Thursday, 17 September 2026
For established companies trying to adapt to artificial intelligence, the biggest obstacle may not be technology at all. It may be the accumulated processes, assumptions and business models that once created competitive advantage but have gradually become barriers to change. That was the central argument presented by Samantha Lomow, EVP and Chief Commercial Officer at Tupperware, during AI4 2026 in Las Vegas. Drawing on transformation work at Tupperware, Foot Locker and Hasbro, Lomow argued that AI is becoming valuable not simply because it allows companies to automate existing work, but because it makes inefficient ways of working increasingly difficult to justify.
Tupperware provides an unusually revealing example. The brand has existed for roughly eight decades and became synonymous with its direct-selling model and the Tupperware party. That model created enormous recognition and helped build one of the world’s best-known household brands, but consumer behaviour eventually changed faster than the organisation around it. Customers discovered products differently, shopped through different channels and increasingly expected brands to be available wherever they chose to buy. Tupperware’s challenge was therefore not simply to digitise an established direct-selling system. It had to reconsider which parts of the company’s traditional model remained valuable and which had become constraints.
The process became more urgent following Tupperware’s financial restructuring. Tupperware Brands Corporation and several related entities filed for Chapter 11 protection in the United States in September 2024. A lender group subsequently acquired major brand assets and intellectual property, creating what Lomow described as a rare opportunity for an established organisation to approach its future with something closer to a fresh start. That did not mean abandoning the heritage that made Tupperware valuable. The more difficult task was separating the brand from the organisational practices that had accumulated around it.
Lomow’s experience across several major consumer businesses suggests that this problem is not unique to Tupperware. At Hasbro, the important shift came from looking beyond the traditional definition of a toy company and recognising the value of intellectual property that could be developed across entertainment and other formats. At Foot Locker, dependence on a historically successful relationship with Nike eventually became less suitable as consumer tastes and the athletic footwear market diversified. The common feature was not age. It was that organisations had become heavily optimised around conditions that were changing.
AI is accelerating the point at which those assumptions need to be reconsidered. Companies can now produce marketing concepts more quickly, analyse customers more deeply, automate service interactions and generate product ideas at speeds that were previously difficult to achieve. But introducing those capabilities into an organisation with cumbersome approval structures and fragmented data can merely expose how slow the underlying company remains. Tupperware therefore began its AI transformation with something less technologically ambitious: understanding how work actually travelled through the organisation.
Management examined the approvals, handovers and internal steps required before decisions reached customers. Processes that had existed for years were reassessed rather than automatically transferred into new digital systems. In some cases, the problem was not that employees needed a faster technological tool. The process itself needed redesigning. That distinction is increasingly important as companies invest in generative AI. Automating an inefficient workflow can make the same inefficient process operate more quickly without solving the underlying business problem. The larger productivity gain can come from removing unnecessary steps before automation begins.
Tupperware encountered a similar issue with data. Before AI could become useful for marketing, product development or customer engagement, years of information needed to be organised. Historical customer data had to be cleaned, while decades of photography, recipes, product information and other brand assets required digitisation. For a legacy consumer company, these archives potentially represent an enormous competitive asset. An AI system can draw on decades of product knowledge, consumer behaviour and creative material that a younger competitor does not possess. But that advantage exists only when the information can actually be accessed and understood digitally.
This is becoming one of the paradoxes of enterprise AI. Companies with the longest histories can possess some of the richest proprietary data, yet they may also face the greatest difficulty making it usable because information has accumulated across older systems, paper records, disconnected databases and different organisational structures. Once that groundwork had begun, Tupperware started using AI alongside employees in product innovation and creative development. Lomow described teams using the technology to explore new product concepts and consider how different consumers around the world might use them. Ideas could be tested more rapidly before determining which concepts justified further development and investment.
The objective was not to replace the company’s designers or product specialists. AI was used to increase the number of possibilities teams could explore and reduce the effort required to reach an early conclusion. That changes the economics of experimentation. Product development traditionally requires companies to commit people, money and time before receiving meaningful customer feedback. Generative technologies can lower the cost of the earliest stages, allowing teams to discard weaker possibilities sooner and concentrate investment on concepts with greater potential.
For established brands, that capability could be particularly important. Large organisations often become less willing to experiment precisely because they have more to protect. Approval systems, financial controls and brand standards develop for legitimate reasons, but over time they can also increase the cost of trying something new. Lomow argued that companies therefore need controlled environments where employees can experiment without exposing customers, corporate data or the brand to unacceptable risk. Governance remains essential, particularly around privacy, customer trust and sensitive information, but governance should create safe boundaries rather than make experimentation practically impossible.
The cultural issue may prove more difficult than the technological one. Employees who have experienced years of restructuring, digital programmes and management initiatives may not automatically view another transformation positively. Gartner has repeatedly highlighted growing concern around organisational change fatigue, while Gallup’s 2026 global workplace research estimated that low employee engagement cost the world economy approximately $10 trillion in lost productivity.
AI arrives in that environment carrying additional uncertainty about jobs, skills and organisational structures. Employees may therefore judge a company’s AI programme not only by whether the technology works but by whether leadership provides a credible explanation of why it is being introduced and what role people will continue to play. At Tupperware, that included identifying situations where automated service could improve speed while retaining human support for interactions requiring greater attention. Customer-service automation becomes considerably more acceptable when customers understand when they are dealing with technology and know that a person remains available when the automated system cannot resolve a problem.
The same principle applies internally. The most successful teams, according to Lomow, were not necessarily those with the strongest technical backgrounds. They were often the employees most willing to experiment with different ways of working. That observation challenges one of the assumptions surrounding corporate AI investment. Businesses frequently begin by asking which technology they should purchase, which model they should deploy or which AI specialists they need to hire. The more fundamental question may be whether the organisation allows employees to challenge the process into which the technology will be introduced.
If every new idea continues through the same layers of approvals, reporting structures and organisational boundaries, the company can acquire modern technology without becoming meaningfully faster. This is particularly relevant to legacy brands because their organisational procedures often emerged from earlier periods of success. A rule that now appears unnecessary may once have solved a genuine problem. Direct selling, for example, was not an arbitrary restriction imposed on Tupperware; it was central to the brand’s extraordinary historical expansion. The difficulty is recognising when a successful answer to yesterday’s market has become an inappropriate restriction in today’s one.
Tupperware’s current transformation therefore involves broadening rather than simply eliminating its heritage. The company continues to recognise the importance of its seller community and social-selling roots while expanding digital channels and modernising how consumers interact with the brand. Lomow has separately described Tupperware’s recent work as including digital-first initiatives, live selling, renewed product development and experimentation with AI. This highlights the central tension facing legacy brands. Reinvention cannot mean discarding everything that produced decades of brand equity. Consumers may value the history, recognition, product characteristics and emotional connection that a younger competitor cannot reproduce.
The challenge is determining which characteristics belong to the brand and which belong merely to an old operating model. That question has significant consequences for investment. Established companies routinely spend heavily on technology transformations, but the financial return can remain disappointing when the surrounding organisation is unchanged. AI potentially increases this risk because implementation can appear deceptively easy. A company can deploy a generative assistant rapidly while leaving the deeper problems surrounding data, decision-making and customer experience untouched.
Tupperware’s experience suggests a different sequence. Identify what the customer now expects, map how the organisation currently responds, remove unnecessary friction, make proprietary information usable and then determine where AI genuinely improves the process. That approach also changes the role of senior leadership. AI transformation cannot be delegated entirely to technology departments because many of the most important decisions concern organisational design rather than software.
Deciding whether a five-stage approval process should still exist, whether a historical distribution structure remains suitable or whether a customer-service policy needs redesigning requires business leadership. Technology can expose the inefficiency, but it cannot independently decide which corporate assumptions should survive. For Lomow, leadership consequently needs to create conditions in which employees can ask questions that established organisations frequently discourage. Why does this approval exist? Why is this meeting necessary? Why does a decision pass through several departments? What is being protected that customers no longer value?
Those questions can appear simple, but they become increasingly consequential when AI dramatically increases the speed at which competitors can experiment. A younger company designing its operations today does not have to inherit decades of organisational architecture. It can construct workflows around cloud systems, automation and AI from the beginning. Incumbents cannot reproduce that clean starting point easily, but they can selectively dismantle processes that no longer serve a purpose.
Tupperware’s restructuring created an unusually strong reason to do so. Most companies will not have the same external event forcing the question. That makes deliberate experimentation increasingly important. Companies need ways of testing new processes before commercial consequences become significant. Small controlled experiments can provide evidence that a task can be completed faster, with fewer approvals or with better customer insight, giving employees confidence that changing established procedures does not necessarily create greater risk.
The broader lesson extends well beyond consumer brands. Banks, industrial groups, property companies, retailers and professional-services businesses all contain workflows created years or decades before generative AI existed. Many are now attempting to add AI to those structures rather than examining whether the structures themselves remain necessary. The companies that gain the greatest advantage may therefore not be those deploying AI across the largest number of functions. They may be those that use the current technology transition as an opportunity to reconsider how their organisation was designed in the first place.
Tupperware remains a work in progress rather than a finished transformation story. Its restructuring, ownership changes and evolving commercial model mean the brand is still navigating a complicated transition after one of the most difficult periods in its history. Parts of the business are also moving under different ownership structures internationally, including Tupperware’s Latin American operations, acquired by BeFra in June 2026. That complexity arguably makes the example more relevant. Reinvention rarely happens when an organisation is perfectly stable and every decision is obvious. It takes place while companies are simultaneously protecting revenue, employees, customers and existing operations.
AI gives those companies increasingly powerful tools. It can shorten creative cycles, assist customer service, analyse large archives, simulate scenarios and reduce repetitive work. But none of those capabilities automatically tells a company what it should become. For legacy brands, the more valuable opportunity may therefore be the pressure AI creates to ask uncomfortable questions.
Tupperware’s experience suggests that corporate longevity itself does not have to be a disadvantage. An established brand can possess decades of customer relationships, proprietary information and cultural recognition that a new entrant would require enormous capital to recreate. The danger comes when a company treats the processes built around those assets as equally permanent.
In that sense, the defining question facing legacy businesses in the AI era is not whether they can adopt the newest technology. Most eventually will. It is whether they can distinguish the traditions that still create value from the rules that merely preserve the way the organisation used to work.
Source: CIJ.World Research & Analysis Team
