Franklin Templeton CEO urges businesses to adopt AI faster and protect core data

Franklin Templeton CEO Jenny Johnson urged companies not to delay adopting new technologies and to embrace artificial intelligence more actively. In her view, the question is no longer whether AI is fashionable, but how quickly a business can build it into real processes without losing control over key data.

Why AI has become mandatory

Speaking on Bloomberg Surveillance in Washington, Johnson voiced a position that large corporations and investors increasingly share: AI can no longer be kept in the status of a lab experiment. For executives it is no longer a separate innovation program but a working tool that affects productivity, decision speed and operating costs. This is especially visible in sectors with a lot of manual analysis, reporting and customer service, including finance, consulting and corporate services.

"Companies need to learn to use new technologies, including AI."

Her point is not that every business must rush to buy fashionable tools. It is something else: companies that learn to use the new systems properly before their competitors will gain a compounding effect. They process data faster, see customer behavior more clearly and scale internal processes more cheaply. Those who keep treating AI as an optional extra for one team risk finding, in a year or two, that the gap is no longer one of marketing but of operations and finances.

Data as the foundation for adoption

Johnson's second key emphasis is protecting core data, the foundational data a company's work rests on. This matters especially as generative models are rolled out quickly, since they need access to internal documents, customer information, analytics and correspondence. If a business gives AI access to poorly labeled or poorly protected data sets, it creates a double risk: leaks and wrong answers. In such a setup even a strong model will not deliver a reliable result, because the problem lies not in the algorithm but in the foundation of the whole system.

In practice, this means AI adoption starts not with a polished demo for the board of directors, but with infrastructure work that rarely looks impressive yet determines the outcome. Companies need to decide in advance which data can be connected to models, which data must not be sent to external services, who is responsible for the quality of sources and how results are verified. Without this, even a successful pilot will quickly run into questions of security, compliance and employee trust.

• inventory and classification of key data
• access controls for employees and external services
• connecting models only to verified sources
• measuring impact through time, quality and risk reduction

For a financial company this rule is almost obvious, but it is now becoming universal for other industries too. If a company does not understand which of its data is truly critical, who uses it and where it is stored, any conversation about AI stays superficial. Mass rollout of models without data discipline may temporarily speed up individual tasks, but it can then produce costly errors in reporting, support, compliance and decision-making.

The market looks wider

Johnson also touched on the state of the consumer and oil prices. At first glance these are separate topics, but for an asset manager of this size they are directly tied to the conversation about technology. Consumer demand affects business revenue and companies' appetite for investment, while oil remains one of the markers of inflationary pressure and costs. So for senior executives, the question of AI has long ceased to exist in a vacuum: it is considered together with costs, interest rates, demand and the resilience of the business model.

Hence the more sober approach to adopting new systems in 2026. Boards want to understand not only where processes can be automated, but also how that decision will behave when the macro environment changes. If the consumer weakens, companies need faster control over margins and demand. If energy gets more expensive, the price of inefficiency rises. In such an environment AI becomes not a showcase of innovation but a way to make decisions faster where a mistake costs more than it used to.

What this means

The signal from Franklin Templeton is simple: it is no longer enough for businesses to discuss AI in strategy sessions. They need to carry it through to processes, metrics and protected data handling. The winners will not be those who talk loudest about transformation, but those who tie technology to operational discipline and can make decisions amid shifting demand, costs and prices.