Managed AI in retail: From AI pilot to value creation engine for retail and consumer goods

From marketing hype to hard infrastructure: Why "Managed AI" is the new operating basis for the retail and consumer goods industry.

USA vs. Europe: Two radically different paths to AI dominance in the retail sector

For a long time, artificial intelligence in retail was considered a playground for innovation departments: a chatbot here, an isolated forecasting model there. But this era of non-committal pilot projects is coming to an end. Given historically low margins, volatile supply chains, and a fragmented data landscape, retailers and CPG manufacturers face a harsh reality: those who merely test AI today instead of scaling it will, in the medium term, be financing the growth of their competitors.

The core problem for many companies is not a lack of data, but the inability to translate it quickly enough into profitable decisions. The retail sector is "data-rich, but decision-poor." Sales figures, inventory levels, customer loyalty card information, and online behavior are buried in silos, while decisions about promotions, pricing, or replenishment are often still based on gut feeling or outdated spreadsheets.

This is precisely where the concept of "Managed AI" marks a paradigm shift. It departs from the notion that every AI project must be a laborious, large-scale IT undertaking. Instead, AI is understood as industrial infrastructure – a managed platform that integrates algorithms, data governance, and operational processes. The goal is no longer the technically fascinating proof of concept, but rather measurable time-to-value: solutions for complex problems such as trade spend optimization or supply chain resilience must be productive not in months, but in days.

This article explores why the transition to managed AI platforms (such as Unframe) is becoming vital for the industry's survival. We analyze how this can drastically reduce forecasting errors, why building your own AI solutions often becomes a costly trap, and how European companies can secure a competitive advantage over the US despite strict regulations. This is no longer science fiction, but the industrialization of intelligence as the new standard for value creation.

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From marketing term to infrastructure question: What “Managed AI” really means in retail

At first glance, the term "Managed AI" seems like the next buzzword in technology marketing. For retail and consumer goods companies, however, it actually describes a profound shift: away from individual AI pilot projects and towards AI as a productive infrastructure layer that runs across promotions, supply chain, pricing, store operations, and customer experience.

Essentially, it comes down to three characteristics that make the difference between hype and measurable added value:

• First, AI is understood as a managed platform, not a project. Instead of forming a new PoC team for each question, a unified AI layer is established that bundles data, models, governance, and integration and can be reused for different use cases.
• Secondly, time-to-value is becoming increasingly important. The traditional approach of "months until the first productive solution" is hardly viable given the current margin and competitive realities in retail. Platforms that provide industry-specific building blocks – for example, for trade promotion optimization, demand forecasting, or store analytics – enable solutions in days instead of months because 70 to 80 percent of the logic is already pre-built and simply needs to be mapped to individual data and processes.
• Thirdly, "managed" is more than just operation. It encompasses continuous monitoring, retraining, performance optimization, security and compliance handling, as well as integration into existing workflows and authorization systems. For decision-makers, the crucial point is that it's not the individual model, but the guaranteed, auditable behavior of the overall solution that determines its economic value.

For providers like Unframe, who position themselves as a managed AI platform for retail and consumer goods, this shift is precisely the leverage point: They address structural scaling problems that the majority of companies are currently struggling with and combine them with the economic logic of reusable, domain-specific solutions.

The structural dilemma of trade: data-rich, decision-poor.

Why is the need for managed AI solutions in retail so pronounced? From an economic perspective, three developments are converging in this sector, reinforcing each other.

• First, retailers and FMCG manufacturers are experiencing a historically high volume of data coupled with fragmented system landscapes. Sales, pricing, inventory, campaign, loyalty, and online interaction data reside in separate systems, often combinations of ERP, POS, CRM, DWH, e-commerce platforms, and Excel-based subledgers that have evolved over decades. Analyses show that many European retailers operate multiple, poorly integrated data silos across channels and countries, severely hindering a consistent view of customers, inventory, and margins.
• Secondly, customer expectations are rising significantly faster than companies' internal capabilities. Current studies show that a growing proportion of consumers are already actively integrating AI into their shopping process – for example, for inspiration, product comparisons, or personalization. At the same time, brick-and-mortar retail remains crucial: Over a third of surveyed consumers still prefer shopping in physical stores, partly because they want to see and try out products and value the immediate experience of possession. This intensifies the pressure on omnichannel capabilities: Customers expect consistent experiences across apps, websites, social media, marketplaces, and physical stores.
• Third, the industry is under persistent margin pressure. Rising costs for personnel, rent, and logistics coincide with price sensitivity and high transparency thanks to price comparison platforms. The scope for foregoing efficiency gains is minimal. AI is therefore not seen as a nice innovation project, but increasingly as a key tool for improving forecast accuracy, inventory turnover, trade spend yield, and average order value.

The result: Many retailers describe a fundamental lack – a consistent, trustworthy 360-degree view of customers, inventory, and profitability across all channels and partners. The mix of fragmented data, historically grown processes, and ad hoc IT projects leads to retailers operating with a wealth of data but limited decision-making capabilities. This is precisely where the platform concept of Managed AI comes in: The solution isn't promised by individual algorithms, but by an architecture that unifies data, orchestrates models, and translates decision recommendations into actionable workflows.

Why so many AI initiatives fail in retail – and what distinguishes “AI that actually works”.

Numerous board members and CIOs in the retail sector look back on several years of AI investments without these translating into clearly measurable improvements in results. Large consulting studies show that only about a quarter of companies are able to scale AI initiatives beyond pilot projects and unlock substantial value, while roughly three-quarters have yet to achieve a tangible ROI. The root cause analysis is noteworthy: around 70 percent of the problems are located not in the technology, but in processes, organization, and governance.

Applied to the retail sector, this means: The bottleneck rarely lies in the quality of a demand forecasting algorithm, but rather in issues such as:

• Lack of end-to-end responsibility for use cases (between IT, business department, data science, controlling),
• unclear data responsibilities and quality,
• Change management deficits in sales, purchasing, finance and store operations,
• a project logic that is optimized for PoCs rather than runtime and scalability.

The figures mentioned in the original text – high proportions of decision-makers without a complete view of customer data, companies lacking confidence in their ability to scale AI company-wide, and organizations lacking the capability to move beyond proofs of concept – reflect precisely this pattern. They align with overarching findings that while personalization and AI are recognized as key drivers of growth, only a minority of companies have operationalized these capabilities across functions and countries.

“AI that actually works” therefore differs less through sensational model innovations than through a consistent logic of industrialization:

• AI solutions are firmly integrated into core processes (e.g., promotion planning, replenishment, vendor evaluation), not as a separate analysis tool.
• Output is action-oriented (e.g., concrete action plans, price recommendations, order suggestions) and editable and traceable in existing systems.
• Results are explainable and auditable – crucial for finance, auditing, compliance and regulatory requirements, especially in Europe.
• The platform handles monitoring, performance measurement, retraining and governance, instead of organizing these ad hoc in projects.

Managed AI platforms implement this logic technically and organizationally. For retailers, the crucial difference is this: instead of mobilizing a new team each time, a growing portfolio of AI applications is operated on the same platform, with shared data models, roles, policies, and integration into the existing stack.

Platform instead of patchwork: The economics of a managed AI stack

Many retailers and consumer goods manufacturers have gained their initial AI experience with point solutions – recommendation engines in e-commerce, standalone demand forecasts in the supply chain, chatbots for customer service. While these individual solutions generate local benefits, they simultaneously create an invisible technical debt: multiple models, data pipelines, access control concepts, and monitoring mechanisms that need to be maintained in parallel.

From an economic perspective, there are many arguments in favor of consolidating this landscape towards a common managed AI stack:

• First, the marginal cost per additional use case decreases. The initial investment in data integration, identity and access management, observability, and compliance pays off across many use cases. The additional effort for further solutions—such as extending pure promotion optimization to include AI-supported anomaly detection in the supply chain—is significantly reduced.
• Secondly, a governance layer is created that makes risks manageable. Instead of ten different models operating with varying data versions and unclear responsibilities, there is a central authority that controls data quality, permissions, audit trails, and incident handling. For European companies with strict data protection requirements and regulatory pressure, this is often a crucial acceptance criterion.
• Third, integration becomes a strength rather than a hurdle. A managed AI approach explicitly designed for broad connectivity – “Any SaaS, Any API, Any DB, Any File” – addresses the core problem of heterogeneous retail landscapes: legacy ERP systems, industry-specific solutions, in-house developed data warehouses, cloud services, and local Excel processes. For business departments, this means that AI solutions appear where work is already being done – in the trade promotion system, the vendor portal, the store dashboard – instead of requiring the creation of new interfaces.
• Fourth, a new OPEX-oriented financing path opens up. Instead of bearing high individual CAPEX costs for one-off AI projects, companies can choose usage models that link costs more closely to adoption and value contribution. This is particularly attractive in volatile markets where investment budgets are tightly controlled.

For providers like Unframe , this platform focus means they are not primarily competing with individual tools, but with the question of who will become the dominant AI orchestrator in the retail and CPG landscape – similar to large cloud platforms in the infrastructure sector.

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Promotions and pricing as a lever for returns: AI-powered trade spend optimization

Promotional and pricing decisions are among the most significant economic levers in the retail and consumer goods industries – and are often characterized by manual, historically grown processes. Trade spend budgets at large FMCG companies reach double-digit percentages of sales; even small improvements in efficiency and accuracy therefore have a massive impact on EBIT and cash flow.

Studies on AI use in the consumer goods sector show that the application of AI, and in particular generative AI, in marketing, R&D, and supply chain management is already widespread: Around two-thirds of global CPG companies use generative AI tools, and even more are planning corresponding budgets. Analyses indicate that AI can increase marketing ROI by approximately 30 percent, reduce forecasting errors by up to 65 percent, and improve the efficiency of supply chain processes by around 20 percent. Applied to promotions, this translates to more targeted campaign mechanics, better volume and uplift forecasts, fewer out-of-stocks, and more efficient budget allocation.

Specific managed AI solutions in the field of doctoral studies aim to industrialize the entire lifecycle:

• Centralization of dealer feedback, historical promotion data, sales and financial data into a consistent data model.
• Automated validation of promotion inputs (e.g. conditions, durations, channels) using rule sets and ML-based anomaly detection.
• Simulation of uplift and profitability scenarios at the SKU, customer, and channel levels.
• Automated generation of suggestions and scenario comparisons for category managers and key account teams.
• Continuous feedback of actual data into the models for continuous improvement.

The effects mentioned in the original example – reducing cycle times from days to minutes and saving tens of millions in trade spend – are economically plausible when one considers that large FMCG companies invest billions annually in trade promotions and terms. Even optimizations in the single-digit percentage range can lead to significant savings without jeopardizing growth.

Differences exist between the US and Europe: In the US, promotional and discount mechanisms are heavily influenced by national chains and sophisticated loyalty programs; the data depth per customer is often greater, and there is a stronger willingness to conduct aggressive pricing and personalization experiments. In Europe, on the other hand, the focus is increasingly on reconciling personalization with data protection and fairness; at the same time, the retail landscape is more fragmented, with many formats and country-specific characteristics. Managed AI solutions must reflect these divergences – from data sources and regulations to differing KPI logics.

Resilient supply chains and vendor management: From reactive firefighting to predictive control

Supply chains in the retail sector are becoming increasingly complex due to geopolitical tensions, volatile demand, sustainability regulations, and growing customer expectations. Traditional planning approaches are reaching their limits; miscalculations quickly lead to overstocking, write-offs, or out-of-stock situations.

Benchmark studies document that AI applications can significantly reduce forecast errors and measurably increase the efficiency of supply chain processes – for example, by reducing forecast errors by up to two-thirds and increasing supply chain efficiency by around one-fifth. For retailers, this means: lower safety stock, better space utilization, less tied-up working capital, and higher availability.

Managed AI solutions for supply chain and vendor management typically integrate several building blocks:

• Demand forecasts that take into account not only historical sales figures, but also promotions, weather, events, competitive activities and online signals.
• Anomaly detection along the supply chain, providing early warnings of demand outliers, delivery delays, capacity bottlenecks or quality problems.
• AI-powered procurement and vendor analytics that evaluates suppliers based on performance, risk, sustainability, and compliance.
• Automated workflows for documents, certificates, audit processes and contract management.

The economic logic is clear: Every day of earlier visibility of an impending shortage or overstock increases the scope for action and reduces costs. In a world where supply chain risks directly impact brand perception and customer loyalty, predictive management becomes a strategic differentiator.

Regional differences are driving the need for managed AI: In Europe, regulatory initiatives such as supply chain and sustainability laws are pushing for greater transparency and documentation, which supports AI-powered vendor and compliance analytics. In the US, on the other hand, flexibility, speed, and cost efficiency take center stage; here, use cases such as dynamic inventory allocation, omnichannel fulfillment, and same-day logistics dominate. A managed AI approach that can serve both worlds significantly expands its addressable market.

Omnichannel personalization and customer experience: More lifetime value instead of more advertising pressure

Consumption isn't simply shifting "from offline to online," but rather into hybrid customer journeys. Current retail studies show that a significant proportion of consumers already actively use AI to plan or execute purchases, and that more than half are open to shopping with AI in the future. At the same time, many customers expect to be able to interact with brands and retailers across multiple touchpoints—social media, apps, marketplaces, physical stores—and still have a consistent experience.

At the same time, physical retail remains relevant: A larger proportion of respondents prefer brick-and-mortar stores to purely digital purchases, especially because they want to see, touch, try on, and take products home immediately. For retailers, this means that personalization shouldn't be limited to e-commerce but must be considered across all channels – from personalized app offers and digital in-store assistants to individualized customer interaction at the checkout.

AI-powered omnichannel personalization aims precisely at this: It aggregates behavioral data from online channels, transaction data from point-of-sale systems, loyalty information, and, where applicable, external signals, and translates this data into concrete recommendations, content, and offers per customer, channel, and context. Unlike traditional rule sets, modern AI models can recognize patterns that escape human analysts—such as combinations of products, times, channels, and price ranges.

Economically, this translates into a higher average order value, increased conversion rate, lower churn, and higher repurchase frequency. Studies in the retail and CPG sectors report that companies using AI-powered personalization achieve significant revenue increases per customer; personalization is among the most important value drivers of AI in consumer goods and retail companies.

There are clear differences between the US and Europe in this regard: In the US, consumers are traditionally more willing to share data in exchange for personalized offers and convenience; the loyalty ecosystems of large chains generate deep, individualized datasets. In Europe, on the other hand, data protection regulations and a generally more skeptical attitude shape the opportunities and limitations of data-driven personalization. Managed AI platforms that want to succeed in Europe must therefore operate differently not only technically, but also in terms of regulation and communication: greater data minimization, a focus on transparency, privacy by design, and on-premises or EU-based data processing.

Smart stores and autonomous shopping experiences: The renaissance of retail space

While many debates in recent years have been dominated by the growth of online retail, it is now clear that physical stores remain the most important sales channel and are simultaneously the testing ground for new AI-powered solutions. Retailers still see great growth opportunities in brick-and-mortar stores and are using AI to unlock this potential.

A key area is AI-powered store analytics. Current surveys from the retail sector show that a large proportion of companies are already using AI for store analytics and insights – often as their primary brick-and-mortar use case. Using computer vision, sensor data, and predictive models, retailers are optimizing store layouts, product presentation, staff scheduling, and replenishment. The benefits range from increased sales floor productivity and shorter wait times to improved product availability.

A second area is the reduction of shrinkage and fraud. Retailers and CPG companies are using AI to detect anomalies at self-checkout tills, in the flow of goods, and with returns, thereby limiting losses. Given that global shrinkage volumes amount to hundreds of billions of dollars, this represents a significant economic lever.

Thirdly, retailers are experimenting with autonomous and "frictionless" shopping experiences – for example, stores where customers can take products and leave without paying in the traditional way; billing and identification are handled in the background via sensors and AI. In Europe, for instance, a large French chain has demonstrated with an AI-powered "10 seconds shopping, 10 seconds paying" store that such concepts are also viable in strictly regulated markets.

Managed AI platforms that combine store analytics, real-time inventory monitoring, shrinkage detection, and autonomous checkout processes not only address efficiency issues but also redefine the in-store experience. This presents retailers with a dual opportunity: they can increase the economic appeal of their retail space while simultaneously creating a differentiated customer experience that isn't solely defined by price.

Integration into complex IT landscapes: Why open connectivity is a strong competitive advantage

In theory, AI-driven transformation often sounds simple; in practice, it fails due to the basic principles of integration. Large retail companies operate historically grown IT landscapes with disparate ERP systems, branch backends, POS systems, e-commerce platforms, data warehouses, and specialized applications – often distributed across countries and formats.

A managed AI approach that is consistently designed for integration – meaning it supports connections to any SaaS system, APIs, databases, and files – creates a structural advantage here. This is because it reduces three key cost factors:

First, the integration effort per project decreases because reusable connectors and integration patterns can be used instead of starting from scratch each time. This is highly relevant from an economic perspective for retail companies that want to address several dozen AI use cases along the value chain.

Secondly, the risk of “IT shadow projects” is reduced. When departments know that the platform can connect their preferred tools and data sources, the temptation to introduce external, isolated solutions that can later only be integrated into the overall architecture with considerable effort decreases.

Thirdly, it increases flexibility in the face of future changes. New SaaS applications, data sources, or cloud platforms can be integrated more quickly without requiring a redesign of the AI ​​layer. This is particularly crucial in the US market with its rapid pace of innovation, but increasingly also in Europe with its growing cloud adoption.

For providers like Unframe, who communicate integration capabilities as a core promise, this is a key differentiator compared to niche solutions. Crucially, the platform must not only connect technically, but also build semantic bridges: shared data models, unified identities and roles, and harmonized business logic.

Market potential up to 2030 and beyond: magnitudes and growth dynamics

To assess the economic relevance of Managed AI in commerce, it is worthwhile to look at the market forecasts for AI in the retail and consumer goods sector.

The global market for AI in retail is currently estimated to be in the low to low double-digit billions, with very high annual growth rates. Various analyses project a market volume in the mid-single-digit to low double-digit billions by 2024/2025 and forecast growth to several tens of billions by 2030 and over 40 billion by the early 2030s, with annual growth rates between 20 and over 30 percent. The common denominator: AI in retail is evolving from a niche market to a core market, which is expected to reach many times its current size over the course of the decade.

In Europe, the market for AI in retail is currently estimated at several billion US dollars, with expected growth reaching the mid- to high single-digit billions by 2030 and beyond. According to forecasts, Europe could thus achieve a share of approximately 15 to 20 percent of the global market by the early 2030s. The growth drivers here are primarily digitalization, omnichannel expansion, personalization, and increased efficiency – slowed, but also qualitatively shaped, by data protection and compliance requirements.

In parallel, an even more dynamically growing submarket is emerging: generative AI in retail. Estimates suggest that the market volume here will be in the low billions by the mid-2020s and could grow to a high double-digit billion figure by the mid-2030s – with annual growth rates well over 30 percent. For the US alone, generative AI in retail is projected to increase from a low three-digit million figure in the mid-2020s to a mid-single-digit billion figure by the mid-2030s.

Similar dynamics are visible in the consumer goods segment: The market for AI in consumer goods is estimated at several billion US dollars, with expected growth rates of around 30 percent per year and a potential volume in the mid-double-digit billion range towards the end of the decade.

These figures illustrate that the addressable market for managed AI platforms in the retail and FMCG sectors encompasses not only pure AI software licenses, but also integration, data, governance, and operational services. Even if only a portion of the projected AI spending is channeled through managed platforms, this represents a multi-year growth market worth billions.

Another perspective comes into play: Some analyses suggest that AI agents could influence or directly control a double-digit percentage of online sales in US e-commerce by 2030. If a significant portion of digital sales growth is orchestrated by AI-powered systems, the central question for retailers is no longer whether to invest in AI, but rather who controls these agentic systems – internal teams or external platform providers.

USA vs. Europe: Two different paths to the same AI goal

Although AI is gaining importance in global commerce, the starting conditions and path dependencies differ significantly between the US and Europe.

In the US, the retail market is more concentrated, with large national chains and platforms possessing enormous data sets and investment budgets. There is a strong willingness to invest aggressively in new technologies and rapidly scale experiments. Studies show that a very large proportion of retail and CPG companies are already evaluating or using AI, that a high percentage report positive effects on revenue and costs, and that the vast majority plan to further increase their AI investments in the coming years. Generative AI is already widely seen there as a lever for customer experience, marketing, pricing, and internal efficiency.

In Europe, the market is more fragmented, with more formats, regional chains, and differing regulatory frameworks. Data protection and data sovereignty play a significantly larger role, as do requirements for transparency, explainability, and fairness of AI systems. At the same time, European retailers report that they are making intensive use of AI—particularly in store analytics, personalization, and supply chain management—with brick-and-mortar scenarios playing a particularly important role.

These differences have direct consequences for managed AI providers:

– In the US, speed, scalability, and innovation are key. Platforms that offer a fast time-to-value combined with high flexibility and multi-cloud capability meet a market that is willing to bear even high initial investments, provided the value proposition appears plausible.

– In Europe, controllability, compliance, and depth of integration are decisive. Platforms must demonstrate that they guarantee data sovereignty, regional storage, GDPR compliance, auditability, and reliable governance without unduly stifling innovation.

At the same time, markets are converging: European retailers recognize the need to accelerate the pace of innovation, while US companies are increasingly acknowledging the importance of data privacy, transparency, and responsible AI. Managed AI platforms that address both worlds—fast, flexible solutions with a high degree of governance and compliance—therefore have the best chance of gaining a foothold in both regions.

Economic business cases and financing logics: From project to recurring value creation

For decision-makers in the retail and consumer goods industries, the question arises: How can the economic value of Managed AI be concretely measured beyond generic growth forecasts?

At the use-case level, benchmark studies show that AI solutions can significantly increase ROI in areas such as marketing and pricing, drastically reduce forecasting errors in demand planning, and significantly improve supply chain efficiency. When this is supplemented by industry studies reporting that a high percentage of companies in the retail sector have achieved revenue increases and cost reductions through the use of AI, a consistent picture emerges: AI is not an add-on, but rather a lever for core P&L positions.

The challenge lies less in the theoretical potential and more in its operationalization at the portfolio level. Managed AI platforms provide support on three levels:

First, they enable a standardized business case logic across use cases. Instead of evaluating each use case separately, systematic cost-benefit models can be established for categories such as promotions, supply chain, store operations, or personalization, each based on industry data, company-specific key performance indicators, and empirical data.

Secondly, they allow for a gradual scaling of the investment. Starting with a focused, highly profitable use case – such as AI-supported promotional planning or store analytics – the platform can be successively expanded to include further use cases without the initial investment being lost. The overall ROI improves as more use cases are built on the same infrastructure.

Third, they support alternative financing models. Usage-based pricing models, success-based models, or hybrid approaches lower the barrier to entry, shift some of the risk to the provider, and link payments more closely to actual benefits. For providers like Unframe , this means that strong reference projects—such as significant savings in trade spend or drastic reductions in manual research effort for financial reconciliations—not only serve as a marketing argument but also form the basis for new, value-based pricing models.

From an economic perspective, Managed AI shifts the discussion from "How much does an AI project cost?" to "What recurring value streams does an AI platform generate over time, and how are these distributed between retailers, manufacturers, and platform providers?".

Governance, explainability and risk: Why "managed" is more than just operations

An often underestimated aspect of managed AI in retail is governance and risk. AI solutions that influence pricing, promotion mechanics, inventory, store layouts, or credit and fraud decisions have a direct impact on sales, margins, compliance, and reputation. The difference between an AI tool and a managed AI platform therefore lies not only in the user interface but also in the depth of the control mechanisms.

Large studies on AI adoption emphasize that the majority of challenges lie in the human and organizational realm: roles, responsibilities, willingness to change, training, and governance structures. A managed AI platform with built-in governance—featuring role and rights models, clear approval workflows, audit trails, cross-model policies, and monitoring—reduces the risk of AI decisions seeping into everyday operations in an uncontrolled and untraceable manner.

This is particularly relevant for the European market. Here, data protection rules, transparency requirements, and industry-specific regulations create a situation in which the explainability and traceability of AI decisions are not only good practice but also a legal obligation. This applies especially when personal data is processed or algorithmic decisions with significant impacts on customers or employees are made.

Managed AI providers who understand governance as a core component of their platform – rather than an add-on module – are therefore positioning themselves not only as technology partners but also as risk partners. For retailers and consumer goods manufacturers, this means they can deploy AI in sensitive areas without having to build separate governance structures for each individual solution.

Strategic implications for decision-makers: How retailers can industrialize managed AI

For C-level decision-makers in the retail and consumer goods industries, the combination of market potential, technological maturity, and organizational challenges results in a clear strategic task: AI must be moved from the experimentation phase to the industrialization and portfolio management phase.

This initially involves focusing on a few, highly relevant use cases with a clear P&L impact, which also serve as "anchors" for further applications – such as trade promotion optimization, demand forecasting, store analytics, or AI-supported finance reconciliation. Such use cases have a high leverage effect on revenue, margin, and working capital, and are simultaneously suitable for building data and governance capabilities that benefit other areas.

In parallel, a platform decision is required: Should AI be built in-house – with all the associated requirements for data engineering, MLOps, governance, and operations – or should the company rely on a managed AI partner that provides industry-specific solutions and infrastructure? The answer depends on factors such as company size, existing expertise, risk tolerance, and the regulatory environment. In many cases, a hybrid approach will make sense, where critical core capabilities remain internal, while standard use cases and infrastructure are implemented via platforms like Unframe .

Crucially, it must also be embedded within the organization. AI should not be isolated in data science teams or innovation labs, but must be integrated into the line organization: Category management, purchasing, logistics, sales, finance, and store operations each need clarity about which tasks are supported by AI, how decisions are made and accounted for, and how performance is measured.

Finally, a realistic assessment of pace and learning curve is necessary. Market forecasts and success stories show that AI will gain massive importance in retail and the consumer goods industry in the coming years. At the same time, studies show that the majority of companies currently still struggle to realize scalable value. Managed AI platforms can close this gap by consolidating technical and organizational complexity, shortening time-to-value, and industrializing governance.

Companies that want to succeed in the retail and consumer goods industries in the coming years—in the data- and margin-intensive markets of the US as well as in the regulated and fragmented markets of Europe—will have to understand AI not as a project, but as a productive, managed layer of their value chain. The strategic question is therefore no longer whether companies use managed AI, but how consistently they do so—and whether they merely achieve efficiency gains or establish new, AI-centric business logics in retail.

Konrad Wolfenstein

I would be happy to serve as your personal advisor.

contact me under Wolfenstein ∂ Xpert.digital

call me under +49 89 674 804 (Munich)

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