When every ad agency specialises in AI, what happens to AI agencies?

What began as a rush of standalone AI shops has evolved into a structural shift, redefining how agencies embed intelligence and deliver growth.

In 2022 and early 2023, 'AI agency' became one of the most aggressively used labels in the marketing ecosystem. Generative AI had just exploded into public consciousness. Tools like ChatGPT and Midjourney democratised creation. According to McKinsey’s State of AI 2023 report, generative AI alone was estimated to add between $2.6 trillion to $4.4 trillion annually to the global economy. 

Meanwhile, Gartner’s 2023 CMO Spend Survey showed that while marketing budgets remained tight, investment in marketing technology and automation was among the few areas seeing growth. This was also a sign that CMOs were under pressure to innovate, and while purse strings were tightening, AI experimentation wasn’t.

And so, AI boutiques emerged, not just as experimental labs, but as commercial propositions.

When AI became the pitch

AI boutiques were lean, technical, specialist shops built around one promise: speed, intelligence, automation. They didn’t look like traditional agencies. They didn’t talk like traditional agencies. They didn’t bill like traditional agencies. They solved problems legacy structures were slow to fix.

Rajiv Dingra, Founder & CEO, ReBid, recalls what that early wave addressed:

“Early AI agencies stepped into gaps traditional agencies were structurally slow to address — like real-time optimisation, predictive performance modelling, automated creative testing, and cross-platform data unification. Most legacy agencies were still organised around channels and manual workflows, whereas AI-first players focused on speed, scale and outcome-driven automation.”

Similarly, Mohit Joshi, CEO, Havas Media Network India, agrees that early AI players identified a real market gap.

“They identified the right opportunity at the right time. AI unlocked speed, responsiveness and insights that were previously difficult to access. Early AI agencies significantly improved efficiency across bidding, targeting and automation.”

In short, traditional agencies were organised by silos: creative, media, digital, and data. AI boutiques were organised throughout.

Gopa Menon, COO & Co-founder, Theblurr, says, “Speed and cost, two things traditional agencies are structurally designed to resist. 

In the earlier days and now as well, AI shops solved the "blank page" problem. They weren't only replacing the final polished TVC, but they were producing storyboards, mood boards, and variations at a pace that allowed clients to test ten ideas in the time it usually took to approve one. They solved the problem of throughput in a content-hungry world.”

And for a brief moment, that advantage was powerful. 

The novelty fades

As AI tools became widely accessible, the scarcity that once powered specialist positioning began to erode.

Capturing the turning point, Menon says, “The ‘Novelty Curve’ flattened much faster than anyone expected.” 

He explains that two years ago, the mere ability to generate a decent image or text using AI was itself a sellable service, as the barrier to entry felt high for the average CMO. Agencies were effectively selling access to the technology. However, once these tools shifted from complex code repositories to simple browser-based interfaces, that “access” arbitrage disappeared almost overnight.

As the initial excitement settled, a more measured assessment began to emerge. Ram Jalan, Director Digital Transformation, AI and Solution Consulting, offers that counterbalance.

Jalan says, “A significant portion of the early AI market was driven by novelty, with agencies focusing on prompts, prototypes, and experiments but lacking the capabilities to fully integrate into brands' Martech stacks, data layers, or operating models.”

The early AI moment, then, was both an innovation and an experiment. Agencies dramatically improved production throughput and testing agility, but many stopped short of embedding intelligence across the full marketing funnel. Generating outputs was easier than integrating them into governance frameworks, data ecosystems and long-term growth strategy.

This is where the distinction between tech capability and marketing depth became clearer. 

As Menon explains, many standalone AI specialists were tech-first rather than marketing-first. They could generate thousands of assets at scale, but often lacked the strategic grounding to define why those assets should exist in the first place. 

As the market matured, that strategic gap became harder to ignore. The ecosystem began to reorganise around scale, integration and institutional muscle.

Bought, built or left behind

Some boutiques evolved or found strategic buyers. 

Rephrase.ai was among the earliest Indian AI-led creative technology companies to position itself at the intersection of generative AI and brand communication. It gained global attention as the technology partner behind Cadbury’s ‘Not Just a Cadbury Ad’ campaign, enabling thousands of small retailers to generate customised ads featuring Shah Rukh Khan, a landmark moment for AI-powered advertising in India.

In 2023, it was acquired by Adobe to strengthen Adobe’s generative AI and video personalisation capabilities across Creative Cloud and Experience Cloud. 

The deal reflected a broader shift in the ecosystem: early AI boutiques with strong proprietary technology and proven marketing use cases were increasingly being absorbed into larger global platforms rather than operating as standalone specialists.

Some boutiques struggled because they lacked marketing depth, client servicing muscle, or the ability to integrate AI into broader brand ecosystems.

At the same time, integrated networks began building their own AI verticals. As the ecosystem matured, it wasn’t just independent AI-first players driving change; global holding companies started embedding artificial intelligence directly into their organisational architecture. Players like WPP, Publicis Groupe, Omnicom, Havas and Dentsu all signalled strategic bets on proprietary AI capabilities rather than outsourcing intelligence solely to specialists.

WPP rolled out WPP Open, an AI-powered platform designed to help brands plan, create and publish campaigns with machine assistance, signalling a move from experimentation to scalable, client-facing AI products. Publicis Groupe embedded AI into its operating model through Marcel, its internal AI-powered platform connecting talent and workflows globally. Omnicom integrated AI across planning and media through its Omni intelligence platform, while Havas structured its transformation around Converged.AI as a core operating system rather than a bolt-on tool. Dentsu also expanded its innovation hubs and AI labs to bring together creative and technology talent, accelerating media insights and CX analysis.

“The large networks and seasoned independents caught up by treating AI not as a separate service, but as a horizontal layer just like the internet or mobile before it,” Menon adds.

As integration became the benchmark, that gap widened. What had initially been a competitive advantage began to feel incomplete in environments where accountability, brand coherence and measurable outcomes mattered more.

Joshi adds that while early AI agencies were directionally right, their positioning may have been overstated.

“However, positioning ‘AI-first’ as the core differentiator may have been overplayed. The real client challenge has always been delivering actionable outcomes that require strategic context, creative intelligence, and integration across media, content, and business objectives. With the rise of Generative AI, the shift has moved beyond targeting into CRM, content and deeper consumer connections.”

In effect, the label that once signalled differentiation began to lose its edge. In today’s day and age, the term “AI agency” itself is being questioned.

Joshi makes the commercial reality clear:

“AI today is table stakes. The value no longer lies in being standalone, but in how AI is embedded within a strategic framework. Brands do not buy AI. They buy growth solutions powered by intelligence, creativity and measurable results.”

The emphasis has shifted from tooling to orchestration. Dingra echoes this transformation in operating models:

“The value proposition has shifted from ‘we do AI’ to ‘we operationalise intelligence across the entire growth stack.’”

The implications are profound. AI is no longer a department. For agencies, that means restructuring workflows. For clients, it means fewer silos and more unified accountability.

How early AI shops are surviving

Not all early AI-first players disappeared. Many pivoted, and quickly.

Describing the sustainability test, Menon says,“The ‘wrapper’ agencies those just putting a UI on top of OpenAI are in trouble.” Instead, he shares, “The sustainable pivot is toward Process IP.”

Productising workflows rather than simply offering prompt engineering has become key. Agencies that build proprietary systems, connecting brand guidelines, performance data, and automation pipelines, are finding defensibility.

Outlining the survival archetypes, Jalan shares a systematic list.

“First, grow into full-service agencies with backward integration or white-label partnerships. Sell outcomes instead of AI-tokens.” 

Second, sticking to a niche or specific use-case like AI-driven SEO, CRO, etc., rather than an all-out competition with networks or groups.

Third, move to productization or ‘agency-in-a-box’ platforms for smaller brands, and monetise via the traditional SaaS model.”

In essence, the market split into three tracks: integration, specialisation, or productisation. The generalist AI shop without IP became the most exposed.

What do clients want now?

Client expectations have matured dramatically.

Dingra shares, “Most enterprise clients increasingly prefer AI embedded within an integrated agency or operating partner model, because AI decisions now affect media, creative, measurement and commerce simultaneously.”

Fragmentation fatigue is real. CMOs are reluctant to manage multiple specialist vendors for intelligence layers that now touch every part of the funnel.

Reinforcing this trend, Joshi says, “Clients now prefer AI embedded within end-to-end marketing, data and commerce solutions because it reduces risk, simplifies governance, and directly ties to business outcomes.”

Governance, compliance, bias management, and data privacy are no longer afterthoughts. As AI scales, accountability scales with it.

Where independent AI players still have an edge

Despite consolidation, opportunity remains. But it’s sharper and more specific.

Menon believes agility is the differentiator. He says, “The value is in being the ‘fastest learner in the room.’”

Independent players can build AI-native workflows without legacy friction. They can pivot models, processes, and production pipelines faster than networks retrofitting decades-old systems.

Jalan identifies defensible white spaces in vertical depth and governance. He says, “Independent studios that build proprietary platforms or SLMs (small language models) can act as infrastructure for both brands and other agencies.”

He also highlights the rise of neutral governance advisory, an emerging layer as brands seek AI frameworks that are “safe, scalable, and sustainable.”

Meanwhile, Dingra highlights the intersection play. He says, “Defensibility will come from owning the intersection of three layers: proprietary data systems, domain-specific intelligence, and automation that directly moves business KPIs.”

The future, then, is less about claiming AI expertise and more about embedding it into measurable systems.

Between 2023 and 2026, the Indian agency ecosystem has experienced compressed transformation from a differentiator to an operating system.

Early AI agencies sparked urgency. Networks industrialised it. Independents refined it. Clients professionalised it.

The agencies that will define the next phase won’t be those that merely ‘use AI.’ They will be those who operationalise intelligence responsibly, creatively, and measurably.