Imagine a campaign summary sitting on a strategist’s desk. Before the first stakeholder meeting, the AI tool was already scanning consumer sentiment data, suggesting audience segments, generating three creative directions, and flagging which CTA variations performed best last quarter. At a media agency, programmatic systems optimize bids in real time, and machine learning models predict click-through rates before a single ad is published. In the martech stack, AI personalizes the email journeys of millions of users simultaneously, without the need for humans to touch a single send.
This perceived future is already the operating rhythm of many advertising and marketing organizations today. And at the top of the org chart, the words AI-first, AI-native, and AI-powered have changed accordingly. WPP has committed more than £300m to AI investment this year, building an open marketing platform and partnering with Google. $400 million transaction. Publicis Groupe has promised 300 million euros CoreAI is an in-house AI system built on 2.3 billion consumer profiles. The entire strategy of the holding company is AI.
But here’s a question that rarely appears in press releases. Who in the organization actually understands what that means?
Investment is increasing. Skills comparable to that have not caught up.
Global AI spending is projected to exceed $2.52 trillion The changes are already visible in employment. LinkedIn skills on the rise in 2026 report Prompted Engineering, Workflow Automation, LLMOps, and Data Storytelling are among the fastest growing competencies in India, and Prompted Engineering is now emerging far beyond its technical origins, across HR, marketing, sales, and consulting. Additionally, 46% of recruiters globally now rely on skills data to fill roles, while 74% of recruiters in India say finding qualified talent is more difficult than ever, according to LinkedIn data.
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The demands in advertising and marketing are special.Anand V.“Competencies at the intersection of AI and marketing, such as data analytics, content optimization with AI, customer journey personalization, model governance, machine learning literacy, and the ability to incorporate AI into campaign strategy, are in highest demand. However, advanced technology foundations such as proficiency in NLP and cloud AI, and AI There remains a gap in the talent pool that can translate outputs into strategic outcomes.” business value. ”
The gap Anand describes is not simply a question of whether employees have access to tools. It’s about whether they know what to do with the output. And that distinction is becoming increasingly inadequate in organizations.
Many agencies, platforms, and brands are now positioning themselves as AI-first. Internally, the hope is to create talent that can use AI to improve strategy, content, and media planning and measurement. But whether formal training exists to support that expectation is another matter.
According to Adecco Group’s 2025 report, 60% of business leaders expect their employees to upskill in AI, yet 34% of organizations admit they don’t have a formal AI policy in the workplace without guidance, safeguards, or a plan.
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Roopa BadrinathFounder and Principal Consultant at Turmeric Consulting says, “I sometimes wonder if organizations are democratizing AI access without democratizing AI literacy. Many companies describe themselves as AI-first, but the more important question is: AI-first for whom? For all employees across the organization, for customers, and for the customers they serve? If the answer is truly ‘all of the above,’ then AI Implementation needs to move beyond tool deployment to more comprehensive and deliberate capacity building. effort. ”
Anand agrees, citing data-backed concerns.
“While the use of AI tools is becoming commonplace, with the majority of sales and marketing professionals already using them in some form, formal role-specific training remains limited, leaving talent lacking the deep, practical skills needed to effectively leverage AI on the job. This suggests that many companies may be overestimating how AI-ready their workforce actually is.”
That overestimation also affects the leadership level. According to Gartner research: 65% of CMOs expect AI to dramatically change their roles within two years, but only 32% believe their own skills will require significant changes. The report predicts that by 2027, lack of AI literacy will rank among the top three reasons for CMO turnover at large companies. As Lizzy Foo Kune, Special Vice President Analyst at Gartner, notes in a report, CMOs cannot treat AI as something for their teams to use while leaders sit on the sidelines.
Depth of training still lags behind breadth of implementation
When it comes to who receives training and how, the situation varies depending on job function and level. Most professionals can use AI for drafting, image generation, or extracting findings from large documents. Fewer people can do difficult things.
Himani MangutaniSW Network’s business director explains the split: “Exposure to AI now extends from junior management to senior leadership levels, although the depth of application varies. Within performance marketing and strategy, AI is now being used to build structured frameworks that map target stages, define user actions, generate CTAs variations, and identify optimization paths. Given the measurability of impact, these capabilities are now more clearly integrated.”
However, the integration is still inconsistent. Mangutani speaks candidly about what is missing at the industry level. She says, “What is still missing across the industry is structured, advanced training in automated workflows, predictive thinking, data interpretation, and responsible AI governance, with most programs still focused on using tools rather than building systems.”
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Bhavya MisraA CHRO at Godrej Capital sees the same pattern from an HR perspective, noting that teams dealing with data, performance, and customer engagement tend to pick up on it faster, but the real challenge is spreading adoption across the organization.
She points out the risks of siled deployments. Even if marketing is flexible with AI, friction will arise if finance, HR, and operations aren’t. “Adoption should be horizontal, not hierarchical,” she says. When it comes to marketing in particular, she argues that marketing sits at the intersection of insight, creativity and measurable impact, making it a natural candidate for AI integration as well as an important test case for whether organizations can achieve consistent depth.
Research supports the importance of depth. Research led by University of Michigan doctoral student Snehal Prabhudesai and professor Nikola Banovic I looked into it How students work with large-scale language models and whether they are able to critically evaluate the output of AI.
Using a framework called PromptAuditor, the researchers found that students who did not receive structured instruction struggled to identify bias in AI output and often relied on surface-level interactions. Workshop participants who received no prior structured AI instruction scored an average of 66.86 on the AI Literacy 100-point scale, compared to 84.78 for classroom participants who received formal instruction. This study found that targeted instructional support significantly improves the ability to critically evaluate AI output. Without this, people will be using AI without understanding what it is telling them and why.
The implications for marketing teams are not subtle. As AI shapes audience insights, creative decisions, and campaign strategies, the quality of human judgment applied to that output determines the quality of what reaches consumers.
Roopa Badrinath says, “The quality of AI output is entirely dependent on the quality of the human input entered at the keyboard: assumptions, context, and blind spots. If employees are not trained to recognize and consider their own biases before creating prompts, those biases will not be mitigated, but simply automated at scale.”
Implied expectations: find out for yourself
The more difficult structural question is who is responsible for closing this gap. Formal programs exist, but they are not yet standard.
IBM is committed to training 2 million Build AI learners by 2026 through the SkillsBuild platform. Through the Learning Accelerator, OpenAI partnered In collaboration with the Indian Ministry of Education, AICTE, and six leading universities, including IIT Delhi and IIM Ahmedabad, we will provide ChatGPT Edu access and structured AI training to over 100,000 students and faculty. Microsoft has expanded its Elevate program in collaboration with government agencies to improve the skills of teachers across schools and higher education in India. These are broad, sector-agnostic initiatives that are important. But it doesn’t specifically fill the gap within advertising and marketing organizations.
Inside the agency, the approach is different.
Mangutani points out that some of the more substantive capacity building is happening through customer relationships rather than internally. “For specific briefs, especially for large organizations like Procter & Gamble, we invite you to participate in dedicated training sessions and workshops tailored to AI frameworks and expectations. This allows our teams to not only build internal capacity, but also ensure compliance with global best practices and client-specific standards.”
BCG Research adds useful benchmarks here: twenty two% of companies are beyond the proof-of-concept stage with AI, and only 4% are creating significant business value. The gap between pilot and production is almost always a people problem, not a technology problem.
Bhavya Misra says, “Formal programs help set a shared baseline, unite teams around governance, and reduce fragmentation. At the same time, AI Progress is so rapid that adoption is often accelerated by curiosity and peer-driven experimentation. The challenge is when exploration starts to feel like an implicit expectation rather than an encouraged opportunity. Not everyone has the same time, confidence, and exposure, and without explicit organizational support, gaps can emerge silently.
That quiet gap is what worries me. The implicit message of “upskill yourself or fall behind” has already been absorbed by employees who feel at risk but have not received a roadmap.
Roopa Badrinath identifies the skills that are the hardest to find and the hardest to build. “The most difficult skills to build or find are not technical; they are human: critical judgment of AI output, data literacy that recognizes bias and risk, and the ability to translate the use of AI into strategic value rather than superficial efficiency.”
The difference between superficial and substantive AI usage is that AI fluency is already starting to show up in reward data. Roles that explicitly require AI skills command significantly higher pay premiums compared to comparable traditional roles. Anand V notes, “Organizations are increasingly incorporating AI expectations into job descriptions, with generative AI and data-driven marketing capabilities surging in demand and delivering significant pay premiums compared to traditional skills. Meanwhile, structured upskilling programs are not yet widespread or mature enough to fill the gap at scale.”
Bhavya Misra says it most clearly. “AI first is really about changing the way we work. It can’t just be a positioning statement, it has to be translated into an operational discipline. In practice, it means starting with structured literacy at the fundamentals and then moving steadily to functional integration at the workflow level. It’s less about exposing teams to tools and more about incorporating AI into planning, optimization, and decision-making. It becomes part of the team’s mindset, not just something the team tries.”
The advertising and marketing industry has always moved at the speed of culture. Advances in AI are accelerating. Declaring AI-first intent has become a baseline rather than a differentiator. What separates organizations that truly benefit from AI from those that only seem to benefit from it is whether their employees understand it well enough to push back, direct it, and build on it. Currently, most organizations do not yet have the size of workforce they need.
