Service organisations are not staring at an AI apocalypse; they are facing something subtler and more personal—a widening gap between professionals who can work fluently with AI and those who still treat it as a sideshow. In my view, the real competitive battleground in 2026 is not “AI vs jobs”, but how quickly service leaders and practitioners can redesign work, skills, and careers around human–AI collaboration in customer service, contact centres, and shared-services environments.

The Fault Line I See In Service Work

In the clients and teams, I observe, the sharpest divide is no longer between “tech” and “non‑tech” roles, but between AI‑literate and AI‑indifferent professionals. The AI‑literate group is quietly becoming the default choice for new mandates, stretch projects because they can take on more work, with more variation, at higher quality.

Evidence from multiple directions supports this picture:

• Breaking jobs into tasks, Andrew Ng points out that for many roles AI in service organizations can do only 30–40 percent of the work “for the foreseeable future”, leaving 60–70 percent uniquely human.
• The consequence, in his words, is not mass unemployment but rising productivity gaps: “A person that uses AI will be so much more productive, they will replace someone that doesn’t use AI.”
• Large‑scale field experiments in AI in customer service show that giving agents a generative AI assistant increases issues resolved per hour by roughly 14–15 percent on average, with the biggest gains for less experienced workers.

My reading of all this is straightforward: jobs are not disappearing wholesale, but the internal hierarchy within roles is being rewritten around AI fluency, AI skills, and the ability to work effectively with AI in customer service workflows.

How I Think About “Good” And “Bad” AI In Services

I don’t buy either extreme—the “AI will take all the jobs” panic or the “nothing really changes” complacency. What I see inside AI‑enabled service organizations is a messy middle where AI can either elevate the craft of service or hollow it out, depending on how we use it.

Two ideas from economics help sharpen this distinction, and I find them very useful in boardrooms:

• AI as complement, not cheap imitation.
  Erik Brynjolfsson’s work on AI complementarity shows that when AI is used to extend what humans do well—judgment, tacit knowledge, creativity—both productivity and worker outcomes tend to improve. Customer‑service studies where AI copilots make junior agents perform closer to experts are a concrete example of AI in customer service used as augmentation rather than automation.
• “So‑so automation” as a real risk.
  Daron Acemoglu warns about “so‑so automation”—systems that cut some labour but don’t meaningfully improve productivity or experience. Think of the IVR mazes and clumsy chatbots everyone hates: they displace agents, don’t delight customers, and barely move the needle on costs in customer service and contact centres.

My own filter for any AI initiative in a service organisation is therefore simple:
If it doesn’t improve either experience or productivity in a way that people on the ground can feel, it’s probably bad automation and should be redesigned or killed.

Where Service Work Is Really Changing

The most honest way to understand AI in services is to look beyond slides and into specific workflows. Three areas stand out in my view: AI in contact centres, AI for “non‑technical” roles, and AI‑first service leadership.

1. Contact centres as live laboratories

Contact centres are already living through the transition that many other service functions will experience next, making them the most visible testbed for AI in customer service:

• Automation is swallowing routine, not the whole job.
  Virtual agents are beginning to handle a growing share of routine L1 enquiries across chat, voice, and messaging, using better language understanding and tool use. But edge cases, emotionally charged situations, and multi‑system problems still land with humans in AI‑enabled contact centres.
• Humans are moving up the stack.
  The best centres I see are deliberately positioning human agents as escalation specialists, relationship owners, and fixers of broken journeys—not just “handle time” machines. In other words, AI in contact centres is pushing people toward higher‑value work.
• AI is becoming a live coach.
  Conversation intelligence and generative AI copilots provide real‑time suggestions, knowledge retrieval, and automatic summarisation, reducing cognitive load while raising consistency. This is exactly the pattern Brynjolfsson and others document: AI assistance in customer service boosts productivity and can even reduce turnover by giving agents better support.

My conclusion: in contact centres, AI is not replacing people en masse, but agents who refuse to work with AI are increasingly outperformed by peers who lean into these tools and build new AI skills. Hansa Cequity with its Varta Solution Suite precisely addresses the above-mentioned points.

2. The “non‑technical” canaries: marketers, recruiters, analysts

The roles I worry about most in AI workforce transformation are not coders; they are marketers, recruiters, analysts, and relationship managers. These are precisely the profiles Ng calls out when he says employers “strongly prefer” people who can work with AI at a quasi‑builder level, not just as occasional chat users.

What I see in practice:

• Marketers who can use AI to generate ideas, segment audiences, run experiments, and interpret signals are becoming growth engines; those who only review creative are getting boxed into low‑leverage approval roles.
• Recruiters who use AI to source, prioritise, summarise, and personalise outreach free up time for actual conversations and stakeholder management; those who don’t are drowning in manual screening and admin.
• Analysts who let AI handle data prep and first‑pass commentary spend more time on framing the right questions and influencing decisions; those who insist on manual pipelines are simply slower and less visible in decision forums.

My view is that every one of these professions is being redefined from “task performer” to “workflow and judgment layer on top of AI”. The sooner people accept that shift in how AI in service organizations works, the more agency they have over their careers.

3. Service leadership as workflow design, not technology procurement

For service leaders, I don’t think the core question is “Which model should we use?” It’s “What kind of AI‑enabled work system are we building for our people?”

The patterns I see in successful teams are:

• They avoid “AI as pepper”.
  Instead of sprinkling AI into random journeys, they pick a few critical processes in customer service, contact centres, or shared services and redesign them end‑to‑end around human–AI collaboration. That means rethinking metrics, roles, handoffs, and escalation paths.
• They invest in learning velocity, not just tools.
  Ng talks a lot about upskilling and “learning velocity”, especially for India’s $280 billion IT and services ecosystem. The best leaders internalise this: they create sandboxes, allocate time for experimentation, and explicitly recognise AI‑enabled productivity in performance systems, turning AI skills into a visible career lever.
• They align AI with what’s special about their workforce.
  Acemoglu’s advice to executives is to focus on amplifying the things that are uniquely valuable about their people, not blindly automating tasks that look “automatable”. I find that a useful sanity check for every roadmap item in AI in service organizations.

In short: leadership’s real job is to design complementary human–AI systems, not to chase AI headlines.

My Three‑Horizon Lens For Service Organizations

To make this actionable, I use a simple three‑horizon lens with clients. The experts inform it, but the framework is mine and tuned to AI in service organizations.

Horizon 1: Assist – AI as co‑pilot

Focus: Use AI to help humans inside existing workflows.

• Examples: AI agent assist in contact centres, AI search for frontline teams, email and document drafting support in customer service and internal operations.
• Risk if you stay here forever: you get incremental gains, but competitors who redesign workflows around human–AI collaboration leapfrog you.
• Professional shift: every agent, marketer, recruiter, and manager needs to be able to operate AI tools fluently, critique them, and correct them in real time as part of their core AI skills.

My belief: this is the minimum bar for AI in service organizations in 2026. If you’re not here yet, the priority is catching up.

Horizon 2: Re‑engineer – Human–AI collaboration by design

Focus: Redesign processes so that humans and AI each do what they are best at.

• Examples: intelligent triage and routing in AI‑enabled contact centres, omnichannel journeys with AI maintaining context, back‑office workflows that pair AI document understanding with human sign‑off.
• Risk if you ignore it: AI‑native competitors run leaner, more responsive service organisations with smaller teams and better economics.
• Professional shift: service professionals must see themselves as workflow designers and product owners of their own processes, not just “resources” executing steps, which is a big mindset change in AI workforce transformation.

This is where, in my experience, new roles are being carved out: people who can talk both “service” and “systems” are in demand.

Horizon 3: Reimagine – New services, not just cheaper ones

Focus: Use AI to offer experiences that were not previously possible.

• Examples: proactive service based on predictive signals, hyper‑personalised customer journeys, collaborative advisory offerings where AI and humans co‑create with clients in real time.
• Risk if you never get here: you become a commodity provider of basic interactions while margin pools move elsewhere.
• Professional shift: senior service leaders and high‑potential talent need to operate at the intersection of domain expertise, data, and design, using AI as a building block for new propositions and new AI in customer experience models.

My own conviction is that Horizon 3 is where the most interesting service careers of the next decade will be built.

A Personal Playbook For Service Professionals

At the individual level, I think every service professional—whether in CX, marketing, HR, IT services, or operations, has two jobs: doing the work, and learning how to do that work with AI. This is the real AI skills agenda in service organizations.

Here is the playbook I recommend:

• Treat AI fluency as a core professional muscle.
  Ng is right to say that in many roles the person who uses AI will replace the person who doesn’t. I would go further: AI competence should sit alongside communication and domain expertise in your self‑definition as a professional in modern customer service and CX.
• Aim for complementarity in your daily tasks.
  Each week, ask: “Where can AI best amplify my strengths?” That might be using AI for prep while you own delivery, using it to generate options you then curate, or letting it act as a coach while you stay in charge of the judgment calls in customer conversations and stakeholder meetings.
• Refuse “so‑so automation” in your context.
  If a bot, script, or workflow makes things worse for customers or colleagues and delivers no clear productivity benefit, say so and help redesign it. This is how you protect both the craft and the reputation of service work in an AI‑driven organization.
• Build learning velocity, not just skills.
  The tools will keep changing. What matters is your ability to keep up: weekly experimentation, sharing patterns with peers, and taking on small projects that stretch your AI capabilities. This is the heart of personal AI workforce transformation.
• Deliberately climb the value chain.
  Move from answering questions to designing knowledge systems, from solving tickets to redesigning journeys, from filling roles to shaping workforce strategy. Research on AI augmentation suggests that workers who reposition themselves where AI is a complement, not a substitute, are more likely to see their opportunities and earnings grow.

When I cut through the noise, my view is this: AI is not about to wipe out service work, but it is rewriting the rules of who thrives in these roles and what “good” service organisations look like. The leaders and professionals who treat AI as a partner, reject shallow automation, and consciously move toward higher‑value human–AI collaboration in customer service and beyond will define the next decade of service excellence. Everyone else risks discovering too late that it wasn’t AI that replaced them—it was colleagues who learned to work with it faster.