{{item.videoDuration}}
{{item.title}}
{{item.text}}
{{item.videoDuration}}
{{item.title}}
{{item.text}}
Next in consumer markets 2025
Evolving AI from experimentation to excellence
The consumer markets industry stands at a defining moment as artificial intelligence (AI) transitions from isolated deployments to the cornerstone of enterprise-wide transformation. Moving beyond incremental AI experiments, companies are reimagining their operations and processes through an AI-first lens as they accelerate efforts to scale before falling behind. With over two-thirds of consumer markets leaders (67%) indicating that their ability to leverage generative AI is what’s driving an expected increase in their company’s cloud budget within the next planning cycle, the question has evolved from whether to adopt AI to how quickly organizations can scale AI across operations.
This shift comes as the industry faces mounting pressure to deliver personalized customer experiences while maintaining operational efficiency in a fast-changing and multifaceted market environment. Industry leading companies are reinventing their core functions with intelligent systems from dynamic pricing, supply chain and inventory management optimization to M&A strategy and cybersecurity, all while maintaining robust data privacy practices that help build customer trust. The technology introduces new levels of organizational agility by delegating programmatic tasks to autonomous AI agents while freeing up employees’ time to focus on what matters most: understanding and serving their customers. With AI handling routine operations like order processing and basic customer queries, store associates and managers can spend more time building relationships and providing personalized service.
As we look ahead, the landscape is shifting from isolated AI use cases to holistic AI-enabled business models that blend digital and physical touchpoints into seamless experiences. LMC research shows that top performing companies across industries are twice as likely to have already adopted an AI-specific operating model and developed GenAI-based products and services. Success in 2025 will likely belong to organizations that excel across three key dimensions.
- Leveraging AI to anticipate generational needs, personalize experiences and foster genuine customer loyalty.
- Balancing innovation with responsible deployment while turning privacy requirements into opportunities for trust.
- Driving enterprise-wide human-AI collaboration where technology enhances rather than replaces human capabilities across both customer-facing and back-office operational functions.
The most profound implementations will be those where AI listens, understands and helps retail teams become more attuned to their customers, not less — thereby defining retail excellence in the years ahead.
Pivotal trends in 2025 will fall into these categories:
Digital value acceleration: Reimagining organizational agility with AI
As 60% of consumer markets companies plan to increase cloud investments to leverage generative AI in their next planning cycle, digital value acceleration through AI is quickly becoming the key differentiator for market leaders. This sweeping change is turning traditional value chains into dynamic AI-driven networks connecting commercial operations with back-office processes.
- In pricing and promotion, for example, AI adds granularity to decision-making, moving beyond blanket promotional strategies to data-driven targeted approaches that consider price elasticity, customer sentiment and geographic nuances.
- Similarly, approaches to shelf space optimization and inventory management are being redrawn using AI-enabled analytics that can help predict fast-moving versus slow-moving goods and position products for better return.
Another significant impact of AI integration is taking place in shared services and corporate functions, where cost reductions of up to 50% have been realized, along with improved output quality and experiences. But success here takes more than just implementing AI technology — it requires in-depth business model reinvention and operational simplification. Many organizations have powerful AI algorithms that remain underutilized due to challenges in scaling and practical adoption. To succeed, transformations should extend across the value chain, from back-office functions to customer-facing operations.
- This involves collaborative orchestration between AI agents and humans, enhancing and streamlining agentic workflows.
- For example, a prominent pharmacy chain realized significant savings — into the nine figures — by digitizing consumer pharmacy interactions.
- Similar opportunities for streamlining and cost reduction are present in other codified processes. The objective is to reimagine processes with AI fundamentally integrated and managed under human oversight to improve productivity. Mere insertion of AI into sporadic steps of existing processes and automating individual tasks limits potential gains.
Industry leading consumer markets companies are keeping human insight at the forefront of crucial decisions like pricing, trade, promotions and markdowns, while advancing automation in areas such as HR and finance. To manage these changes effectively, they are focusing on strengthening their workforce training programs. Balancing digital tools with human knowledge is key for these companies to fully benefit from AI in a practical and sustainable manner as they approach 2025.
AI-focused strategies for digital value acceleration across key CM sectors in 2025
AI-driven M&A: Bridging capability gaps and aiding successful integrations
AI’s role in M&A is expanding from proven use cases like contract analysis to now becoming a central driver of deal strategy and value creation. Thirty-six percent of consumer markets companies say they’re already adopting generative AI in many parts of the front office, and 32% say they’re adopting it across all areas. In 2025, AI will fundamentally reshape how companies make portfolio decisions and execute transactions.
This transformation extends beyond traditional due diligence automation, with many companies now doubling down on data modernization and combining AI with proprietary datasets to bring additional rigor to evaluating which businesses to buy, sell or keep. Simultaneously, some companies are using machine learning to analyze earnings call patterns, anticipate analyst questions and shape compelling transaction narratives to share with investors.
For one client, we developed an intuitive GenAI-powered earnings process that streamlines content creations and generates real-time talking points during earnings calls, while also analyzing sentiment and summarizing peer insights. These are capabilities that can be successfully scaled in a matter of weeks.
The impact is particularly visible in many large-scale integrations, where AI is beginning to revolutionize merger integration planning.
- Companies can use AI to help unlock value by analyzing portfolio overlap, standardizing profitability and other golden metrics, consolidating supplier terms across merged organizations and accelerating process and system integration.
- As one example, when dealing with divergent data structures between merging entities, AI enables rapid matching and analysis to identify cost synergies through digital decoupling of data from source and transactional systems.
- In retail mergers, many companies are embedding AI across functions to gain more control over brand experience, customer relationships and supply chains, increasingly resembling vertically integrated brands over traditional department store models.
- Consumer goods companies seeking growth are leveraging AI to target acquisitions that boost innovation and digital capabilities. At the same time, dealmakers should apply healthy skepticism, rigorously evaluating algorithm scalability, data uniqueness and obsolescence risks to maintain strategic alignment and avoid overpaying for limited-value assets.
- Meanwhile, divestitures are becoming AI transformation hubs, where carved-out companies build AI-native operations instead of modernizing outdated processes inherited from the parent entities.
“Consumer companies that proactively manage their portfolios and capitalize on opportunities for both short-term growth and long-term reinvention are more likely to thrive, with market rewards for successful divestitures and strategic acquisitions.”
AI-driven M&A opportunities across key CM sectors in 2025
Enhancing cyber resilience with AI-driven security strategies
The conflict between AI-enabled security systems and threats is intensifying within consumer markets. With fraudsters increasingly adopting AI, complex attacks are expected to rise in 2025. This includes deep fakes disrupting customer service and counterfeit video conferences targeting corporate payment systems. The traditional response of deploying more AI for defense won’t be enough. Retailers and consumer products companies should rethink their security architecture and embed Responsible AI into their AI strategy from the outset to unlock value and manage the risks of their AI investments.
Industry leading retailers are listening to consumers and recognizing that cybersecurity is no longer just a technical requirement but a competitive differentiator. A crucial factor, however, lies in moving beyond mere regulatory compliance to build digital trust.
- This calls for transparent AI governance frameworks designed that match the specificity of each use case, backed by risk-based controls and mechanisms that give customers clear choices about how their data is used in AI training.
- Critical to this Responsible AI approach is a structured risk-tiering system that categorizes AI initiatives based on their potential impact and exposure levels, thereby aligning them with frameworks like the EU's UAI Act and NIST AI guidelines. This creates a systematic evaluation process that considers a range of factors — from data sensitivity to regulatory requirements and deployment context — to achieve the optimal balance between speed of innovation and safety.
Industry leaders are likely to be companies that master the sandbox approach and create protected environments for AI experimentation while maintaining enterprise-grade security protocols.
- This means developing cross-functional teams that bring together CISOs, risk managers and business leaders to evaluate use cases through both innovation and security lenses.
- Organizations need to strengthen their third-party risk management, particularly as AI systems become more interconnected with cloud providers and vendors. This will mean prioritizing AI-powered cloud architectures and potentially resetting cloud provider relationships and expectations.
- Leading companies are also considering the benefits of periodic audits by internal audit teams or third-party reviewers. This helps assure stakeholders of the security and effectiveness of AI solutions and reinforce the trust customers place in their brands. The goal is rapid innovation that maintains the trust equity brands have with their customers.
AI-driven security strategies across key CM sectors in 2025
NextGen customer experience: Combining digital innovation with GenZ expectations
As Gen Z consumers reshape retail expectations, increasingly viewing their data as currency for hyper-personalized experiences and frictionless shopping, brands and retailers face mounting pressure to deliver seamless engagement across all touchpoints. While retailers are building sophisticated omnichannel ecosystems powered by enterprise platforms like Adobe Experience Cloud and Salesforce Commerce Cloud, leading CPG brands are moving quickly to develop D2C channels to build direct customer relationships and capture first-party data. Notably, pure-play e-commerce players are expected to outpace both traditional retailers and large CPG producers in the years ahead. Through subscription services and branded digital marketplaces, these platforms excel at integrating real-time intelligence across digital and physical channels that create cohesive engagements that anticipate customer signals while feeling natural and unforced.
- Adobe's Real-Time CDP combined with Journey Optimizer, for example, lets retailers instantly recognize customers across channels and dynamically adjust content, product recommendations and messaging based on unified customer profiles and real-time shopping patterns.
- Similarly, platforms like Salesforce’s Social Studio — used by leading beauty retailers to track tens of thousands of daily consumer conversations — show how AI is transforming customer intelligence and engagement.
- These systems proactively discern needs as they craft personalized experiences across channels, freeing human experts to focus on high-value consultative interactions. LMC sees this trend accelerating as successful brands pivot from price-based engagement to personalized communications that authentically reflect consumer preferences and lifestyles.
Equally important, top performers are using advanced AI to analyze consumer behavior patterns and emerging trends, such as the transformative impact of weight loss drugs on food and beverage consumption.
- To succeed here, retailers are changing their approach to merchandising and inventory management by letting AI bridge the traditionally siloed functions of marketing and supply chain — and even R&D — as they adapt to potentially dramatic shifts in consumer relationships with food and beverages.
- In the luxury segment we're seeing particularly innovative applications, where leading retailers are prioritizing relevance and trend prediction over dynamic pricing. One client, for example, employs an AI-powered virtual shopping assistant to deliver personalized recommendations, styling advice and curated product selections.
- LMC anticipates these innovations will drive fundamental changes in how luxury brands compete, moving beyond price-based strategies to deliver defining moments through trend analysis and custom experiences.
AI strategies for NextGen customer experience across key CM sectors in 2025
Dynamic AI pricing models: Adapting to consumer behavior in real-time
The era of blanket pricing strategies is drawing to a close as consumer markets companies face mounting pressure from customers, retail partners and regulatory bodies. In 2025, we’ll likely see AI-driven pricing evolve from periodic optimization exercises to continuous, precisely planned operations. Category leaders are already moving beyond traditional elasticity modeling and investing in sophisticated simulation capabilities that help improve existing portfolios while scenario planning competitor responses before launch. More than just setting prices, these platforms predict market restructuring, volume redistribution and profitability impacts across entire portfolios, boosting a company’s ability to capture growth in mature markets where traditional expansion has become limited.
The integration of AI pricing with supply chain management represents a new frontier as well.
- Many global companies have discovered that even highly accurate demand predictions can falter when supply chains stretch from international manufacturing hubs to US retail shelves.
- Beyond just forecasting demand, the challenge is developing nimble AI systems that can quickly recalibrate pricing and promotional strategies based on real-time supply information.
- Leading organizations are responding by connecting their supply sensing directly to dynamic pricing engines, enabling automated adjustments that can preserve revenue even when supply constraints reallocate inventory between channels.
- For consumer goods companies operating in an environment where supply chain volatility continues to impact global operations, this connected approach to pricing and supply is becoming indispensable for survival.
Finally, the retail sector stands on the cusp of a decisive turn toward personalized dynamic pricing.
- Following a path blazed by airlines and hotels, which have long adjusted prices based on demand, seasonality and capacity, retailers and direct-to-consumer models are now exploring similar capabilities.
- This evolution is particularly evident in quick-service restaurants and fast-moving consumer goods, where AI systems can adjust prices based on local market conditions and real-time consumer behavior patterns.
- It’s worth noting, however, that as AI pricing capabilities mature, the industry has faced mounting pressure for transparency in pricing decisions.
- The challenge centers around building explainable AI models that can justify pricing decisions to consumers, retail partners and regulatory bodies.
AI-driven pricing strategies across key CM sectors in 2025
Streamlining regulatory compliance, including tax: AI-enhanced reporting for regulatory requirements
AI compliance solutions are changing how consumer markets companies tackle today’s intensifying regulatory demands. Organizations are beginning to deploy autonomous AI agents alongside human talent in areas like sustainability reporting and trade policy impact analyses, where unprecedented data collection challenges are demanding innovative solutions.
Sixty-two percent of CM leaders expect generative AI to deliver measurable value within the next 12 months or more, particularly in improved resilience concerning risk, security and controls. These capabilities are becoming indispensable for many organizations navigating diverse state-by-state packaging regulations and fluid sustainability mandates. Moreover, AI plays a pivotal role in compliance monitoring, with systems that continuously scan supply chains and instantly update protocols to prevent risks across borders. This covers packaging compliance as well as broader environmental and social considerations, including health and safety, which are important to consider as we move into 2025.
At the same time, the expansion of AI’s cross-functional reach is redefining regulatory compliance from a siloed function into an integrated dynamic capability.
- Market leaders are combining sustainability data, financial data and operational indicators for real-time compliance insights. AI verification systems now instantly analyze marketing claims across digital channels, catching potential regulatory issues before they arise and aligning with the Federal Trade Commission’s (FTC) Green Guides, as well as international requirements like the EU’s Green Claims Directive.
- The true potential of modernized compliance operations, however, is unlocked when guided by strategic human oversight — an approach that closes reporting gaps and enhances the impact of AI-driven insights on broader business objectives.
We expect to see continuous acceleration of AI in tax operations, as real-time intelligence transforms how consumer markets companies handle multi-jurisdiction operations and respond to compressed margins.
- AI is giving tax functions the ability to deliver more with fewer resources — critically important for processing Pillar Two’s thousands of pages of guidance and complex calculations — while helping companies improve tax data management and navigate evolving tariff scenarios with greater agility.
- The direct integration of AI-based solutions into tax processes, such as transaction classification and categorization, enhances automation at scale and dynamic tax optimization.
- This shift elevates tax departments into strategic business partners capable of modeling the implications of decisions or regulatory scenarios instantly. It also repositions tax within business strategy, moving it from retrospective reporting to proactive business advisory.
AI-powered tax and regulatory strategies across key CM sectors in 2025
{{filterContent.facetedTitle}}
{{contentList.loadingText}}
{{contentList.loadingText}}
Contact us
Follow us
