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.

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Following a three-month beta programme, First Insight has made its new AI tool, Ellis, available to brands and retailers. Ellis is designed as a conversational interface that allows merchandising, pricing and planning teams to ask questions about products, pricing, and demand in the First Insight platform. The company says its approach is intended to compress decision times into minutes.

Research by McKinsey has found that while most large retailers now collect volumes of customer data, some can’t translate insights into action quickly enough to influence product development decisions. It notes AI tools which shorten the distance between insight and execution are more likely to deliver measurable commercial value than reporting systems.

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For marketing leaders managing seven-figure budgets in media environments, the implications are practical not theoretical, implying faster decision cycles and a re-balance of human effort to strategy and differentiation.

Programmatic advertising promises efficiency, but in practice accumulates operational complexity. Campaigns span formats, devices, data partnerships, and regulatory constraints, which make manual optimisation problematic. PubMatic is positioning AgenticOS as a response to such pressure, presenting it as an ‘operating system’ that allows multiple AI agents to transact and optimise campaigns inside human-defined objectives, and with what company-defined guardrails.

AgenticOS acts across infrastructure and applications to coordinate decisions. This aligns with current research trends showing that agentic systems outperform single-model automation in contexts where campaign tasks trade-off cost, performance, and risk analysis that are inherent in media buying.

Cost reduction through operational compression

For medium to large organisations, marketing cost rises are driven by operational overhead rather than media prices. PubMatic reports early tests where agent-led campaigns reduced setup time by 87% and issue resolution by 70%. Even allowing for bias, these figures are consistent with studies of AI-assisted workflow automation in enterprise marketing. Typically, these find 30–50% reductions in manual labour in planning and reporting.

The near-term opportunity for budget holders is not headcount reduction necessarily, but capacity gains. Agentic systems absorb decision load—bid adjustments, pacing changes, and inventory discovery. This lets teams run more campaigns concurrently or redirect effort to activities like experimentation and testing.

Decision quality at scale

AgenticOS’s claim is that it enables continuous decision-making without fragmentation, significant as most marketing inefficiency arises from delayed or inconsistent execution, not poor strategy. Human teams operate in reporting cycles, while agentic systems operate in seconds.

Research into real-time optimisation suggests marginal gains at auction level can compound with large spends. At enterprise level, even low single-digit percentage improvements in effective CPM or conversion efficiency translate can have budgetary impact. Agentic AI does not eliminate the need for human judgement, but changes where and when judgement is made. Instead of reactive troubleshooting, teams define objectives, constraints, and success goal definitions.

Governance, control, and brand safety

A persistent concern among senior marketers is loss of control to agentic processes. PubMatic states AgenticOS works from advertisers’ objectives, brand-safety rules, and creative parameters, with agents operating inside those boundaries. This reflects a wider industry consensus that agentic AI adoption will only scale where governance is embedded at system level rather than bolted on.

For decision-makers, the practical lesson is to invest early in codifying marketing intent, detailing performance hierarchies, set brand constraints, and escalation thresholds. Organisations that treat agentic AI as a strategic execution layer, rather than a black box, are likely to realise benefits faster and with lower risk.

Predictions for the next 24 months

Evidence from adjacent enterprise functions such as supply chain, finance, and customer support suggest three likely developments:

First, agentic AI will become a standard execution layer in programmatic advertising, with a shift from automation to high-quality intent modelling and agent coordination.

Second, marketing operating models will flatten, with smaller teams managing large, more complex portfolios. Senior marketers will spending more time on scenario planning and less on day-to-day campaign mechanics.

Third, vendors offering system-level agentic platforms (not isolated point solutions) will be able to deliver ROI, as cost savings and performance gains compound across the workflow rather than at isolated points.

Practical advice for marketing leaders

Marketing decision-makers could regard AgenticOS and similar platforms as infrastructure investment. Pilot programmes should focus on high-volume, rules-based campaigns where efficiency gains are easier to measure. Success can be evaluated on performance metrics and time saved.

Most importantly, internal preparation is of paramount importance. The more precisely objectives and constraints are defined, the more effectively autonomous systems will operate. In this sense, the adoption of agentic AI is as much an organisational discipline challenge than a technological one.

PubMatic’s AgenticOS illustrates agentic AI in marketing entering operational phases. The question is how quickly organisations can adapt their processes to take advantage of the technology. Those that do are likely to see lower costs and more effective use of marketing spend in increasingly complex media environments.

(Image source: “market” by star-one is licensed under CC BY-SA 2.0. )

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.

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That pressure is pushing some large companies to test where AI fits inside everyday marketing work. At L’Oréal, AI-generated creative tools are being used to support parts of the digital advertising process, particularly video and visual content. The aim is not to replace human teams, but to reduce friction in a system that demands constant refresh.

The shift offers a useful view into how enterprise AI adoption is unfolding in creative functions, where speed and control matter as much as originality.

Scaling content without scaling production

For a global beauty group, digital advertising is no longer a seasonal exercise. Content is needed continuously across social platforms, ecommerce sites, and regional campaigns, often with small variations in language, format, or visual emphasis.

Traditional production models struggle to keep up. Each new asset typically involves planning, filming, editing, and approvals. AI-generated images and video elements allow you to reuse old content and extend it into new formats without having to start from scratch every time.

At L’Oréal, AI tools are being used to help generate or adapt visual content that fits specific digital channels. This includes polishing footage, modifying formats, and creating versions for different platforms. Human teams continue to monitor creative direction and final output, but AI speeds up the time between idea and delivery.

The practical value is not about producing something altogether new. It is about producing enough usable content to meet the pace of digital advertising.

Why L’Oréal keeps AI under tight creative control

One reason large brands move cautiously with AI in creative work is brand risk. Visual identity, tone, and messaging are tightly regulated, and small inconsistencies can be amplified when content is distributed at scale.

Rather than handing over creative decisions, companies like L’Oréal are using AI as a support layer. AI-generated output is examined, adjusted, and approved using existing workflows. This keeps accountability with internal teams and external agencies, while still gaining efficiency.

This approach reflects a broader pattern in enterprise AI adoption. Tools are being introduced into workflows that already exist, rather than reshaping how decisions are made. In marketing, that often means AI assists with production, not with defining brand voice.

Cost, speed, and repeatability

Digital advertising budgets are under pressure, even for large consumer groups. Media prices fluctuate, platforms change their restrictions, and audiences expect constant updates. AI offers a way to absorb some of that pressure by lowering the marginal cost of producing additional assets.

By reusing footage and applying AI-based enhancements, brands can stretch the value of each shoot. This is especially important in areas where campaigns must be quickly changed, or when local teams want specific assets but lack full-scale production support.

The result is not a dramatic cost cut in one area, but incremental savings across hundreds of minor decisions. Over time, those savings shape how marketing teams plan campaigns and allocate expenditures.

What this says about enterprise AI maturity

L’Oréal’s use of AI-generated creative work is less about experimentation and more about operational fit. The tools are used in situations where output is predictable, quality can be measured, and mistakes may be caught before release.

This mirrors how AI is being adopted across many enterprise functions. Instead of broad, open-ended use, companies are identifying narrow tasks where AI can reliably assist without introducing new risk. In marketing, those tasks often sit between creative concept and final distribution.

The approach also emphasises a key constraint. AI works best in environments with existing data, rules, and review processes. Creative freedom still belongs to people, while AI supports scale.

Implications for marketing teams

For marketing leaders, the lesson is not that AI will replace agencies or internal creatives. It is that production models built for slower cycles are becoming harder to sustain.

Teams are being asked to deliver more content, more often, with tighter budgets and faster turnaround. AI tools offer one way to manage that demand, but only if they fit existing controls and expectations.

This places new demands on governance. Marketing teams need clear rules on where AI can be used, how outputs are reviewed, and who remains accountable for final decisions. Without that structure, efficiency gains can quickly be offset by risk.

What L’Oréal’s approach signals for enterprise AI adoption

What stands out in L’Oréal’s approach is restraint. AI is applied where it reduces friction, not where it reshapes the role of creative teams. That makes it easier to integrate into large organisations with established processes and brand safeguards.

As more enterprises look to AI for productivity gains, similar patterns are emerging. AI becomes part of the workflow, not the headline. Success is measured in time saved and consistency maintained, not in novelty.

For now, AI-generated creative work remains a supporting act in enterprise marketing. Its real impact lies in how quietly it changes the economics of content production, one asset at a time.

(Photo by Helio E. López Vega)

See also: Disney is embedding generative AI into its operating model

Want to learn more about AI and big data from industry leaders? Check outAI & 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, click here for more information.

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Here, we’re talking about a focus on the practical constraints that determine whether AI changes daily work or merely adds another layer of complexity or tooling.

Brand accuracy a repeatable capability

Marketing agencies’ AI treats brand accuracy as something to be engineered. WPP and Stability AI note that off-the-shelf models “don’t come trained on your brand’s visual identity”, so outputs can often look generic. The companies’ remedy is fine-tuning, that is, training models on brand-specific datasets so the model learns the brand playbook, including style, look, and colours. Then, these elements can be reproduced consistently.

WPP’s Argos is a prime example. After fine-tuning a model for the retailer, the team described how the model picked up details beyond the characters, including lighting and subtle shadows used in the brand’s 3D animations. Reproducing these finer details can be where time disappears in production, in the form of re-rendering and several rounds of approvals. When AI outputs start closer to “finished”, teams spend less time correcting and more time shaping narratives and adapting media for different channels.

Cycle time collapses (and calendars change)

WPP and Stability AI point out that traditional 3D animation can be too slow for reactive marketing. After all, cultural moments demand immediate content, not cycles defined in weeks or months. In its Argos case study, WPP trained custom models on two 3D toy characters so the models learned how they look and behave, including details such as proportions and how characters hold objects.

The outcome was “high-quality images…generated in minutes instead of months”.

The accelerated workflow moves rather than removes production bottlenecks. If generating variations becomes fast, then review, compliance, rights management and distribution, become the constraints. Those issues were always there, but the speed and efficiency of AI in this context shows the difference between what’s possible, and systems that have become embedded and accepted into workflows. Agencies that want AI to change daily operations have to redesign the workflow around it, not just add the technology as a new tool.

The “AI front end” becomes essential

WPP and Stability AI call out a “UI problem”, wherecreative teams lose time interfaces to common tools are “disconnected, complex and confusing”, forcing workarounds and constant asset movement between tools. Often, responses are bespoke, brand-specific front ends with complex workflows in the back end..

WPP positions WPP Open as a platform that encodes WPP’s proprietary knowledge into “globally accessible AI agents”, which helps teams plan, produce, create media, and sell. Operational gains come from cleaner handoffs between tools, as work moves from briefs into production, assets into activation, and performance signals back into planning.

Self-serve capability changes agency operations

AI-powered marketing platforms are also becoming client-facing. Operationally, that pushes agencies to concentrate on the parts of the workflow their clients can’t self-serve easily, like designing the brand system, building fine-tunings, and ensuring governance is embedded.

Governance moves from policy to workflow

For AI to be used daily, governance needs to be embedded where work happens. Dentsu describes building “walled gardens”, which are digital spaces where employees can prototype and develop AI-enabled solutions securely, and commercialise the best ideas. This reduces the risk of sensitive data exposure and lets experiments move into production systems.

Planning and insight compress too

The operational impact is not limited to production. Publicis Sapient describes AI-powered content strategy and planning that “transforms months of research into minutes of insight” by combining large language models with contextual knowledge and prompt libraries [PDF]. Research and brief development compress work schedules, so more client work can happen and the agency has faster responses to shifting culture and platform algorithms.

What changes for people

Across these examples, the impact on marketing professionals is one of rebalancing and shifting job descriptions. Less time goes on mechanical drafting, resizing, and versioning, and more time goes on brand stewardship. New operational roles expand, with titles like– model trainer, workflow designer, and AI governance lead.

AI makes the biggest operational difference when agencies use customised models, usable front ends that make adoption (especially by clients) frictionless, and integrated platforms that connect planning, production, and execution.

The headline benefit is speed and scale, but the deeper change is that marketing delivery starts to resemble a software-enabled supply chain, standardised, flexible where it needs to be, and measurable.

(Image source: “Solar Wind Workhorse Marks 20 Years of Science Discoveries” by NASA Goddard Photo and Video is licensed under CC BY 2.0.)

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.

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Recent reporting shows the retailer using AI to generate new images of real models wearing different outfits, based on existing photoshoots. Models remain involved in the process, including consent and compensation, but AI is used to extend and adapt imagery without repeating production from scratch. The stated aim is to speed up content creation and reduce the need for repeated shoots.

On the surface, the change looks incremental. In practice, it reflects a familiar pattern in enterprise AI adoption, where technology is introduced not to overhaul how a business works, but to remove friction from tasks that repeat at scale.

How Zara uses AI to reduce friction in repeatable retail work

For a global retailer like Zara, imagery is not a creative afterthought. It is a production requirement tied directly to how quickly products can be launched, refreshed, and sold across markets. Each item typically needs multiple visual variations for different regions, digital channels, and campaign cycles. Even when garments change only slightly, the surrounding production work often starts again from scratch.

That repetition creates delays and cost that are easy to overlook precisely because they are routine. AI offers a way to compress those cycles by reusing approved material and generating variations without resetting the entire process.

AI enters the production pipeline

The placement of the technology is as important as the capability itself. Zara is not positioning AI as a separate creative product or asking teams to adopt an entirely new workflow. The tools are being used inside an existing production pipeline, supporting the same outputs with fewer handoffs. That keeps the focus on throughput and coordination rather than experimentation.

This kind of deployment is typical once AI moves beyond pilot stages. Rather than asking organisations to rethink how work is done, the technology is introduced where constraints already exist. The question becomes whether teams can move faster and with less duplication, not whether AI can replace human judgement.

The imagery initiative also sits alongside a broader set of data-driven systems that Zara has built up over time. The retailer has long relied on analytics and machine learning to forecast demand, allocate inventory, and respond quickly to changes in customer behaviour. Those systems depend on fast feedback loops between what customers see, what they buy, and how stock moves through the network.

From that perspective, faster content production supports the wider operation even if it is not framed as a strategic shift. When product imagery can be updated or localised more quickly, it reduces lag between physical inventory, online presentation, and customer response. Each improvement is small, but together they help maintain the pace that fast fashion relies on.

From experimentation to routine use

Notably, the company has avoided framing this move in grand terms. There are no published figures on cost savings or productivity gains, and no claims that AI is transforming the creative function. The scope remains narrow and operational, which limits both risk and expectation.

That restraint is often a sign that AI has moved out of experimentation and into routine use. Once technology becomes part of day-to-day operations, organisations tend to talk about it less, not more. It stops being an innovation story and starts being treated as infrastructure.

There are also constraints that remain visible. The process still relies on human models and creative oversight, and there is no suggestion that AI-generated imagery operates independently. Quality control, brand consistency, and ethical considerations continue to shape how the tools are applied. AI extends existing assets rather than generating content in isolation.

This is consistent with how enterprises typically approach creative automation. Rather than replacing subjective work outright, they target the repeatable components around it. Over time, those changes accumulate and reshape how teams allocate effort, even if the core roles remain intact.

Zara’s use of generative AI does not signal a reinvention of fashion retail. It shows how AI is beginning to touch parts of the organisation that were previously considered manual or difficult to standardise, without changing how the business fundamentally operates.

In large enterprises, that is often how AI adoption becomes durable. It does not arrive through sweeping strategy announcements or dramatic claims. It takes hold through small, practical changes that make everyday work move a little faster — until those changes become hard to imagine doing without.

(Photo by M. Rennim)

See also: Walmart’s AI strategy: Beyond the hype, what’s actually working

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, click here for more information.

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Businesses are always looking for new ways to improve their online presence. Link strategies are the key to boosting SEO rankings, ensuring websites get the visibility they need. With AI integration, businesses can achieve better results by using data-driven insights and automation. This guide explores Bazoom’s backlink building service, showing how it can change traditional methods and help companies prepare for the future.

How AI is reshaping link strategies

Traditional link strategies often relied on manual processes, like contacting potential partners and tracking backlinks. These methods, while useful, were labour-intensive and prone to error. AI addresses these issues by automating many aspects of link building. Machine learning algorithms allow AI to quickly analyse large data sets, identifying the most relevant linking opportunities for your business.

By using AI technologies, businesses can improve the accuracy of their backlink profiles. AI tools can predict which links will most impact search rankings based on historical data and current trends. The predictive ability helps businesses allocate resources more effectively and focus on high-value opportunities. Besides accuracy, AI saves time by automating repetitive tasks, allowing professionals to focus on strategy development.

AI-enhanced link strategies offer benefits beyond efficiency and accuracy. The technologies provide insights into competitive landscapes, helping businesses understand their position relative to competitors. By gaining a comprehensive market view, companies can make informed decisions about their link building efforts and adapt quickly to changes in search engine algorithms.

AI tools that enhance link strategies

Today, various AI tools significantly improve link building processes. Tools use machine learning to identify data patterns that may not be immediately apparent to humans. Features like automated outreach, relationship management, and real-time analytics streamline the process from start to finish.

AI tools offer more than automation. They provide actionable insights that help optimise link strategies for maximum impact. By analysing competitor links and industry trends, the tools offer strategic recommendations tailored to specific business needs. Integrating such technology enables tracking and reporting, providing visibility into each campaign’s effectiveness.

For businesses aiming to stay competitive in SEO, implementing advanced tools is essential. They simplify complex processes and ensure every decision is backed by solid data analysis. The ability to forecast potential outcomes allows companies to adjust their strategies proactively rather than reactively.

Practical uses of AI in link strategies

AI’s practical applications in link strategies are diverse. For example, businesses can automate their backlink initiatives by using tools that facilitate efficient outreach and engagement with potential partners. The automation ensures consistent and targeted communication without overwhelming resources.

AI’s impact on data analysis is significant; it transforms raw data into valuable insights that drive decision-making in SEO campaigns. By identifying patterns and predicting search engine trends, AI helps businesses optimise their link building efforts effectively. The capability ensures companies remain competitive in a constantly changing digital landscape.

Moreover, AI enables businesses to predict future industry trends accurately. By forecasting shifts in consumer behaviour or search engine algorithm changes, companies can adjust their strategies accordingly and maintain a competitive edge. The proactive approach reduces the risk associated with sudden changes in SEO dynamics.

Future trends in AI and link building practices

Several emerging trends suggest further integration between AI technologies and digital marketing efforts. One notable tendency is the increased use of natural language processing (NLP) in SEO tools, allowing for better understanding of contextually relevant content creation.

The potential for AI integration extends beyond improving existing systems; it offers opportunities for creating new marketing paradigms through collaborative approaches in various platforms like social media channels or content management systems. As these technologies continue to advance, expect more innovation in the future.

While predicting what’s around the corner can be challenging, one thing is clear: embracing technology advances will be crucial for success. Adopting cutting-edge solutions now ensures readiness for whatever comes next, and provides a foundation for sustainable growth in the long term.

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.

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Announced during a virtual press conference, the platform brings together Google’s Gemini models, first and third-party agents, and the core technology of what was formerly known as Google Agentspace to create a singular agentic platform. It aims to democratise the creation and use of AI-powered agents for automating complex workflows and boosting productivity across entire organisations.

Thomas Kurian, CEO of Google Cloud, introduced the new offering, explaining that as customers moved beyond simply building applications with AI, the company saw them “advancing to build agents”.

Gemini Enterprise is Google’s answer to this evolution, bundling its entire AI stack into a cohesive user experience that allows developers and business users alike to build agents with a no-code workbench.

The platform is built on six core components. The “brains” are Google’s powerful Gemini models, including the newly available Gemini 2.5 Flash Image. The “workbench” is the agent creation and orchestration technology pioneered with Agentspace, allowing any user to manage agents and automate processes. Finally, this is complemented by the “taskforce,” a suite of pre-built Google agents for specialised jobs like the new Code Assist Agent and the Deep Research Agent.

To make these agents effective, there is deep integration with a company’s data through new connectors for systems like Microsoft Teams, Salesforce, Box, Confluence, and Jira. Kurian explained the system’s intelligence, stating, “We remember who you are and what you do and use it to personalise the context you have when we work with a large language model”.

A central “governance” framework allows organisations to monitor, secure, and audit all agents from one place, with protections like Model Armor now built-in. Finally, the platform is built on an open “ecosystem” of over 100,000 partners.

Gemini Enterprise: A glimpse into the future of work

To demonstrate the platform’s capabilities, Maryam Gholami walked through a practical use case.

“The beauty of Gemini Enterprise is that it offers the familiar interface of the Gemini but built for enterprise workflows, including full control to enable or disable any of the sources as needed,” Gholami said.

Using a custom ‘campaigns agent’, she used four different agents to handle market research, media generation, team communications, and inventory management. The agent identified a market trend towards sci-fi themes, flagged a 25 percent inventory gap, created a purchase order in ServiceNow, drafted an email to store managers, and generated social media assets.

“Gemini Enterprise is more than just a chat interface,” Gholami concluded after the demonstration. “It’s an end-to-end AI system that unifies your data, your tools, and your teams, turning weeks of complex work into a single, streamlined conversation”.

Customers drive transformation with AI fleets

Proving the platform’s real-world value, Nirmal Saverimuttu, CEO of Virgin Voyages, shared his perspective that “any major disruption like AI requires a cultural transformation to be successful”.

Importantly, Saverimuttu stressed that AI’s role is to work alongside, not replace, his team.

“Our people are our biggest asset. AI. And never replace our people,” he stated. “To me, AI is about getting the best from our people. It’s about unleashing human potential”.

The cruise line has deployed a fleet of over 50 specialised AI agents company-wide. The first, ‘Email Ellie’, has boosted content production speed by 40 percent and contributed to a 28 percent year-over-year increase in July sales. Saverimuttu also noted welcome operational gains, including a “35 percent reduction in agency dependency costs, resulting in creative independence”.

Another early adopter is Macquarie Bank. The bank, one of Australia’s largest, has rolled out Gemini Enterprise to every employee and reports that 99 percent of its staff have already completed generative AI training.

Google emphasised that Gemini Enterprise is an open platform, with partners like Box, Salesforce, and ServiceNow announcing compatible agents. A new AI agent finder will also help customers discover thousands of validated partner solutions.

To support adoption, Google has also launched Google Skills, a new free learning platform with 3,000 courses. As part of this, the company announced the Gemini Enterprise Agent Ready (GEAR) program; an educational sprint designed to enable one million developers to build and deploy agents.

Pricing and availability of Gemini Enterprise

Gemini Enterprise is available globally in all countries where Google Cloud products are sold. Gemini Business, for small businesses, starts at $21 per seat per month, while Gemini Enterprise Standard and Plus editions for larger organisations start at $30 per seat per month.

For Kurian, the launch is about democratising powerful technology.

“Gemini Enterprise technology is really about reimagining a super powerful AI technology [for the workplace] but making it super easy to use and putting it in the hands of every company and every user in those companies,” Kurian concludes.

See also: AI value remains elusive despite soaring investment

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 Expo, click here for more information.

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According to SAP Emarsys – which took the pulse of over 10,000 consumers and 1,250 marketers – while businesses are seeing real benefits from AI, shoppers are becoming increasingly distrustful, especially when it comes to their personal data. This divide could easily unravel the personalised shopping experience that brands are working so hard to build.

The rush to bring AI into marketing has been fast and decisive. As Sara Richter, CMO at SAP Emarsys, puts it, “AI marketing is now fully in motion: it has transitioned from the theoretical to the practical as marketers welcome AI into their strategies and test possibilities.”

For businesses, the appeal is obvious. 71 percent of marketers say AI helps them launch campaigns faster, saving them over two hours on average for each one. This newfound efficiency is doing what we often hear AI is best at: freeing up teams from repetitive work. 72 percent report they can now focus on more creative and strategic tasks. 

The results are hitting the bottom line, too. 60 percent of marketers have seen customer engagement climb, and 58 percent report a boost in customer loyalty since bringing AI on board.

But shoppers are telling a different story. The report reveals a “personalisation gap,” where the efforts of marketers just aren’t hitting the mark. Even with heavy investment in AI-driven tailoring, 40 percent of consumers feel that brands just don’t get them as people—a huge jump from 25 percent last year. To make matters worse, 60 percent say the marketing emails they receive are mostly irrelevant.

Dig deeper, and you find a real crisis of confidence in how personal data is being handled for AI marketing. 63 percent of consumers globally don’t trust AI with their data, up from 44 percent in 2024. In the UK, it’s even more stark, with 76 percent of shoppers feeling uneasy.

This collapse in trust is happening just as new rules come into play. A year after the EU’s AI Act was introduced, more than a third (37%) of UK marketers have overhauled their approach to AI, with 44% stating their use of the technology has become more ethical.

This creates a tension that the whole industry is talking about: how to be responsible without killing innovation. While the AI Act provides a clearer rulebook, over a quarter (28%) of marketing professionals are worried that rigid regulations could stifle creativity.

As Dr Stefan Wenzell, Chief Product Officer at SAP Emarsys, says, “regulation must strike a balance – protecting consumers without slowing innovation. At SAP Emarsys, we believe responsible AI is about building trust through clarity, relevance, and smart data use.”

The message for retailers is loud and clear: prove your worth. People are happy to use AI when it actually helps them. Over half of shoppers agree that AI makes shopping easier (55%) and faster (53%), using it to find products, compare prices, or come up with gift ideas. The interest in helpful AI is there, but it has to come with a promise of transparency and privacy.

Some brands are getting this right by focusing on people, not just the technology. Sterling Doak, Head of Marketing at iconic guitar maker Gibson, says it’s about thinking differently.

“If I can find a utility [AI] that can help my staff think more strategically and creatively, that’s needed because we’re a very creative business at the core,” Doak explains. For Gibson, AI serves human creativity rather than just automating tasks.

It’s a similar story for Australian retailer City Beach, which used AI marketing to keep its customers coming back. Mike Cheng, the company’s Head of Digital, discovered AI was the ideal tool for spotting and winning back customers who were about to leave.

“AI was able to predict where people were churning or defecting at a 1:1 level, and this allowed us to send campaigns based on customers’ individual lifecycle,” Cheng notes. Their approach brought back 48 percent of those customers within three months.

What these success stories have in common is a focus on solving real problems for people. As retailers venture deeper into what SAP Emarsys calls the “Engagement Era,” the way forward is becoming clearer. Investment in AI isn’t slowing down—64 percent of marketers are planning to increase their spend next year.

The technology isn’t the problem; it’s how it’s being used. Retailers need to close the gap between what they’re doing and what their customers are feeling. That means going beyond basic personalisation to offer real value, being open about how data is used, and proving that sharing information leads to a better experience.

The AI revolution is here, but for it to truly succeed, marketing professionals need to remember the person on the other side of the screen.

See also: Google Vids gets AI avatars and image-to-video tools

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, click here for more information.

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

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Ever watched an AI-generated video and felt like something was missing? The visuals might be stunning, but they often have an eerie silence that breaks the spell. In the film industry, the sound that fills that silence – the rustle of leaves, the clap of thunder, the clink of a glass – is called Foley art, and it’s a painstaking craft performed by experts.

Matching that level of detail is a huge challenge for AI. For years, automated systems have struggled to create believable sounds for videos.

How is Tencent solving the AI-generated audio for video problem?

One of the biggest reasons video-to-audio (V2A) models often fell short in the sound department was what the researchers call “modality imbalance”. Essentially, the AI was listening more to the text prompts it was given than it was watching the actual video.

For instance, if you gave a model a video of a busy beach with people walking and seagulls flying, but the text prompt only said “the sound of ocean waves,” you’d likely just get the sound of waves. The AI would completely ignore the footsteps in the sand and the calls of the birds, making the scene feel lifeless.

On top of that, the quality of the audio was often subpar, and there simply wasn’t enough high-quality video with sound to train the models effectively.

Tencent’s Hunyuan team tackled these problems from three different angles:

1. Tencent realised the AI needed a better education, so they built a massive, 100,000-hour library of video, audio, and text descriptions for it to learn from. They created an automated pipeline that filtered out low-quality content from the internet, getting rid of clips with long silences or compressed, fuzzy audio, ensuring the AI learned from the best possible material.

2. They designed a smarter architecture for the AI. Think of it like teaching the model to properly multitask. The system first pays incredibly close attention to the visual-audio link to get the timing just right—like matching the thump of a footstep to the exact moment a shoe hits the pavement. Once it has that timing locked down, it then incorporates the text prompt to understand the overall mood and context of the scene. This dual approach ensures the specific details of the video are never overlooked.

3. To guarantee the sound was high-quality, they used a training strategy called Representation Alignment (REPA). This is like having an expert audio engineer constantly looking over the AI’s shoulder during its training. It compares the AI’s work to features from a pre-trained, professional-grade audio model to guide it towards producing cleaner, richer, and more stable sound.

The results speak sound for themselves

When Tencent tested Hunyuan Video-Foley against other leading AI models, the audio results were clear. It wasn’t just that the computer-based metrics were better; human listeners consistently rated its output as higher quality, better matched to the video, and more accurately timed.

Across the board, the AI delivered improvements in making the sound match the on-screen action, both in terms of content and timing. The results across multiple evaluation datasets support this:

Tencent’s work helps to close the gap between silent AI videos and an immersive viewing experience with quality audio. It’s bringing the magic of Foley art to the world of automated content creation, which could be a powerful capability for filmmakers, animators, and creators everywhere.

See also: Google Vids gets AI avatars and image-to-video tools

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, click here for more information.

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

The post Tencent Hunyuan Video-Foley brings lifelike audio to AI video appeared first on AI News.

Between wrestling with complicated software, finding someone willing to be on camera, and then spending hours editing out all the “ums” and “ahs,” video production often feels more trouble than it’s worth. Google is aiming to change that narrative with Vids.

So far, it seems to be finding its audience. Google announced that Vids has already rocketed past one million monthly active users, a clear sign that teams are crying out for simpler ways to bring their ideas to life with video.

Your photos now move, and avatars do the talking

Among the latest additions is the ability to turn static images into motion pictures. Imagine you’ve got a great photo of a new product but need something more engaging for a social media post or presentation. You can now upload that picture to Vids, type a quick prompt describing what you want to happen, and Google’s Veo AI will turn it into an eight-second animated clip, complete with sound. It’s a simple way to create eye-catching, brand-aligned content in minutes.

For anyone who dreads being on camera, the new AI avatars will be a welcome relief. This feature lets you produce a polished video without ever stepping in front of a lens. You write your script, choose from a selection of digital presenters, and the AI handles the delivery. It’s perfect for creating consistent training guides, product demos, or team updates without worrying about lighting, background noise, or re-recording twenty takes to get it right.

Google is also tackling the tedious task of editing. A new automatic transcript trimming tool listens to your recordings and, with a few clicks, snips out all the filler words and awkward silences. Speaking from plenty of experience, that will be a huge time-saver.

Building on this, the company confirmed that familiar tools from Google Meet – like noise cancellation, custom backgrounds, and appearance filters – are set to arrive next month. Google Vids will also soon support portrait and square formats, making it much easier to create content for different platforms.

Getting started with Google Vids

With these new tools, Google is trying to make video creation as routine as building a slide deck.

The company is broadening access to Google Vids, making it available to more Workspace customers on business and education plans. Better yet, a basic version of the Vids editor is now completely free for all consumers, offering a range of templates to help you create anything from a tutorial to a party invitation.

To get everyone up to speed, Google has also launched a new “Vids on Vids” instructional series. The video guides walk you through the entire process, demonstrates the best features, and offers practical tips to help you create professional-looking content quickly.

Real businesses are seeing the benefit

Companies are already putting Vids to work. At Mercer International, a global manufacturing firm, it’s being used for employee safety training.

Alistair Skey, CIO of Mercer International, said: “Google Vids has given us the ability to create safety content, developed and curated by our organisation rather than having to go to market to hire very expensive resources to produce that for us.”

It’s also a story of speed and scale. Forest Donovan from the data platform Fullstory was impressed by the efficiency gains. “The amount of [high gloss] content we can create in a matter of hours versus what would normally take weeks has been astounding,” he said.

By embedding these powerful yet simple AI tools directly into its Workspace suite, Google is making the case that video is no longer the exclusive domain of specialist creative teams. It’s becoming a fundamental tool for everyday communication, and these updates just made it accessible to everyone.

See also: Google Cloud unveils AI ally for security teams

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, click here for more information.

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

The post Google Vids gets AI avatars and image-to-video tools appeared first on AI News.

However, for many consumer brands, the divide between expectations and reality looms large. Marketers envisioning a seamless, magical customer experience must recognise that AI’s effectiveness depends on high-quality underlying data. Without that, the AI falls flat, leaving marketers grappling with a less-than-magical reality.

AI-powered marketing fail

Let’s take a closer look at what AI-powered marketing with poor data quality could look like. Say I’m a customer of a general sports apparel and outdoor store, and I’m planning for my upcoming annual winter ski trip. I’m excited to use the personal shopper AI to give me an experience that’s easy and customised to me.

I need to fill in some gaps in my ski wardrobe, so I ask the personal shopper AI to suggest some items to purchase. But the AI is creating its responses based on data about me that’s been scattered across the brand’s multiple systems. Without a clear picture of who I am, it asks me for some basic information that it should already know. Slightly annoying… I’m used to entering my info when I shop online, but I was hoping the AI upgrade to the experience would make things easier for me. 

Because my data is so disconnected, the AI concierge only has an order associated with my name from two years ago, which was actually a gift. Without a full picture of me, this personal shopper AI is unable to generate accurate insights and ends up sharing recommendations that aren’t helpful.

Ultimately this subpar experience makes me less excited about purchasing from this brand, and I decide to go elsewhere. 

The culprit behind a disconnected and impersonal generative AI experience is data quality — poor data quality = poor customer experience. 

AI-powered marketing for the win

Now, let’s revisit this outdoor sports retailer scenario, but imagine that the personal shopper AI is powered by accurate, unified data that has a complete history of my interactions with the brand from first purchase to last return. 

I enter my first question, and I get a super-personalised and friendly response, already starting to create the experience of a one-on-one connection with a helpful sales associate. It automatically references my shopping history and connects my past purchases to my current shopping needs. 

Based on my prompts and responses, the concierge provides a tailored set of recommendations to fill in my ski wardrobe along with direct links to purchase. The AI is then able to generate sophisticated insights about me as a customer and even make predictions about the types of products I might want to buy based on my past purchases, driving up the likelihood of me purchasing and potentially even expanding my basket to buy additional items. 

Within the experience, I am able to actually use the concierge to order without having to navigate elsewhere. I also know my returns or any future purchases will be incorporated into my profile. 

Because it knew my history and preferences, Generative AI was able to create a buying experience for me that was super personalised and convenient. This is a brand I will keep returning to for future purchases.

In other words, when it comes to AI for marketing, better data = better results.

So how do you actually address the data quality challenge? And what could that look like in this new world of AI?

Solving the data quality problem

The critical first element to powering an effective AI strategy is a unified customer data foundation. The tricky part is that accurately unifying customer data is hard due to its scale and complexity — most consumers have at least two email addresses, have moved over eleven times in their lifetimes and use an average of five channels (or if they are millennials or Gen Z, it’s actually twelve channels).

Many familiar approaches to unifying customer data are rules-based and use deterministic/fuzzy matching, but these methods are rigid and break down when data doesn’t match perfectly. This, in turn, creates an inaccurate customer profile that can actually miss a huge portion of a customer’s lifetime history with the brand and not account for recent purchases or changes of contact information. 

A better way to build a unified data foundation actually involves using AI models (a different flavour of AI than generative AI for marketing) to find the connections between data points to tell if they belong to the same person with the same nuance and flexibility of a human but at massive scale. 

When your customer data tools can use AI to unify every touchpoint in the customer journey from first interaction to last purchase and beyond (loyalty, email, website data, etc…), the result is a comprehensive customer profile that tells you who your customers are and how they interact with your brand. 

How data quality in generative AI drives growth

For the most part, marketers have access to the same set of generative AI tools, therefore, the fuel you input will become your differentiator. 

Data quality to power AI provides benefits in three areas: 

• Customer experiences that stand out — more personalised, creative offers, better customer service interactions, a smoother end-to-end experience, etc.
• Operational efficiency gains for your teams — faster time to market, less manual intervention, better ROI on campaigns, etc.
• Reduced compute costs — better-informed AI doesn’t need to go back and forth with the user, which saves on racking up API calls that quickly get expensive

As generative AI tools for marketing continue to evolve, they bring the promise of getting back to the level of one-to-one personalisation that customers would expect in their favourite stores, but now at a massive scale. That won’t happen on its own, though — brands need to provide AI tools with accurate customer data to bring the AI magic to life.

The dos and don’ts of AI in marketing

AI is a helpful sidekick to many industries, especially marketing — as long as it’s leveraged appropriately. Here’s a quick ‘cheat-sheet’ to help marketers on their GenAI journey:

Do:

• Be explicit about the specific use cases where you plan to use data and AI and specify the expected outcomes. What results do you expect to achieve?
  
• Carefully evaluate if Gen AI is the most appropriate tool for your specific use case.
  
• Prioritise data quality and comprehensiveness — establishing a unified customer data foundation is essential for an effective AI strategy.

Don’t:

• Rush to implement GenAI across all areas. Start with a manageable, human-in-the-loop use case, such as generating subject lines.

(Editor’s note: This article is sponsored by Amperity)

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