Jason Barnard Explores How AI Systems Influence Customer Decisions at EO Paris Online Event
EO Paris hosted an online session titled “Your Brand Has AI Employees. Train Them to Sell for You.” on Thursday, 28 May 2026, bringing members together to explore how AI systems are already shaping customer decisions long before buyers reach a company website.
Jason Barnard, MarCom Chair at EO Paris and CEO of Kalicube, led the discussion and explained how AI Assistive Engines such as Google, ChatGPT, Perplexity, Claude, Gemini, Microsoft Copilot, and Alexa now actively influence modern buyer journeys.
He argued that these systems no longer function as passive search tools. Instead, they operate as continuous digital representatives of brands, answering questions, comparing options, evaluating credibility, and shaping purchasing decisions around the clock.
He added that customers increasingly encounter AI generated answers before they visit a company website, speak with a salesperson, or request a proposal. As a result, AI systems now influence trust, reputation, and buying decisions much earlier in the customer journey than traditional search engines ever did.
The Seven AI Employees Already Representing Every Brand
During the session, Jason Barnard challenged members to reconsider how they think about AI systems. He explained that businesses already interact with seven AI driven platforms every day that shape how customers discover and evaluate them:
Google
ChatGPT
Perplexity
Claude
Gemini
Microsoft Copilot
Alexa
These systems increasingly influence how customers discover, evaluate, and compare businesses.
Jason noted that while each platform relies on different technologies and data sources, they perform a remarkably similar role. They gather information, interpret it, and present conclusions to users seeking answers, recommendations, and guidance.
In practical terms, he explained, businesses no longer compete solely for visibility. They compete for recommendation.
He highlighted a growing gap between how many companies approach marketing and how customers now make decisions. While many organisations continue to focus primarily on traditional search rankings, AI systems have evolved into recommendation engines that influence purchasing decisions before prospects ever visit a website.
As AI becomes more deeply embedded into everyday workflows and consumer behaviour, Jason said businesses must actively train these systems to understand, trust, and confidently recommend their brands.
From SEO to AEO to Assistive Agent Optimization
As the discussion progressed, Jason traced the evolution of digital visibility over the past two decades.
He explained that Search Engine Optimization originally focused on helping webpages appear in search results, with success measured through rankings, traffic, and clicks.
The arrival of AI generated answers changed that model. Instead of presenting users with a list of options, search engines increasingly began delivering direct responses.
Jason described this shift as the rise of Answer Engine Optimization (AEO), a term he coined in 2017 to describe the move from appearing in results to becoming the answer itself.
Today, he argued, the landscape has evolved again.
AI systems no longer simply answer questions. They compare products, assess credibility, evaluate expertise, and influence decisions.
This shift changes the fundamental objective for businesses. The challenge is no longer ensuring that an AI system can find information about a company. It is ensuring that the system understands the company well enough to recommend it confidently when users ask for advice, comparisons, or purchasing recommendations.
Jason described this next phase as Assistive Agent Optimization (AAO), a discipline focused on training what he referred to as an algorithmic salesforce.
The Three Revenue Taxes Quietly Affecting Brands
Jason used the session to introduce a framework explaining how poorly trained AI systems can affect business performance.
He identified three hidden revenue taxes that businesses increasingly face in the AI era:
Invisibility Tax
A brand fails to appear during early stage AI driven discovery conversations.
Ghost Tax
AI systems mention a brand but subtly favour competitors in recommendations and comparisons.
Doubt Tax
AI systems hesitate, hedge, or weaken confidence when discussing a business in buying related contexts.
Jason argued that these taxes show why AI visibility should not be treated as a traditional SEO problem.
A business may still appear in AI generated responses while quietly losing opportunities to competitors.
For example, an AI system may include a company as one option among several but provide stronger descriptions, clearer positioning, or higher confidence when discussing competing brands. In those situations, visibility exists, but recommendation confidence does not.
According to Jason, recommendation confidence increasingly determines which businesses earn attention, trust, and ultimately revenue.
A Diagnostic for Understanding What AI Says About Your Brand
Jason also shared a diagnostic approach designed to help businesses understand how AI systems currently perceive them.
He explained that many organisations assume visibility alone signals success. However, AI systems now evaluate brands using a broader set of signals, including:
- Confidence
- Corroboration
- Consistency
- Trust
- Recommendation preference
He encouraged members to examine how AI platforms currently describe their brands by reviewing:
- How often they appear in AI generated answers
- Where AI systems express uncertainty
- Which competitors receive stronger recommendations
- Which signals strengthen confidence and trust
Rather than relying on assumptions, Jason said this approach allows businesses to identify gaps in how AI systems interpret their brand and address them systematically.
The Data Behind the Framework
Jason also discussed the research underpinning Kalicube’s work in AI era brand visibility.
He explained that the framework draws on analysis of 73 million brand profiles, enabling researchers to identify patterns in how AI systems understand organisations, personal brands, and entities across the web.
By studying these large scale datasets, Kalicube identifies signals that consistently move AI systems from uncertainty to confident recommendation.
Jason said this research supports the company’s broader work across:
- Search Engine Optimization (SEO)
- Answer Engine Optimization (AEO)
- Assistive Agent Optimization (AAO)
- Knowledge Graph optimisation
- AI driven brand visibility
Rather than positioning these as future focused concepts, he framed them as operational systems that businesses can begin applying immediately.
Businesses Already Have What They Need to Start
One of the strongest messages throughout the session was that most businesses already possess much of the information AI systems need to understand and represent them correctly.
Jason encouraged members to focus less on creating new content and more on organising existing assets.
These often include:
- Frequently asked questions
- Customer support conversations
- Reviews and testimonials
- Sales call insights
- Internal expertise
- Product documentation
He explained that the challenge is not content creation, but consistency.
Businesses that structure and align existing information across platforms make it significantly easier for AI systems to interpret who they are and what they offer.
The Bigger Shift Behind the EO Paris Event
As the session concluded, Jason stepped back from individual frameworks and addressed the broader transformation reshaping digital marketing and customer acquisition.
Search engines, AI assistants, recommendation systems, and conversational interfaces are increasingly converging into a single decision making layer.
Customers now rely on these systems to filter information, evaluate options, and guide purchasing decisions before engaging directly with brands.
Within this environment, businesses no longer compete solely for rankings.
They compete for recommendation confidence.
Jason concluded that organisations which begin training their AI facing brand infrastructure today will shape how AI systems interpret and recommend them long before competitors adapt to the shift.
The businesses that help AI understand, trust, and confidently describe their brand will increasingly influence customer decisions long before those customers visit a website, complete a search, or speak to a sales team.