Agentic commerce is no longer a future-facing concept tucked away in product roadmaps. It is becoming the operating reality of how discovery, decision making and checkout converge inside AI-led environments. With Google expanding AI shopping through features such as Direct Offers and the Universal Commerce Protocol, the centre of gravity in digital retail is shifting from brand-led journeys to platform-orchestrated decisions. What is at stake is not just efficiency or conversion. It is negotiating power inside AI ecosystems.
Google’s latest moves underline how quickly this shift is accelerating. By embedding personalised offers directly inside AI Mode conversations and standardising how shopping agents interact with merchant systems, the company is compressing what was once a multi-step funnel into a single assisted flow.
Data shared by Ankur Sharma, co-founder of Brandshark, highlights how the balance is already tilting. He estimates that today 60-70 per cent of digital sales are still driven by direct brand or retailer intent, anchored in brand search, recall and repeat behaviour. Algorithmic influence accounts for roughly 30-40 per cent, spanning search rankings, social delivery, marketplaces and retargeting systems. “That balance, however, is not expected to hold,” he noted.
When decision power moves upstream
“By 2027, we expect decision power to flip to around 55-65 per cent AI-led influence,” Sharma says. “Brand recall will not disappear, but it will increasingly decide whether you enter the recommendation set, not whether you win outright.”
This distinction is critical. In agentic commerce, brands are not competing only against category peers. They are competing for eligibility inside recommendation systems that prioritise safety, relevance and predicted outcomes. The implication is that brands can no longer rely on awareness alone. They must continuously signal reliability, trust and value in machine-readable ways.
Offers, margins and the new performance math
Google’s Direct Offers pilot brings this tension into sharp focus. By allowing AI systems to surface contextual deals at moments of hesitation, offers become a form of performance inventory rather than a blunt growth lever.
Sharma points out that the economics are unforgiving. In his experience, AI-triggered offers need to deliver a minimum of 15-20 per cent incremental conversion lift to justify margin sacrifice. For many mid-scale brands, the break-even point sits closer to 12-14 per cent. Anything below that risks cannibalising organic demand and inflating perceived returns while quietly eroding contribution margins.
This is why agentic commerce cannot be treated as a checkout innovation. It forces brands to rethink where value is created and who captures it. Platforms optimise for closing probability. Brands must protect long-term equity and profitability in systems that reward short-term efficiency.
Data ownership does not disappear, it relocates
One of the biggest anxieties around AI-led commerce is the fear of losing the customer. Ankur Daga, Founder and CEO of Angara, a D2C jewellery brand, argues that this fear is misplaced but not unfounded. As discovery and comparison move into AI interfaces, platforms will control more top-of-funnel intent signals. What users ask, shortlist or compare inside assistants will remain largely opaque, much as it is today.
However, Daga believes the most valuable layer of ownership still sits with brands. “Once a transaction happens, D2C brands retain ownership of purchase behaviour, regional preferences, customisation choices, post-purchase engagement and lifetime value signals,” he says. “The real mistake is assuming the journey is lost because discovery shifts upstream. Post-purchase becomes the new strategic centre of gravity.”
In this view, AI platforms may own intent context, but brands that invest in CRM, personalisation and service excellence continue to own customer truth. Those that rely purely on platform-led discovery without building direct relationships steadily lose insight and leverage.
What AI actually rewards
Understanding how AI systems rank and recommend products is becoming a core strategic capability. Sharma outlines a clear hierarchy in search and large language model driven recommendations, where brand strength acts as an entry gate, followed by distributed reputation through reviews, contextual relevance, relative price intelligence and fulfilment reliability. On social platforms, the logic shifts toward behavioural performance history, predicted action probability and creative resonance, with brand strength playing a secondary role.
This explains why price alone is an unreliable lever. Daga reinforces this view, noting that while availability and price transparency matter in the near term because AI systems are designed to reduce friction, longer-term differentiation comes from outcomes rather than clicks. Repeat purchases, low return rates, positive reviews and brand recall compound into stronger preference over time.
Ravi Adhikari, Head of brand and retail at Chupps, a D2C sustainable footwear brand, echoes this perspective. He expects AI to lean initially on clear, comparable inputs such as availability, delivery speed and pricing, but over time to reward brands that deliver sustained satisfaction. “AI will make discovery frictionless, but it will also make brands easier to substitute,” he says. “The winners will be brands that pair platform readiness with a strong first-party relationship, so they are not just the cheapest option an AI can find, but the brand a customer actively prefers.”
Attribution moves from certainty to probability
Another fault line emerging in agentic commerce is measurement. Sharma estimates that 25-35 per cent of conversions today are influenced by AI-driven discovery but are not cleanly attributable through last-click or platform-reported models. By 2027, he expects AI-assisted conversion influence to become a core KPI alongside CAC and ROAS, tracked through assisted cohorts, brand lift and repeat behaviour after AI exposure.
Daga places this shift on a similar timeline, arguing that over the next 18-24 months the industry will move away from click-centric metrics toward cohort-based and outcome-led measurement. AI has made influence non-linear. Recommendation may shape preference days or weeks before conversion without generating a trackable click.
A redistribution of power
Google’s push into agentic commerce through AI Mode, Direct Offers and the Universal Commerce Protocol signals where the market is heading. By working with large retailers and platforms such as Walmart, Target and Shopify, Google is laying the groundwork for AI agents that do more than recommend. They execute, transact and complete shopping tasks.
This is why the moment feels disruptive. Agentic commerce redistributes power toward platforms that control orchestration layers. Brands that invest early in data ownership, differentiated value signals and AI readiness may retain negotiating leverage. Those that do not risk becoming interchangeable nodes inside someone else’s operating system.
As Daga puts it, AI should be treated as a distribution layer, not the brand itself. It can help a brand get discovered, but it cannot build legacy. The industry is only beginning to grapple with what that means. The next phase of retail will not be decided by who shouts the loudest, but by who understands how to be chosen, trusted and remembered inside machines that are increasingly making the decisions.