In the clients and teams, I observe, the sharpest divide is no longer between \u201ctech\u201d and \u201cnon\u2011tech\u201d roles, but between AI\u2011literate and AI\u2011indifferent professionals. The AI\u2011literate 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.<\/p>\n\n\n\n
Evidence from multiple directions supports this picture:<\/p>\n\n\n\n
- \n
- Breaking jobs into tasks, Andrew Ng points out that for many roles\u00a0AI in service organizations<\/em>\u00a0can do only 30\u201340 percent of the work \u201cfor the foreseeable future\u201d, leaving 60\u201370 percent uniquely human.<\/li>\n\n\n\n
- The consequence, in his words, is not mass unemployment but rising productivity gaps: \u201cA person that uses AI will be so much more productive, they will replace someone that doesn’t use AI.\u201d<\/li>\n\n\n\n
- Large\u2011scale field experiments in AI in customer service show that giving agents a generative AI assistant increases issues resolved per hour by roughly 14\u201315 percent on average, with the biggest gains for less experienced workers.<\/li>\n<\/ul>\n\n\n\n
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.<\/p>\n\n\n\n
How I Think About \u201cGood\u201d And \u201cBad\u201d AI In Services<\/strong><\/p>\n\n\n\n
I don\u2019t buy either extreme\u2014the \u201cAI will take all the jobs\u201d panic or the \u201cnothing really changes\u201d complacency. What I see inside AI\u2011enabled 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.<\/p>\n\n\n\n
Two ideas from economics help sharpen this distinction, and I find them very useful in boardrooms:<\/p>\n\n\n\n
- \n
- AI as complement, not cheap imitation.<\/strong>
Erik Brynjolfsson\u2019s work on AI complementarity shows that when AI is used to extend what humans do well\u2014judgment, tacit knowledge, creativity\u2014both productivity and worker outcomes tend to improve. Customer\u2011service 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.<\/li>\n\n\n\n- \u201cSo\u2011so automation\u201d as a real risk.
<\/strong>Daron Acemoglu warns about \u201cso\u2011so automation\u201d\u2014systems that cut some labour but don\u2019t meaningfully improve productivity or experience. Think of the IVR mazes and clumsy chatbots everyone hates: they displace agents, don\u2019t delight customers, and barely move the needle on costs in customer service and contact centres.<\/li>\n<\/ul>\n\n\n\nMy own filter for any AI initiative in a service organisation is therefore simple:
If it doesn\u2019t improve either experience or productivity in a way that people on the ground can feel, it\u2019s probably bad automation and should be redesigned or killed.<\/p>\n\n\n\nWhere Service Work Is Really Changing<\/strong><\/p>\n\n\n\n
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 \u201cnon\u2011technical\u201d roles, and AI\u2011first service leadership.<\/p>\n\n\n\n
1. Contact centres as live laboratories<\/strong><\/p>\n\n\n\n
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:<\/p>\n\n\n\n
- \n
- 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\u2011system problems still land with humans in AI\u2011enabled contact centres.<\/li>\n\n\n\n - 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\u2014not just \u201chandle time\u201d machines. In other words, AI in contact centres is pushing people toward higher\u2011value work.<\/li>\n\n\n\n - AI is becoming a live coach.
Conversation intelligence and generative AI copilots provide real\u2011time 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.<\/li>\n<\/ul>\n\n\n\nMy 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<\/strong> precisely addresses the above-mentioned points.<\/p>\n\n\n\n
2. The \u201cnon\u2011technical\u201d canaries: marketers, recruiters, analysts<\/strong><\/p>\n\n\n\n
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 \u201cstrongly prefer\u201d people who can work with AI at a quasi\u2011builder level, not just as occasional chat users.<\/p>\n\n\n\n
What I see in practice:<\/p>\n\n\n\n
- \n
- 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\u2011leverage approval roles.<\/li>\n\n\n\n
- Recruiters who use AI to source, prioritise, summarise, and personalise outreach free up time for actual conversations and stakeholder management; those who don\u2019t are drowning in manual screening and admin.<\/li>\n\n\n\n
- Analysts who let AI handle data prep and first\u2011pass 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.<\/li>\n<\/ul>\n\n\n\n
My view is that every one of these professions is being redefined from \u201ctask performer\u201d to \u201cworkflow and judgment layer on top of AI\u201d. The sooner people accept that shift in how AI in service organizations works, the more agency they have over their careers.<\/p>\n\n\n\n
3. Service leadership as workflow design, not technology procurement<\/strong><\/p>\n\n\n\n
For service leaders, I don\u2019t think the core question is \u201cWhich model should we use?\u201d It\u2019s \u201cWhat kind of AI\u2011enabled work system are we building for our people?\u201d<\/p>\n\n\n\n
The patterns I see in successful teams are:<\/p>\n\n\n\n
- \n
- They avoid \u201cAI as pepper\u201d.
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\u2011to\u2011end around human\u2013AI collaboration. That means rethinking metrics, roles, handoffs, and escalation paths.<\/li>\n\n\n\n - They invest in learning velocity, not just tools.
Ng talks a lot about upskilling and \u201clearning velocity\u201d, especially for India\u2019s $280 billion IT and services ecosystem. The best leaders internalise this: they create sandboxes, allocate time for experimentation, and explicitly recognise AI\u2011enabled productivity in performance systems, turning AI skills into a visible career lever.<\/li>\n\n\n\n - They align AI with what\u2019s special about their workforce.
Acemoglu\u2019s advice to executives is to focus on amplifying the things that are uniquely valuable about their people, not blindly automating tasks that look \u201cautomatable\u201d. I find that a useful sanity check for every roadmap item in AI in service organizations.<\/li>\n<\/ul>\n\n\n\nIn short: leadership\u2019s real job is to design complementary human\u2013AI systems, not to chase AI headlines.<\/p>\n\n\n\n
![\"\"](\"https:\/\/cxmlab.com\/wp-content\/uploads\/2026\/02\/ai-service-providers.jpg\")
<\/figure>\n\n\n\nMy Three\u2011Horizon Lens For Service Organizations<\/strong><\/p>\n\n\n\n
To make this actionable, I use a simple three\u2011horizon lens with clients. The experts inform it, but the framework is mine and tuned to AI in service organizations.<\/p>\n\n\n\n
Horizon 1: Assist \u2013 AI as co\u2011pilot<\/strong><\/p>\n\n\n\n
Focus: Use AI to help humans inside existing workflows.<\/p>\n\n\n\n
- \n
- Examples: AI agent assist in contact centres, AI search for frontline teams, email and document drafting support in customer service and internal operations.<\/li>\n\n\n\n
- Risk if you stay here forever: you get incremental gains, but competitors who redesign workflows around human\u2013AI collaboration leapfrog you.<\/li>\n\n\n\n
- 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.<\/li>\n<\/ul>\n\n\n\n
My belief: this is the minimum bar for AI in service organizations in 2026. If you\u2019re not here yet, the priority is catching up.<\/p>\n\n\n\n
Horizon 2: Re\u2011engineer \u2013 Human\u2013AI collaboration by design<\/strong><\/p>\n\n\n\n
Focus: Redesign processes so that humans and AI each do what they are best at.<\/p>\n\n\n\n
- \n
- Examples: intelligent triage and routing in AI\u2011enabled contact centres, omnichannel journeys with AI maintaining context, back\u2011office workflows that pair AI document understanding with human sign\u2011off.<\/li>\n\n\n\n
- Risk if you ignore it: AI\u2011native competitors run leaner, more responsive service organisations with smaller teams and better economics.<\/li>\n\n\n\n
- Professional shift: service professionals must see themselves as workflow designers and product owners of their own processes, not just \u201cresources\u201d executing steps, which is a big mindset change in AI workforce transformation.<\/li>\n<\/ul>\n\n\n\n
This is where, in my experience, new roles are being carved out: people who can talk both \u201cservice\u201d and \u201csystems\u201d are in demand.<\/p>\n\n\n\n
Horizon 3: Reimagine \u2013 New services, not just cheaper ones<\/strong><\/p>\n\n\n\n
Focus: Use AI to offer experiences that were not previously possible.<\/p>\n\n\n\n
- \n
- Examples: proactive service based on predictive signals, hyper\u2011personalised customer journeys, collaborative advisory offerings where AI and humans co\u2011create with clients in real time.<\/li>\n\n\n\n
- Risk if you never get here: you become a commodity provider of basic interactions while margin pools move elsewhere.<\/li>\n\n\n\n
- Professional shift: senior service leaders and high\u2011potential 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.<\/li>\n<\/ul>\n\n\n\n
My own conviction is that Horizon 3 is where the most interesting service careers of the next decade will be built.<\/p>\n\n\n\n
A Personal Playbook For Service Professionals<\/strong><\/p>\n\n\n\n
At the individual level, I think every service professional\u2014whether 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.<\/p>\n\n\n\n
Here is the playbook I recommend:<\/p>\n\n\n\n
- \n
- Treat AI fluency as a core professional muscle.
<\/strong>Ng is right to say that in many roles the person who uses AI will replace the person who doesn\u2019t. I would go further: AI competence should sit alongside communication and domain expertise in your self\u2011definition as a professional in modern customer service and CX.<\/li>\n\n\n\n- Aim for complementarity in your daily tasks.<\/strong>
Each week, ask: \u201cWhere can AI best amplify my strengths?\u201d 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.<\/li>\n\n\n\n- Refuse \u201cso\u2011so automation\u201d in your context.
<\/strong>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\u2011driven organization.<\/li>\n\n\n\n- Build learning velocity, not just skills.<\/strong>
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.<\/li>\n\n\n\n- 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.<\/li>\n<\/ul>\n\n\n\nWhen 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 \u201cgood\u201d service organisations look like. The leaders and professionals who treat AI as a partner, reject shallow automation, and consciously move toward higher\u2011value human\u2013AI collaboration in customer service and beyond will define the next decade of service excellence. Everyone else risks discovering too late that it wasn\u2019t AI that replaced them\u2014it was colleagues who learned to work with it faster.<\/p>\n","protected":false},"excerpt":{"rendered":"
AI isn\u2019t about to wipe out service jobs\u2014but it is quietly rewriting who thrives in them. The real divide I see in 2026 is between AI\u2011literate and AI\u2011indifferent professionals. Service leaders and practitioners who treat AI as a partner, redesign workflows, and build new skills will define the next decade of customer experience and service excellence.<\/p>\n","protected":false},"author":2,"featured_media":1926,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[231,230,229,237,238,239],"class_list":["post-1925","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-change-management","tag-ai-in-contact-centres","tag-ai-in-customer-service","tag-ai-in-service-organizations","tag-ai-skills","tag-ai-workforce-transformation","tag-human-ai-collaboration"],"_links":{"self":[{"href":"https:\/\/cxmlab.com\/index.php\/wp-json\/wp\/v2\/posts\/1925","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cxmlab.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cxmlab.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cxmlab.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/cxmlab.com\/index.php\/wp-json\/wp\/v2\/comments?post=1925"}],"version-history":[{"count":1,"href":"https:\/\/cxmlab.com\/index.php\/wp-json\/wp\/v2\/posts\/1925\/revisions"}],"predecessor-version":[{"id":1928,"href":"https:\/\/cxmlab.com\/index.php\/wp-json\/wp\/v2\/posts\/1925\/revisions\/1928"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cxmlab.com\/index.php\/wp-json\/wp\/v2\/media\/1926"}],"wp:attachment":[{"href":"https:\/\/cxmlab.com\/index.php\/wp-json\/wp\/v2\/media?parent=1925"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cxmlab.com\/index.php\/wp-json\/wp\/v2\/categories?post=1925"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cxmlab.com\/index.php\/wp-json\/wp\/v2\/tags?post=1925"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
- Aim for complementarity in your daily tasks.<\/strong>
- Treat AI fluency as a core professional muscle.
- They avoid \u201cAI as pepper\u201d.
- \u201cSo\u2011so automation\u201d as a real risk.