OpenAI’s reported plans to develop a “humanlike” AI companion could bring consumers one step closer to something that, until recently, belonged in a Marvel movie: The real-life version of Iron Man’s trusted sidekick, J.A.R.V.I.S.
I will admit to being enough of a Marvel fan to point out that Stark never named it after a person. J.A.R.V.I.S. stands for Just A Rather Very Intelligent System. What made it useful was not how it spoke, but its connectivity. It was connected to the suit, the workshop and everything he owned, and it could act on the live state of all of it. That distinction matters more than it sounds, because it is the one most of the current conversation is missing. These systems will be defined by context, not conversation.
The vision goes far beyond asking Amazon’s Alexa to play a song, or Apple’s Siri to set a timer. Picture an AI assistant that knows your preferences, understands what you’re doing and anticipates what you need before even asking. This could fundamentally change not only how consumers interact with technology, but also how they interact with brands.
For example, imagine if your J.A.R.V.I.S.-like AI companion knows you’re headed on a tropical vacation. Rather than waiting for you to start searching, it could eventually anticipate that you may need a hotel, restaurant recommendations, activities or a new pair of flip-flops and begin surfacing personalized options on your behalf.
The bigger question is whether consumers are truly ready to embrace AI companions as a primary interface and, if they are, how that shift will reshape the relationship between brands and consumers.
The Building Blocks
Creating an AI companion that understands context and anticipates needs in everyday life will require more than a sophisticated model. On the surface, many companies appear well on their way.
Most large enterprises I work with can tell you they are running AI somewhere. Far fewer can answer a harder question: if an outside system queried yours right now for what you sell, what it costs, whether it’s in stock and whether it suits a particular customer, would it get a correct answer without a person in the loop? Widespread adoption shouldn’t be mistaken for readiness. Investment and activity are not the same thing as preparation for a world where AI is expected to understand consumers across every interaction.
A reliable AI companion, like J.A.R.V.I.S, depends on what sits behind it. Knowing a consumer’s preferences is one thing, but understanding those preferences alongside their past behavior, current circumstances and intent is what gives AI the context to make a recommendation that is actually relevant.
That is a plumbing problem more than a model problem, and the plumbing has been moving quickly. Model Context Protocol went from a proposal to a default way of connecting internal systems to a model in about eighteen months, and the name gives away the problem it was built to solve. The commerce layer moved faster still. OpenAI and Stripe shipped checkout inside ChatGPT alongside the Agentic Commerce Protocol in September 2025, and Google launched a competing standard with Shopify, Target, Walmart, Wayfair and Etsy in January 2026. Whatever eventually reaches a kitchen counter, the rails for an assistant to act on someone’s behalf already exist.
Creating Trust
For AI companions to become a primary front door to information, they will have to earn consumers’ trust, which is what makes the information behind them just as important as the model and intelligence itself. AI may continue to get smarter, but its recommendations will only be as trustworthy as the data it has to work with.
If a retailer’s inventory is outdated or customer information conflicts across systems, a very sophisticated model can deliver a poor recommendation. The more consumers rely on AI to narrow their choices, the more important connected and accurate systems become.
When AI begins making recommendations on a consumer’s behalf, those mistakes carry more weight. It’s one thing for a chatbot to give an incomplete answer to a question, but another when it recommends a hotel that’s already fully booked or surfaces a product that’s out of stock. The cost isn’t only a bad answer. The entire value of delegation is not having to check, and one wrong recommendation puts the checking back. Once someone starts checking again, they’ve stopped delegating.
Trust will not come from making an AI companion sound more “human,” it will come from consistently proving that the information it relies on is correct, and the recommendations it makes are factual and relevant.
For brands, this means information that has traditionally lived across separate systems will need to unite. Customer preferences, purchase history, product information, inventory and interactions across different channels all provide crucial pieces of context and data. If those pieces remain disconnected, an AI companion is left working with an incomplete picture of both the consumer and the options available to them.
In practice that work is unglamorous. Product information management. Entity resolution, so the same item is the same item in every system that describes it. Availability and pricing exposed through an interface rather than rendered into a page built for human eyes. It’s the backlog most organizations have deferred for a decade, and it has just acquired a deadline.
When AI Becomes the Middleman
Today, consumers still do much of the work of discovery themselves, searching for a product, scrolling through options, visiting websites, reading reviews and comparing brands before making a decision. The technology may be moving toward a single interface that can search and recommend on our behalf, but that doesn’t mean consumers are ready to hand over the entire decision-making process.
Search engines, browsers and apps give consumers something important, which is control. We can compare hotel photos, read restaurant reviews, browse different products and decide for ourselves which factors matter most. Instead of presenting a long list of choices to sort through, AI companions could narrow those choices based on what they already know about a consumer’s preferences, needs and circumstances.
Search engines, browsers and apps give consumers something important, which is control. We can compare hotel photos, read restaurant reviews, browse different products and decide for ourselves which factors matter most. Instead of presenting a long list of choices to sort through, AI companions could narrow those choices based on what they already know about a consumer’s preferences, needs and circumstances.
It also inverts where personalization happens. For twenty years, brands personalized on their own property, using behavior collected on their site, in their app, through their loyalty program, to shape what a visitor saw once they arrived. If an assistant becomes the front door, the personalization that matters most happens before the customer reaches you, using data you don’t hold. The job shifts from personalizing the experience to supplying the context that lets something else personalize it well.
Brands have spent years competing for visibility across search engines, social platforms, websites and apps, though AI has put a new spin on what it means to be “discoverable.” In a world where consumers increasingly turn to AI for recommendations, brands will also need to make sure these systems can find them, understand what they offer and recognize when they are relevant. In practice, being discoverable to a machine has less in common with SEO than with data engineering: structured product data, complete and consistent attributes, descriptions written to be parsed rather than skimmed.
There’s a harder problem underneath it. When an assistant assembles three options and yours isn’t among them, nothing tells you. There’s no impression log, no ranking report, no lost-auction notification. You don’t get to observe the consideration set you were left out of, which means brands may start losing demand in a channel with no diagnostic surface at all.
Preparing for a J.A.R.V.I.S. World
If AI earns enough trust, the first search may no longer be ours to make. The question for brands is no longer only whether a consumer can find you, but whether an AI companion understands your brand well enough to put you in front of them in the first place.
Are consumers ready for that? Not broadly, and maybe not soon. Search engines, browsers and apps will remain the dominant gateway for years, because people want control over the decisions they care about. But readiness isn’t a single switch, and treating it that way is how brands talk themselves into waiting. Delegation will start where friction is highest and emotional investment is lowest, in reordering and replenishment and the kind of purchase that feels like an errand. That’s a large share of retail volume, and it’s also where brand preference is thinnest and substitution is easiest.
Preparing for this future is about more than adopting the latest AI tools, and instead ensuring the information behind products, services and customer experiences is connected, accurate and accessible enough for AI to understand. Three things make that concrete. Audit whether an outside system could get accurate, current answers about your top-selling products with no human involved. Give someone ownership of agent readiness, because right now the question sits between commerce, IT and marketing and therefore belongs to nobody. And start measuring assistant-referred traffic while the numbers are still small, because you can’t demonstrate a shift you never captured the beginning of.
The brands that get this right will be better positioned to remain visible as more of the discovery process moves from consumers searching for answers to AI deciding which answers to surface.
After all, if we’re getting closer to a world where everyone has their own personal J.A.R.V.I.S., brands will need to make sure their digital reputation is strong enough to earn a place in its recommendations. Stark’s system was never impressive because it spoke well. It was impressive because it knew things.
