Shifting from price hikes to persuasion, Coca-Cola’s latest strategy signals how AI is moving deeper into the core of corporate marketing.

Recent coverage of the company’s leadership discussions shows that Coca-Cola is entering what executives describe as a new phase focused on influence not pricing power. According to Mi-3, the company is changing its focus from “price to persuasion,” with digital platforms, AI, and in-store execution becoming increasingly important in building demand. This reflects a change in consumer brand behaviour as inflation pressures ease and companies seek new strategies to maintain revenue growth.

That means expanding the role of AI in Coca-Cola’s marketing production and decision-making. The company has already experimented with generative AI in creative campaigns and continues testing how automation can help with content creation, campaign planning, and distribution.

Industry analysis from The Current points out that Coca-Cola has been embedding AI into marketing workflows and scaling its use in creative production and campaign execution. These efforts include using AI tools to generate images, assist with storytelling, and adjust campaigns in channels.

Testing AI in the marketing pipeline

The week’s reporting suggests the company is now testing AI-driven systems that can help automate parts of the advertising process, including drafting scripts or preparing social media content. While these initiatives remain in testing not full rollout, they illustrate how large brands are moving toward more automated marketing pipelines. Instead of relying only on agencies or long creative cycles, companies are exploring ways to shorten the path from concept to campaign.

During the past two years, many consumer goods have firms relied on price increases to offset rising costs. As inflation slows in several markets, analysts say that strategy has limits. Growth increasingly depends on persuading consumers to buy more often or choose higher-margin products. AI offers a way to refine that persuasion at scale, using data to shape messages, target audiences, and adjust campaigns in near real time.

Coca-Cola’s approach fits a wider trend in marketing technology. Generative AI tools have quickly moved from experimental use to regular deployment in large enterprises. According to McKinsey’s 2024 global AI survey, about one-third of organisations already use generative AI in at least one business function, with marketing and sales among the most common areas of adoption. Analysts expect that share to keep rising as companies test automation in creative work and customer engagement.

AI moves upstream in enterprise strategy

What strikes out in Coca-Cola’s case is how the corporation frames AI not only as a cost-saving tool, but also as part of a broader operating shift. By focusing on persuasion, the company signals that AI’s value lies in shaping demand, not improving efficiency. That includes using AI to analyse consumer behaviour, tailor messaging to different markets, and support local teams with adaptable content.

The strategy also reflects a growing tension in the marketing sector. Automation can speed up production and test more campaign ideas, but it also raises questions about creative quality, brand consistency, and the role of human teams. Companies experimenting with AI-generated content must still ensure that messaging aligns with their brand identity and cultural context. For global brands like Coca-Cola, that challenge becomes more complex because campaigns frequently need to work in many regions.

Another factor shaping this transition is the rapid growth of digital advertising channels. As spending shifts toward social platforms, streaming services, and online retail media, the volume of content required has expanded. AI tools offer a way to produce many versions of ads, test different approaches, and adjust messaging based on performance data. This makes automation appealing not only for cost reasons, but also for speed and flexibility.

Coca-Cola’s move reflects a broader pattern: AI adoption is moving upstream in business processes. Early deployments frequently centred on analytics or internal automation. Companies are now applying AI in customer-facing functions like marketing strategy, creative development, and campaign management. That change suggests that AI is becoming part of how companies compete for market share, not how they reduce expenses.

The firm has not indicated that AI will replace creative teams or agencies. Instead, the current direction indicates a hybrid model in which automation handles repetitive or data-heavy tasks while human teams guide brand voice and campaign concepts. Many marketing leaders believe that this blended approach will define the next phase of AI adoption.

Coca-Cola’s emphasis on persuasion over pricing may impact how other consumer brands approach growth in a post-inflation environment. If AI can assist businesses in more precisely shaping demand, it may minimise reliance on price increases or mass-market campaigns.

(Photo by James Yarema)

See also: PepsiCo is using AI to rethink how factories are designed and updated

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The most rigorous international study of firm-level AI impact to date has landed, and its headline finding is more constructive than many expected. Across nearly 6,000 verified executives in four countries, AI has delivered modest aggregate shifts in productivity or employment over the past three years. The measured impact reflects the early phases of deployment rather than a failure of the technology.

The working paper [PDF], published by the National Bureau of Economic Research and produced by teams from the Federal Reserve Bank of Atlanta, the Bank of England, the Deutsche Bundesbank and Macquarie University, found that over 90% of firms report no measurable change headcount attributable to AI over the past three years. Given the short time horizon and the concentration of AI use in discrete functions, such incremental rather than transformative effects are consistent with how general purpose technologies have evolved historically.

Adoption of AI is widespread. Around 69% of firms are already using some form of AI, led by LLM-based text generation at 41%, data processing via machine learning at 28% and visual content creation at 29%. In the UK, firm-level adoption rose from 61% to 71% across 2025. AI tools are embedded in day-to-day workflows, and although measured impact at firm level often lags adoption, the trend is generally upwards.

The forward AI impact numbers indicate acceleration

Executives expect stronger effects to take place over the next three years. On average, they expect a 1.4% increase in productivity and a 0.8% rise in output. US executives project a 2.25% productivity gain, while UK firms expect 1.86%. In economies that have struggled with weak productivity growth for over a decade, gains of that magnitude are notable – incremental improvements, compounded across sectors, shift national outputs.

On the thorny subject of employment, executives expect a modest 0.7% reduction in headcount across the four countries over the same period. In the UK, around two-thirds of this adjustment is expected to come through slower hiring rather than outright redundancies. That pattern suggests a gradual reallocation of roles rather than abrupt terminations. As with previous waves of automation, aggregate figures do not capture job creation in adjacent roles, and in the case of AI, these might include roles around data governance, model oversight, prompt engineering, and AI-enabled service development, many of which would be new roles.

Interpreting the expectation gap

The study also compares executive expectations with those of workers. Researchers fielded parallel questions to US employees through the Survey of Working Arrangements and Attitudes. Employees expect AI to increase employment at their firms by 0.5% over the next three years, while US executives expect a 1.2% reduction. Employees foresee productivity gains of 0.92%, below the executive forecast of 2.25%.

This divergence reflects different vantage points. Executives observe cost structures and competitive pressure, while employees experience task-level augmentation and new capabilities. In practice, AI systems are often deployed to assist rather than replace, particularly in knowledge-intensive work. Evidence from controlled trials, including large language model use in customer support and professional services, shows productivity gains concentrated among less experienced staff, with quality improvements appearing alongside better output figures. Where communication and training are clear, adoption tends to proceed with limited resistance.

Why this AI impact data merits attention

Survey design influences inferences from any statistics, and in this particular case, the researchers noted variation between their own figures and those from, for example, a McKinsey survey taken in the same period that put adoption at 88% of organisations (the survey in question here pegs the figure at just 69%). On the other hand, the US Census Business Trends and Outlook Survey, which draws on a broader respondent base, estimated AI use at around 9% in early 2024, rising to 18% by December 2025. This gap reflects differences in sampling, question framing and respondent seniority. Executive surveys tend to capture intent and enterprise-level deployments, while broader business surveys may reflect narrower definitions of AI or earlier stages of implementation.

In the study in question, respondents were phone-verified, unpaid, and predominantly CEOs and CFOs, with over 90% drawn from the UK and Germany. The data was cross-checked against ten years of macro output and employment figures from national statistics agencies.

The inflection point executives anticipate may unfold over the next three years as deployments mature and integration improves, in the way that many new technologies have emerged into the workplace until they become everyday tools. The central question is less whether AI will affect productivity and employment, and more how quickly organisations can change the technology’s wider adoption into measurable economic gains.

See also: OpenAI’s enterprise push: The hidden story behind AI’s sales race

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The adoption of AI for enterprise treasury management enables businesses to abandon manual spreadsheets for automated data pipelines.

Corporate finance departments face pressure from market volatility, regulatory demands, and digital finance requirements. Ashish Kumar, head of Infosys Oracle Sales for North America, and CM Grover, CEO of IBS FinTech, recently discussed the realities of corporate treasuries.

IBS FinTech has operated for 19 years and currently ranks in the top five globally according to an IDC report. Grover notes that while AI-powered automation has reached many areas of corporate life, treasury departments often still rely on manual spreadsheets.

“IBS FinTech has identified the gap in the CFO’s office in corporations where they are managing their most critical information system, that is, treasury management on Excel,” Grover said.

Treasury teams manage cash, liquidity, and risk. Companies face foreign currency risk through imports and exports, alongside related commodity risks. Cash surplus companies also need to invest in operations to generate returns.

The key problem for many enterprises is a lack of real-time data connection. Teams often execute trades on platforms like Bloomberg, Reuters, or 360D, manually enter the data into spreadsheets, and then post accounting entries into an enterprise resource planning system.

Successfully implementing AI in enterprise treasury management

AI implementations in finance depend on resolving these manual bottlenecks. Enterprise leaders often view the technology as a fast solution, but the technology requires digitised and automated data as a foundation.

“It is not by talking you can do AI in treasury,” Grover said. “You have to create that underlying data set that has to be digitised and automated.”

Integrating treasury management systems with existing enterprise resource planning platforms allows companies to establish this data foundation. IBS FinTech built its backend on Oracle databases from its inception and now integrates with Oracle Cloud, NetSuite, and Fusion.

A connected ecosystem requires the treasury management system to communicate directly with the enterprise resource planning platform, trading platforms, and banks. This integration provides executives with accurate information to manage liquidity, mitigate risk, and monitor compliance violations across the system.

Grover expects global volatility to increase due to geopolitical and economic factors impacting commodities, equities, and foreign exchange. Executives must prioritise automation and real-time information systems to operate in this uncertain environment.

Kumar noted that modernising treasury management with AI and connecting it to enterprise resource planning systems builds financial resilience. Enterprise leaders should audit their existing data workflows. If a finance team relies on manual entry between a trading platform and an enterprise resource planning platform, AI initiatives will fail due to poor data quality.

Implementing direct integrations ensures data flows in real time without error, providing the necessary baseline for future technology deployment.

See also: DBS pilots system that lets AI agents make payments for customers

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.