train your systems. You know your customers and the edge cases. Offer to prepare the training materials yourself. It's cheaper, reflects reality, and keeps your client relationships intact.

The gap isn't technical skills. It's human attention when everyone else automated empathy away.
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Kate Underwood
Founder & Chief People Strategist at Kate Underwood HR and Training
Do not use AI to be lazy. Use it to be efficient, then spend the saved hours on training. Pick one task per team this month for AI to draft. A human always finishes and signs off. HR: notes, job ads. Use the time for coaching and interview practice. Sales: first pass proposals and follow ups. Train objection handling. Customer service: suggested replies and FAQ updates. Train de escalation. Operations: stock lists and rota ideas from exports. Train process improvement. Finance: invoice chasers and expense summaries. Train cash flow basics. Marketing: blog outlines and captions. Train brand voice. Guardrails: no AI only decisions on pay, health, visas, contracts, or moving money. Measure minutes saved weekly and ring fence them for learning. Small wins, human finish, better skills.
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Tom Fletcher
Head of Marketing at Gravitate Accounting
Integration of AI across industries is happening at a staggering rate. But its wholesale use hasn't been stress-tested yet in terms of customer credibility and trust.
This is particularly critical in B2B sectors such as accounting and finance, where accuracy and accountability are priority, but human connections and trust are also hugely important.
While errors like incorrect financial classifications highlight the risks of overreliance on AI, an even bigger long-term concern is the erosion of trust between advisors and clients.
When professional judgement is outsourced to machines, it can undermine the credibility of the human expert, even when the AI output is technically accurate. People want to be advised by people; not machines pretending to be people.
It is tempting to go all-in on AI because of efficiencies, but there’s a reason the companies where people can still talk to people, not robots, are considered the gold standard for service.
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Marcus Wright
Managing Director at Bolton Business Finance
As a forward thinking commercial finance broker, we use technology and AI to speed up applications, while maintaining a personal touch. Many of our clients have complex finance requirements which require a human touch but automating data gathering, email sequences, CRM data enrichment etc is a win win for us and our clients.