Digital
The creative cull: how AI is coming for the marketers, ad men and researchers
Robots aren’t taking over yet, but the writing may already be on the wall for some of the US’ most glamorous white-collar jobs.
CALIFORNIA: The robots are not, it turns out, storming the factory floor. They are sitting quietly at a MacBook in a Soho agency, rewriting your copy, summarising your focus groups and generating your mood boards, and nobody has been sacked. Yet.
A new report from Anthropic, the AI company behind the Claude chatbot, offers the most rigorous look to date at what artificial intelligence is actually doing to jobs, as opposed to what doomsayers and boosters claim it might. The verdict from economists Maxim Massenkoff and Peter McCrory is nuanced but pointed: there is no mass unemployment so far, but some sectors have good reason to be nervous. Marketing, market research and the arts are squarely in the crosshairs.
The researchers introduce a new measure called “observed exposure.” It goes beyond theoretical speculation about what AI could do and instead tracks what it is already doing, drawing on real Claude usage data. The approach is clever. They weight automated uses, where the machine performs the job entirely, more heavily than augmentative ones, where it merely assists. They then map this onto roughly 800 occupations, weighted by how much time workers actually spend on each task. For now the target user base has been the US market, but the findings offer a glimpse of what may be happening in other countries as well.
The results are sobering for the creative and analytical classes. Market research analysts and marketing specialists clock in at 64.8 per cent observed exposure, meaning nearly two-thirds of their daily tasks are already being performed, at least in part, by AI in professional settings. The leading automated task is preparing reports, illustrating data graphically and translating complex findings into written text. In other words, this is the kind of work junior analysts spend most of their days doing.
Arts and media fare little better. The sector shows meaningful theoretical exposure, as large language models can in principle handle the lion’s share of tasks, though observed usage still lags behind capability. The gap is narrowing, however, and the direction of travel is unambiguous.
Here is the sting in the tail. The workers most exposed to AI disruption are not, as popular mythology suggests, low-paid drudges. They are older, better educated, more likely to be women and considerably better paid, earning 47 per cent more per hour on average than their least-exposed counterparts. Graduate degree holders are nearly four times as prevalent in the high-exposure group. The creative professional, the senior analyst and the market researcher with an MBA are precisely the people who should be paying attention.
“We’re not talking about the checkout operator,” the paper implies. “We’re talking about the account planner.”
The most alarming signal in the data concerns not those already in jobs, but those trying to enter them. Among workers aged 22 to 25, hiring into highly exposed occupations has slowed measurably since the release of ChatGPT in late 2022. There has been a 14 per cent drop in the job-finding rate, a figure the authors describe as “just barely statistically significant.” Young people are, in effect, finding the door to exposed professions quietly closing. Whether they are staying in education, taking different jobs or simply giving up is not yet clear.
For a bright graduate eyeing a career in market research or media production, this is not merely an academic data point. It is a flashing amber light.
The paper is careful about what it does not find. Unemployment among highly exposed workers has not risen in any statistically meaningful way since the ChatGPT era began. The apocalypse has not arrived. Even in the Computer and Math category, the most theoretically exposed of all, Claude currently covers just 33 per cent of tasks in practice. The gap between what AI can do and what it actually does at scale in professional workflows remains vast.
Think of it less like a tsunami, the authors suggest, and more like a slowly rising tide. The internet did not destroy journalism overnight. It took 20 years and the collapse of a generation of classified advertising revenue. The China trade shock also took decades to fully register in unemployment statistics, and economists are still debating the numbers.
What does this mean for the luvvies, the admen and the pollsters? The honest answer is: not much yet, but watch this space. AI is already doing the grunt work, including data summaries, draft press releases and boilerplate creative briefs. The question is whether it stops there or continues climbing the value chain.
The authors are building a framework to track exactly that and promise to update it as new data arrives. If the tide does come in, they want to see it coming before the sandcastles are already gone.
For now, the creative industries can breathe, but perhaps not too deeply. The machine is not at the door. It is already at the desk.
Digital
GUEST COLUMN: How AI is restructuring distributor and retailer motivation models
From incentives to intelligence, AI is redefining how brands engage channel partners
MUMBAI: Artificial intelligence is rapidly transforming how brands engage with their most critical yet often overlooked stakeholders: distributors, retailers, and last-mile influencers. For Abhinav Jain, co-founder and CEO of Almonds Ai, this shift marks a fundamental departure from traditional, transaction-led incentive models toward behaviour-driven, data-intelligent ecosystems. In this piece, Jain examines how AI is enabling brands to decode partner motivations, predict engagement patterns, and deliver personalised, scalable experiences—ultimately redefining channel relationships from transactional exchanges to long-term growth partnerships.
Across many sectors, there is increasing recognition that motivating those who bring products to market (distributors, retailers, last-mile influencers) poses a growing challenge.
Brands continue to invest significant marketing and digital resources to consumers, yet in many countries and the vast majority of emerging economies, these types of consumer-focused investment areas have had little impact on ultimate product delivery. Rather, it is still the case that traditional retail continues to make up most products sold.
So why is it that the systems built around motivating these channels have yet to evolve?
For decades, distributor and retailer engagement revolved around static schemes – quarterly targets, volume-based rewards, and occasional trade promotions. These programs were designed around transactions, not behaviour. The assumption was simple: if incentives increase, performance will follow.
Now, with the advent of artificial intelligence, the definition of performance is being challenged.
With the development of artificial intelligence, businesses can move beyond simply creating loyalty based on transactional-based models and toward models built on behaviours, the behaviours of channel partners that are intrinsic to their motivations in engaging with particular brands. As a result, the means by which businesses develop relationships within their distribution network are starting to evolve; thus, ultimately changing how brands interact with those within their distribution network.
Assessing engagement: Transitioning from transactional- to behavioural intelligence
Traditional loyalty systems refer to transactional activity (sales data). Although this data is valuable and important, it only provides a partial view of engagement across the channel partner.
For example, a retailer may have a high frequency of sales of a product, but their lack of engagement with the manufacturer would not reflect that they have true loyalty toward that brand. Conversely, a retailer who actively participates in training programmes, acts as brand advocates, and is engaged in learning with the supplier would exhibit more profound levels of loyalty but would have been invisible based on historical incentive programmes.
Artificial intelligence allows for the identification of behaviours that help to address this gap. Brands are able to use a variety of engagement data points, participate in learning programs, respond to communications, redeem behaviour and track platform use behaviour in order to identify motivation through behaviour.
McKinsey has stated that companies that leverage advanced analytics for their sales and distribution functions can achieve as much as a 15-20 per cent increase in productivity due to increased awareness of their behavioural trends throughout their networks.
This visibility of behavioural patterns within channel ecosystems can be transformational to brands as they can now view how partners engage on their path to purchasing products, instead of just measuring the sales revenue generated by those purchases.
Predicting motivations, not just measuring performance
Possibly, the largest contribution of Artificial Intelligence (AI) to helping brands engage with partners via channel ecosystems is its ability to predict future engagement versus simply measuring past performance.
Traditionally, brands only realised that a partner was disengaged (not likely to purchase products) once their sales performance had already declined. By then, the brand would have to use significant amounts of incentives or aggressive promotional activities to recovery their partner’s engagement level.
AI models can help organisations to detect early signs that a partner is becoming disengaged, such as declining participation in learning modules, declining interaction via the platform, or slower reward redemption rates. These indicators can help organisations to proactively engage with their partners before their sales performance begins to decline.
The practical application of AI and predictive analytics gives brands the ability to re-engage with their partners prior to their sales performance declines. For example, instead of developing and implementing broad-reaching incentive programs that provide a “one size fits all” incentive to all partners in an ecosystem, brands are able to develop targeted, engaging re-engagement programmes. This is how personalisation can be done on a large scale, such as across global distribution and retail networks.
The vast majority of distributor and retailer channels have thousands, if not millions, of individual channel partners. Historically, providing personalisation to such a large number of businesses has not been feasible.
However, with the advent of AI, personalisation at scale is becoming a reality.
Brands can now create tailored engagement journeys for all their partners, based on their partner profiles, through some combination of machine learning models and behavioural segmentation. For example, high-performing distributors might receive higher levels of leadership-based recognition and greater incentives to continue to grow. Emerging retailers, on the other hand, might be supported with training, onboarding rewards, and measurable performance milestones.
The shift towards personalisation of partner engagement echoes the direction that consumer marketing is already moving towards.
According to Salesforce’s report, over 70 per cent of customers expect personalisation in the way that brands engage with them. As such, there is a growing expectation for B2B ecosystems to have these same types of expectations from their channel partners.
Gamification and continuous engagement
AI is also radically changing how brands will engage with their channel partners through the use of gamification.
Many traditional incentive-based contests and leaderboards would spark temporary engagement among their participants, but they struggled to sustain engagement over time. With the use of AI, gamification mechanics are evolving dynamically based on historical and evolving participation patterns by their channel partners.
Challenges, rewards, and recognition structures can be modified continuously in order to sustain engagement with all of a brand’s partner segments. This will provide a greater opportunity to move away from episodic campaigns towards ongoing, continuous engagement experiences.
When channel partners receive motivation as part of their daily business activities through recognition, learning, and tracking their performance, long-term loyalty will be achieved.
Aligning motivation to broader impact
There is a growing trend within the channel ecosystem to integrate sustainability and socially responsible behaviours into the channel partner programmes of brands.
Increasingly, brands are motivating their partners to use sustainable practices in their operations, participate in sustainable practices like sustainability-related knowledge programmes, or promote products that are in line with their sustainability objectives.
Brands can use AI to monitor and measure these types of behaviours and incorporate them into their incentive frameworks so that brands can align their commercial objectives with broader social and environmental outcomes.
A shift in the way brands view their channel partners
AI is having the most significant impact on the way that brands are now viewing their channel partners, as it relates to the underlying philosophy of those fundamental relationships.
For the past several decades, many brands have viewed their channel partners as intermediaries in the supply chain. More and more brands are now beginning to view their channel partners as key ‘partners-in-growth,’ and their actions can have a direct impact on market performance.
In fact, all the channel ecosystems are using behavioural engagement platforms to design new models that reward not just transactional behaviour, but also create continuous engagement journeys for their partners, where their partners can receive recognition for their participation, learning, and continued engagement, thereby reinforcing long-term loyalty to the brand.
The future: Intelligent channel ecosystems
As we consider what the next phase of channel engagement may look like, many believe that it will be based on intelligent ecosystems, using AI to continuously monitor and adjust the engagement strategies used to engage their channel partners, in real time and based on the behaviours of those partners.
For brands operating in complex distribution networks, the ability to perform well will be determined both by whether products are available to their customers, as well as by the enthusiasm, expertise, and loyalty shown from each channel partner that represents the brand each and every day that they are working on behalf of the brand.
While AI clearly does not eliminate the human aspect of a brand’s relationship with its channel partners, it does allow brands to better understand and nurture that relationship.
In markets where the last mile will determine whether a sale is made, how one leverages the intelligence gained by using AI will ultimately be the difference between gaining a new, sustainable competitive advantage versus losing one.
