For centuries, each wave of technological progress nudged humans away from manual toil and toward “higher” work. Machines freed us from repetitive tasks so we could focus on judgement, creativity, strategy. But now, with the rise of AI, that boundary is blurring. The very cognitive layers we thought were off-limits for machines are coming under pressure. The question is: if machines can think, where do humans go next?

The long arc: automation moving upward

To see where we might head, it helps to trace how automation has already shifted human labour:

• In the industrial era, machines replaced muscle. Fewer people were needed in fields or factories; more in management, design, coordination.
• In the information age, software and robotics began eroding repetitive office tasks. Think data entry, bookkeeping, standardised reports.
• Now, AI (especially “cognitive” and “generative” AI) is targeting tasks that require analysis, synthesis, even (some degree of) “judgment.”

This isn’t just theoretical. The World Economic Forum’s Future of Jobs Report 2025 highlights that AI and information processing technologies are among the most disruptive forces to come. It estimates that many occupations will see substantial change – not necessarily total elimination, but a shifting composition of what tasks humans do and what tasks machines take on.

In the UK specifically, the Institute for Government / Institute for Future Skills (via a report, The Impact of AI on the UK Jobs and Training) suggests that somewhere between 10-30 % of jobs are automatable in principle. Meanwhile, KPMG’s “Generative AI and the UK labour market” report gives more nuance: around 2.5 % of tasks could be handled by generative AI per se, with about 40 % of jobs seeing some impact.

So what used to be “safe” — tasks that required thought — are gradually being redefined.

The current threat: mind work under siege

What’s different now is that AI isn’t just mimicking rote logic. It’s getting good at pattern recognition, language, abstraction. That means some traditionally human domains are no longer immune:

• Routine cognitive work like summarising documents, drafting standard contracts, even generating reports is increasingly in scope.
• Entry-level knowledge roles are particularly exposed. A recent Harvard Business Review article titled The Perils of Using AI to Replace Entry-Level Jobs argues that early-career workers may find fewer footholds in fields where machines can already replicate many tasks.
• In the UK, an IPPR study suggests that in a “first wave” of AI adoption, 11 % of tasks across the economy are already exposed. If adoption deepens, that number could climb to 59 % in a second wave. This reaches beyond clerical jobs to what were once seen as higher cognitive roles.
• The Institute Global’s “Impact of AI on the Labour Market” paper estimates that UK firms adopting AI fully could “save” nearly a quarter of private sector workforce time — the equivalent output of some 6 million workers.

These aren’t minor tweaks. They’re signals that the walls protecting what’s “human work” are eroding.

But humans still bring what machines can’t (yet)

If thought becomes automatable, can we still stake out territory? I believe yes – though how wide that territory is will depend on how we redefine “human contribution.” Some qualities are harder (though not impossible) for machines to replicate:

• Empathy, ethics, human judgment in messy contexts: Legal disputes, leadership, conflict resolution, counselling – these often require nuance, moral weighting, context, even vulnerability.
• Creativity untethered from data: AI can remix, recombine, but truly novel leaps, radical reframing, or deeply personal narratives are harder for machines to originate.
• Social intelligence, influence, trust: The ability to lead a team, understand unspoken dynamics, persuade, mentor – these involve deep human connection.
• Meta thinking and reflection: Thinking about thinking, questioning frameworks, reframing entire models – these might remain more human territory (though we can’t be naive).
• Oversight, arbitration, boundary-setting: AI systems will need supervision, governance, ethical constraints, error checking. That calls for human oversight roles.

In fact, a recent academic paper Complement or Substitute? How AI increases the demand for human skills found that while AI does substitute some tasks, the demand for complementary human skills (like digital literacy, teamwork, resilience) is rising – sometimes by more than the substitution effect.

So the future might not be “humans or machines” but “humans in different modes of work.”

The risk: displacement, division, identity crisis

This transition is going to be messy. Some of the dangers we need to acknowledge:

• Widening inequality: Those who can adapt (learning, flexibility, cross-disciplinary skills) may thrive; others may be left behind.
• Loss of early entry points: If entry roles vanish, career progression funnels could narrow.
• Skill mismatch: As tasks shift rapidly, training and reskilling may lag demand.
• Psychological dislocation: If people feel their labour is undervalued or replaced, there’s risk of alienation, identity crisis.
• Overtrust in algorithmic fairness: If hiring, performance review, promotion decisions are handed to opaque AI systems, bias or flaws may propagate invisibly.

History suggests that disruptive transitions tend to widen gaps before new stability emerges. We need to guard against that.

A few scenarios (and what we should watch for)

Let me sketch out possible futures and what we should keep an eye on:

If I were advising clients or candidates, here’s what I’d urge them to watch for:

• The pace of AI adoption (will it happen steadily or in leaps?)
• Which tasks (not jobs) are most vulnerable in their domain
• Signals from leadership and investment – where are firms putting R&D, recruitment, training money
• Policy and regulation – these could slow or channel the transition
• Labour market shifts – what new roles are emerging, in oversight, governance, hybrid human-machine work

What to do now (for candidates, clients, recruiters)

Because this isn’t a distant problem – it’s unfolding now – there’s practical agency in how we respond.

• For candidates: Don’t just deepen legal or domain expertise; layer in transversal skills – ethics, system thinking, digital fluency, narrative, leadership. Seek roles that combine human and machine work, not ones purely replaced by machine.
• For clients/firms: When hiring, think beyond “machine-resistant” roles. Design roles that optimise human+AI collaboration. Invest in reskilling, redesigning workflows.
• For recruiters and agencies: Update your frameworks for evaluation. What used to be a “plus” (tech curiosity, adaptability) may become a baseline. Help clients see the value in human skills that AI cannot easily replicate.

Also, encourage a mindset shift: value transformation over displacement. Rather than asking “which roles vanish?”, ask “how can humans lead the transition to new forms of value?”

Final thoughts: humans are not redundant (yet), but the frontier is shifting

This moment feels different. It’s not just about machines doing heavy or routine tasks. It’s about machines encroaching on thought, analysis, even creativity. That changes the rules of engagement.

Yet I’m not convinced humans are being written out. What’s more likely is that we’ll be asked to re-imagine what “work” means – less about performing tasks and more about curating, governing, imagining, caring.

It’s messy, scary, open-ended. But also potentially liberating. If we lean into the ambiguity, we might get to design roles that feel more human, not less.

Generative AI is no longer just a buzzword whispered in tech circles – it’s quietly reshaping the way public sector organisations in the UK approach recruitment. From the NHS to local councils, tools once considered experimental are now being used to draft job descriptions, sift applications, and even shape interview questions.

But the shift is raising as many questions as it answers.

The Promise: Efficiency in a Resource-Strapped System

The public sector is under relentless pressure – budget constraints, skills shortages, and political scrutiny all collide when it comes to hiring. Generative AI seems like an elegant solution:

• Faster admin: Automating job advert writing or shortlisting can save recruiters hours of manual effort.
• Standardisation: AI can enforce consistent criteria across dozens of roles, reducing the risk of bias creeping in.
• Scalability: High-volume recruitment drives, like those in the NHS, become more manageable with AI-generated assessments and communications.

For a sector where every hour and pound is scrutinised, these advantages are hard to ignore.

The Pitfalls: Risk of Impersonal or Inaccurate Hiring

Yet, there are cracks in the foundation. A recent study found UK public bodies experimenting with generative AI often do so without clear structure or guidance. That creates serious risks:

• Impersonal candidate experience: Applicants may feel reduced to keywords and algorithms rather than people.
• Opaque decision-making: If an AI tool flags someone as unsuitable, can a recruiter explain why?
• Reinforcing bias: Without careful training, AI simply reflects the systemic biases already baked into historic hiring data.

For public institutions accountable to taxpayers, this opacity is not just a technical flaw – it’s a governance problem.

Politics Meets Technology

The Labour government has pledged stronger workers’ rights and fairer hiring practices. At the same time, departments are quietly leaning on AI to cut costs and speed processes. The collision of these forces will define the next few years of public sector recruitment: efficiency vs fairness, speed vs accountability.

How regulators frame “responsible use” of AI in hiring could be one of the most important employment policy debates of this decade.

What Needs to Happen Next

1. Clear frameworks: Every public body using AI in recruitment should publish transparent guidelines for how it’s applied.
2. Human oversight: AI should never make the final decision. Recruiters must remain accountable.
3. Training recruiters, not just training models: Without upskilled HR teams, tech becomes a crutch rather than a tool.
4. Candidate-first design: Remember that every applicant is a taxpayer – their experience matters.

The Bottom Line

Generative AI has the potential to transform public sector hiring in the UK, but without transparency and accountability, it risks doing more harm than good. The NHS may be the frontline, but the ripple effects will be felt across councils, education, and beyond.

The real test won’t be whether AI can save time – it’ll be whether it can help the public sector hire better, fairer, and with integrity.

In the world of talent acquisition and leadership, the importance of soft skills is well understood. Emotional intelligence, adaptability, and communication have become buzzwords that fill every hiring meeting and leadership offsite. But for those seasoned in recruitment and talent strategy, the question isn’t if these skills matter – it’s how to rigorously identify, validate, and quantify them in ways that drive tangible business impact.

This post aims to move beyond the basics and offer a strategic playbook for advanced professionals looking to elevate soft skills hiring from checkbox to competitive advantage.

The Evolving Science of Soft Skills

Recent advances in organisational psychology and behavioural science have deepened our understanding of soft skills. Emotional intelligence, for instance, is no longer just a “nice-to-have.” Meta-analyses (like this one from the Journal of Organizational Behavior) demonstrate that EQ correlates strongly not only with leadership effectiveness but also with employee engagement and turnover reduction.

Moreover, adaptability – often conflated with mere flexibility – is now being redefined as a dynamic capability involving cognitive agility, learning orientation, and resilience under uncertainty (Harvard Business Review).

Understanding these deeper, multidimensional constructs is critical for designing better assessment frameworks.

Advanced Techniques for Spotting Soft Skills

1. Data-Driven Behavioural Profiling

Move beyond traditional behavioural interviews and leverage psychometric tools that measure traits like emotional regulation and cognitive flexibility. Platforms like Talentoday and Pymetrics use neuroscience-backed assessments to quantify soft skills and predict cultural fit.

2. Situational Judgment Tests (SJTs) with Realistic Scenarios

Well-designed SJTs can simulate complex, ambiguous situations candidates face on the job, revealing nuanced soft skills such as ethical judgment or collaborative problem-solving. Unlike generic role-plays, SJTs tied to role-specific challenges provide higher predictive validity (SHRM Research).

3. Narrative Interviewing Techniques

Encourage candidates to articulate their personal “soft skill journey” by asking for stories that explore failure, conflict resolution, and learning moments. Experienced interviewers use active listening and follow-ups to uncover subtle emotional and cognitive patterns.

Selling Soft Skills to Senior Stakeholders

Despite growing awareness, soft skills often fall victim to “intangible” stereotypes when budgets and headcounts are on the line. Here’s how to elevate the conversation:

• Translate Soft Skills Into KPIs: Link emotional intelligence or adaptability to quantifiable business outcomes like customer satisfaction scores, project delivery timelines, or employee Net Promoter Scores.
• Build a Business Case With Internal Data: Analyse your own talent metrics to demonstrate how teams with strong soft skills outperform others in retention, innovation, or revenue growth. Use tools like Gallup’s Q12 to tie engagement data to skill profiles.
• Integrate Into Leadership Development: Position soft skills as core to succession planning and leadership pipelines, not just entry-level hiring. Show how these competencies drive strategic agility in volatile markets.

Thought-Provoking Questions for the Next Frontier

• How can AI augment our ability to assess soft skills without reinforcing bias?
  Emerging tools claim to decode facial expressions or speech patterns, but ethical and accuracy concerns remain. What frameworks ensure fairness and transparency?
• What role does psychological safety play in unlocking soft skills on the job?
  Even candidates with high EQ may under-perform in environments lacking trust. How can recruitment strategies extend beyond hiring into culture shaping?
• Can we move from reactive hiring to proactive talent sculpting?
  Instead of finding “ready-made” soft skills, how do organisations design learning journeys that cultivate these abilities internally at scale?

Final Thoughts

For experienced professionals, soft skills are not a checkbox – they are complex, evolving capabilities demanding rigor, nuance, and strategic intent. By deepening our scientific understanding, embracing advanced assessment techniques, and embedding soft skills into organisational DNA, we can turn this “human factor” into a decisive competitive edge.

The challenge isn’t just spotting these skills – it’s making them central to how we hire, lead, and grow talent in an unpredictable world.

Diversity hiring has been a headline topic for years now. But with all the noise – initiatives, quotas, training sessions – it’s easy for companies to get stuck in buzzword territory without making real progress. The truth is, building genuinely inclusive talent pipelines requires more than ticking boxes or catchy slogans. It demands a strategic, practical approach rooted in measurable outcomes and ongoing learning.

Why Diversity Hiring Still Matters

There’s strong evidence linking diverse teams to better business results, including increased innovation, improved decision-making, and stronger financial performance. A McKinsey study found that companies in the top quartile for ethnic and cultural diversity outperform those in the bottom quartile by 36% in profitability.

But despite growing awareness, many organisations struggle to translate this into effective hiring. According to Deloitte, one major hurdle is unconscious bias embedded in recruitment processes, which continues to block underrepresented groups from advancing.

Practical Steps to Build Inclusive Pipelines

1. Revisit Job Descriptions and Requirements
Job descriptions often contain language that unintentionally discourages diverse candidates. Terms like “aggressive” or “ninja” can alienate some applicants. Tools like Textio can help make language more inclusive and appealing across demographics.

2. Broaden Sourcing Channels
Relying solely on traditional job boards limits your reach. Partnering with community organisations, diverse professional groups, and even non-profits focused on underrepresented talent can expand your candidate pool. For example, Women Who Code and Black Tech Pipeline offer access to untapped talent networks.

3. Implement Structured Interviewing
Unstructured interviews can be rife with bias. Using standardised questions and scoring rubrics reduces subjectivity and levels the playing field. Harvard Business Review’s research on structured interviews highlights their impact on reducing bias and improving hiring quality.

4. Train Hiring Teams on Unconscious Bias
Awareness alone isn’t enough; continuous training and accountability measures are necessary. Creating a culture where bias is openly discussed and addressed helps sustain inclusive practices over time.

5. Measure and Report Progress Transparently
Collect diversity data at every hiring stage and share insights with leadership and teams. Transparency builds trust and helps identify where pipelines are leaking diverse talent.

Moving Beyond Current Practices: What’s Next?

While many companies have started on the journey, the landscape of diversity hiring still needs evolution. Here are some ideas that could push the conversation forward:

• Focus on Inclusion as Much as Hiring
  Diversity in hiring is only half the battle. Retention, belonging, and career progression are equally vital. How can companies build inclusive cultures that truly welcome and support diverse talent?
• Invest in Community Partnerships for Long-Term Impact
  Rather than one-off recruitment drives, sustainable investment in education, mentorship, and skill development in underrepresented communities could create deeper pipelines.
• Leverage Technology Thoughtfully
  AI and analytics can reduce bias but also risk reinforcing it if not carefully designed. Transparency about algorithmic decision-making and ethical tech use is critical.
• Rethink Success Metrics
  Moving beyond headcount numbers to measure impact on innovation, employee satisfaction, and market growth can give a fuller picture of diversity’s value.

Final Thoughts

Diversity hiring will never be a “set and forget” strategy. It requires ongoing attention, creativity, and humility to recognise where efforts fall short. As recruiters and leaders, the challenge is to push past superficial efforts and foster truly inclusive ecosystems where diverse talent thrives – not just survives.

If you want to be part of shaping this next wave, consider how your processes can evolve beyond buzzwords to genuine transformation. It’s not easy, but it’s necessary.

If you’re serious about landing a job quickly – like, within a month – you need more than just luck. You need a plan. A solid, tactical calendar that breaks down every task into manageable, focused steps.

Sounds intense? Yeah, but it can work. Here’s a day-by-day blueprint to help you get focused by cutting through the noise, stay motivated, and finish the job search with an offer in hand.

Why a 30-Day Plan?

The typical job search can drag on for months. That’s frustrating and exhausting. By compressing your efforts into a dedicated 30-day period, you create momentum and focus recruiters notice. Plus, you avoid burnout and distraction.

Think of it like sprint training instead of a marathon. The goal is to be smart and strategic, not frantic. But it’s also important to highly, you don’t necessarily have to wait. If you want to do something which falls into a later timeframe now, and it’s right to do so, go ahead. Get ahead of the plan. It’s there to help guide and break down the steps, not handcuff you.

Week 1: Foundation and Preparation

Day 1–3:

• Update your CV and LinkedIn. Make sure they tell a clear, consistent story that matches the roles you want (not that last word, it’s important).
• Get feedback from someone in your network or a recruiter – those second pair of eyes could be valuable, and even a quick tweak can make a big difference.

Day 4–5:

• Identify your target roles and companies. Research their culture, pain points, and hiring timelines. Have a clear direction, ambition and interest. It will help give you clarity on what you’re really wanting in a next role, and not blindly applying for anything just because it matches a keyword.
• Set up job alerts on key platforms (LinkedIn, Indeed, niche boards). Do this both for job titles, and company alerts. If you’ve done the first point correctly, you should also have a dictionary of the types of job titles those companies will be using too. Not everyone uses the same.

Day 6–7:

• Prepare tailored cover letter templates for different role types. AI can be your friend here to create something quick, well written with elements of personalisation. But be sure to check it over.
• Start reaching out to contacts in your network with a polite, purposeful message. Generic won’t cut mustard. Show you’re interested in their job or a job specifically with them, not just ‘a job’.

Week 2: Active Application and Outreach

Day 8–10:

• Apply to 3-5 targeted roles each day, carefully tailoring each application to the job description.
• Use keywords that match the job ads, which ATS systems will likely be coded to score against, but keep it natural.

Day 11–14:

• Reach out to recruiters who specialise in your field (many see immediate value in starting at this point first. It depends on how well networked you are, and how busy the market is for the types of roles you’re looking for).
• Follow up on previous applications with brief, polite emails.

Week 3: Interview Preparation and Networking

Day 15–17:

• Practice common interview questions and scenarios. Consider a mock interview with a friend or coach. If you’re unsure what types of questions might arise, run the job spec through an AI tool and ask if to come up with competency question from it.
• Research behavioural interview techniques, focusing on the STAR method (Situation, Task, Action, Result).
• If you’re working with an agency recruiter, ask them for hints and tips about upcoming interviews. See what they might know and can help steer you. Sometimes their insights can be invaluable.

Day 18–21:

• Attend industry events, webinars, or online meetups to meet people and get insights.
• Continue networking via LinkedIn: comment on posts, share relevant articles, and engage with recruiters.

Week 4: Closing and Follow-Up

Day 22–25:

• Prepare thoughtful questions to ask employers during interviews. This shows genuine interest. (tip – thoughtful questions are ones which show you’re keen on the role, business, environment, where it’s going and your potential future with them. Not surface reward, or questions which don’t require any pre-thought).
• Send thank-you emails promptly after each interview.

Day 26–28:

• Evaluate any offers with your priorities in mind – salary, growth, culture, flexibility.
• Don’t be afraid to negotiate or ask for clarification.

Day 29–30:

• Ideally, decision time…
• Follow up on any outstanding applications or interviews. Get the last over the line so you can evaluate all options before closing
• Reflect on what worked and where you can improve for future searches. Which hopefully won’t be for years to come in the future.

Bonus Tips to Speed Up the Process

• Stay organised: Use a spreadsheet or app to track applications, contacts, interview dates, and follow-ups.
• Take care of yourself: High-intensity searches can be draining. Remember to rest and reset.
• Be flexible: Sometimes offers come from unexpected places. Stay open.

Final Thoughts

A job search doesn’t have to be a months-long slog. With a clear 30-day plan, you build momentum, maintain focus, and increase your chances of landing the right role fast.

Remember, it’s not just about applying – it’s about targeting, networking, and showing up consistently. And in 30 days, you can make a real difference to your career.

For years, the conversation in tech circles was about moving everything to the public cloud. Faster, cheaper, more scalable – it sounded like the obvious path. But in 2025, the reality for many CTOs is more nuanced.

Rather than going all in on public cloud, organisations are increasingly adopting hybrid infrastructure models – blending on-premises, private cloud, and public cloud resources. And in parallel, they’re turning a sharper eye toward optimising SaaS investments like Microsoft 365 with AI-driven governance.

It’s not about going backwards. It’s about going smarter.

Why Hybrid Is Back in Favour

The hybrid Infrastructure-as-a-Service (IaaS) approach is gaining traction for some compelling reasons:

• Compliance: Certain industries face strict regulations on where and how data is stored. Hybrid setups give flexibility without compromising compliance.
• Cost control: Public cloud bills can spiral quickly without tight oversight. Retaining some workloads on-premises can stabilise costs.
• Performance: Keeping latency-sensitive applications closer to the end-user can improve speed and reliability.

A recent Irish Tech News article highlighted that hybrid models also reduce vendor lock-in – something many CIOs and CTOs are keen to avoid after seeing cloud pricing shifts in recent years.

The Untapped Potential of SaaS Optimisation

SaaS tools like Microsoft 365 are already core to most workplaces. But here’s the uncomfortable truth: a lot of licences go unused, and many features are under-leveraged.

This is where AI-led governance frameworks come in:

• Usage analytics: Identify which apps and licences are actually being used, and by whom.
• Automated provisioning: Use AI to adjust access rights in real-time, based on role changes or inactivity.
• Security compliance: Apply dynamic policies to protect sensitive data without over-restricting users.
• Feature adoption tracking: Pinpoint features that can boost productivity and guide targeted training.

According to CloudOffix, this approach isn’t just about cutting costs. It’s about maximising return on investment by ensuring teams are getting full value from every tool they touch.

What CTOs Should Be Doing Now

1. Audit your cloud workloads – decide which should remain on-prem, which belong in private cloud, and which can thrive in public cloud.
2. Review SaaS utilisation data – you might be shocked by how much is paid for but unused.
3. Explore AI-driven governance solutions – look for platforms that integrate with your current Microsoft 365 or other SaaS tools.
4. Plan for scalability and flexibility – hybrid should make it easier to adapt to changing business needs, not harder.

The Bigger Picture

Hybrid IaaS and SaaS optimisation aren’t short-term cost-cutting measures. They’re strategic moves that give organisations more control, more resilience, and more value from the tech they already have.

In an era where the cloud is everywhere, the smartest move might be knowing when not to put everything there – and making the most of what you’ve already invested in.

References

• Irish Tech News – Adapting to an Evolving Technology Landscape
• CloudOffix – Future Trends Shaping Work and Technology

For years, SEO has been a predictable game. Keyword research, backlinks, page speed – rinse and repeat. Then AI search engines arrived, and suddenly the rules changed. If you’re in digital marketing, recruitment marketing, or even a role where web visibility matters, you might want to pause and look closely at what’s happening. Because we’re not just talking about another Google update; we’re talking about a new search battleground altogether.

What Is GEO and Why Should You Care?

Generative Engine Optimisation (GEO) is the art of making your content show up in AI-driven search results. Instead of a page of blue links, users now get direct answers from tools like ChatGPT, Perplexity, or Google’s generative search features. These engines scan the web, summarise it, and deliver answers in a neat paragraph.

The catch? If your content is not written in a way that these AI systems can understand, trust, and cite, you might simply vanish from visibility. According to a recent report by NYMag, this is already shifting traffic patterns for big publishers and smaller brands alike.

Real-World Shifts Already Happening

Rebekah May, an SEO strategist who runs a site called Marketing Aid, recently shared that AI search engines are now responsible for about 10 percent of her site’s traffic. And it’s not just niche marketers seeing the change. Forbes saw referral visits from AI platforms jump from around 11,000 to over 236,000 in a single year. That is a seismic shift in where audiences are coming from.

This is exactly what trend-spotting looks like in real time. The people paying attention now are the ones who will be ahead in two years.

The Career Implications: Recruiters and Job-Seekers Take Note

For recruiters:

• GEO expertise will soon be a rare skill. Candidates who can adapt SEO strategies for AI search will be gold in the digital marketing job market.
• Knowing which marketing professionals have GEO skills – and being able to talk intelligently about them with clients – will differentiate you from other recruiters.

For job-seekers:

• If your role touches content, marketing, or brand visibility, start experimenting now. Learn how AI search engines pull, interpret, and attribute content.
• This is an opportunity to position yourself as someone who can guide teams through the transition, not just react to it.

How to Spot and Act on Early Trends

1. Track tech announcements – When Google, OpenAI, or Microsoft push an update, look beyond the PR and think about the downstream effect on skills demand.
2. Follow the traffic – Use analytics to see where visits are coming from. If AI referrals are creeping up, that’s a signal to pivot your optimisation strategy.
3. Test and iterate – Create content designed for AI summarisation. Short, clear explanations and authoritative sources are key.
4. Build the narrative – In interviews or pitches, be ready to talk about how you’ve adapted to this shift. That story could set you apart.

Why This Matters More Than a Regular SEO Update

The difference here is speed and scale. GEO is not just about tweaking a headline or fixing a meta description. It’s about positioning yourself, your team, or your clients for visibility in a search world that is less about links and more about being the trusted source the AI quotes directly.

Those who learn this early will not just keep up; they’ll lead.

References

• NYMag: SEO Is Dead, Say Hello to GEO
• Hubcopy: The Future of SEO and AI Search
• Wikipedia: Generative Engine Optimisation

Search is changing fast. AI-driven tools like ChatGPT, Perplexity, and Google’s generative search features are reshaping how people find information online. If you’ve built your digital career around SEO, or your role touches content and brand visibility, you’re about to face a new kind of competition.

The good news? Very few professionals have adapted to Generative Engine Optimisation (GEO) yet. Which means the early movers are going to stand out a lot.

First Things First: What GEO Actually Is

GEO is about making your content show up when AI search engines summarise information. Instead of ranking a web page in a list, the goal is to be the source an AI chooses to quote in its answer.

Think of it as optimising for the robot’s brain, not just its index.

Why This Is a Career Opportunity

• Early skill adoption: Companies will soon need people who understand AI search behaviour. Learning it now puts you ahead of most of the talent pool.
• Better career mobility: Mastering GEO can make you valuable across industries – from recruitment marketing to e-commerce to SaaS.
• Employer appeal: GEO expertise signals adaptability, curiosity, and tech fluency – all traits employers love.

How to Build GEO Skills in 5 Practical Steps

1. Study AI Search Behaviours

Experiment with tools like Perplexity or Google’s Search Generative Experience (SGE). Search your target topics and note:

• What kind of language appears in AI summaries
• Which sites or sources get cited

2. Create AI-Friendly Content

Start publishing:

• Clear, well-structured answers to common questions in your niche
• Short sections with bold headings and supporting facts
• Content that cites credible, up-to-date sources (AI loves these)

3. Track AI Referral Traffic

Use analytics to see if AI platforms are already sending visitors to your site or content. Tools like Google Search Console can help you spot early signals.

4. Add GEO Projects to Your Portfolio

Even if it’s a side project, document:

• Your optimisation approach
• The results (screenshots, traffic changes, citations)
• Lessons learned

These case studies are gold in interviews.

5. Follow the Experts and the Data

Read from sources leading the conversation:

• NYMag’s SEO to GEO piece
• Hubcopy’s GEO trend analysis
• SEO and AI integration discussions on LinkedIn

How to Talk About GEO in a Job Search

In your CV and interviews:

• Frame GEO work as “future-proofing” digital visibility
• Highlight your ability to adapt to industry shifts
• Connect GEO skills to business outcomes: increased reach, better engagement, faster adaptation to market changes

Your Next Move

The easiest mistake now is to think “I’ll wait and see.”
By the time GEO is mainstream, it will be a baseline skill. Right now, it’s a differentiator.

If you start experimenting today, you can walk into your next interview or client meeting with something few others have – proof you understand where search is going, and how to get there first.

References

• NYMag: SEO Is Dead, Say Hello to GEO
• Hubcopy: The Future of SEO and AI Search
• Wikipedia: Generative Engine Optimisation

Technology has been quietly reshaping the legal world for some time, but the pace of change has sped up in recent years thanks to AI and automation. If you’re in legal recruitment or the profession itself, it’s worth taking a moment to think about what this means – not just for how work gets done, but for the nature of legal jobs and recruitment itself.

The Rise of AI in Legal Work

AI and automation tools are now capable of handling tasks like:

• Document review
• Contract analysis
• Legal research

These technologies can process vast amounts of data far quicker than any human could. According to a Deloitte report from 2022, over 60% of law firms have already integrated AI or automation into their workflows for routine tasks.

This means lawyers can focus on higher-value work, but it also means some roles — especially those centred on repetitive tasks — are changing or shrinking.

How Legal Jobs Are Evolving

Rather than disappearing entirely, legal jobs are shifting:

• Declining roles: Junior lawyers and paralegals may spend less time on document review, a task increasingly automated.
• Emerging roles: Positions like legal technologists, AI compliance experts, and data privacy officers are becoming more common.
• Changing skills: Tech fluency, strategic thinking, and emotional intelligence are in higher demand.

A 2021 article in the Harvard Law Review suggests that while AI can handle nearly a quarter of a lawyer’s tasks, uniquely human skills remain essential.

Shifts in Legal Recruitment

Recruitment is evolving alongside technology:

• AI tools help screen resumes and even assess cultural fit.
• Firms seek candidates with both legal expertise and digital skills.
• There’s a growing concern about bias in automated recruitment systems.

Despite AI’s role in recruitment, the human element remains crucial. Legal work ultimately revolves around people – understanding their needs and navigating ethical complexities is something AI can’t replicate.

Ethical and Practical Challenges

Automation in legal services raises important questions:

• Who is responsible if AI makes a legal error?
• How do firms protect sensitive data when using AI tools?
• Might automation reduce opportunities for junior staff to learn?

The Law Society of England and Wales stresses the importance of transparency and careful oversight when using AI.

What’s Next? A Balance Between Tech and Human Expertise

It’s tempting to imagine AI taking over legal work, but the future probably looks more like partnership between human lawyers and machines. AI handles routine tasks, while lawyers apply judgement, empathy, and negotiation skills.

For those in recruitment or law, this means:

• Staying adaptable and open to new skills
• Embracing technology as a tool, not a threat
• Recognising the value of lifelong learning

Technology is changing the game – but it’s not rewriting it completely.

References

• Deloitte, 2022 Global Legal Services Industry Outlook
• Harvard Law Review, The Future of Law and AI, 2021
• Law Society of England and Wales, Guidance on the Use of Artificial Intelligence, 2023