Your Next Sales Rep Lives in ChatGPT:
How to sell more leveraging AI
by Anuar Heberlein
The Neiman Marcus Moment
At the turn of the millennium, luxury retailer Neiman Marcus gathered its executives
around a conference table to debate whether anyone would ever buy a US $45 000
handbag online. Most believed luxury purchases required the in-store experience. Yet
only a few years later, the company’s website had emerged as one of its
strongest-performing “stores”.
Today, executives across industries are having the same debate—only this time, the
question is not about selling online, but being found by artificial intelligence (AI).
As our client put it during a recent strategy session:
Our next sales rep is AI. Large
language models (LLMs) will
answer, demonstrate, and
guide buyers whenever they
start searching, even at 3 a.m.
during those insomnia
moments when people reach
for their phone on the sofa and
no human rep is available. We
have to be ready for that.
That comment captures the inflection point we face. In 2025, 58 per cent of consumers
report turning to generative-AI tools for product recommendations—more than double
the proportion in 2023 (INSEAD Knowledge 2025). Traditional websites are no longer the
starting point of discovery. When a buyer opens ChatGPT, Gemini, or Claude and asks for
the best [insert your product or service], will your brand appear?
From Search to Suggestion
For two decades, marketing teams optimised for Google. Success meant ranking high for
keywords, securing backlinks, and driving clicks. But large-language models (LLMs) have
redrawn that map. They synthesise data from millions of sources, present one coherent
answer, and often omit explicit links.
As INSEAD researchers note, brands must now compete for Share-of-Model—the
frequency and favourability with which an AI cites or recommends them (INSEAD
Knowledge 2025). Unlike search, there is no page two.
IDC (International Data Corporation), one of the leading global providers of market
intelligence for the technology and digital industries, projects that by 2029 firms will
spend up to five times more on LLM optimisation than on conventional SEO (IDC 2025).
Meanwhile, Adobe Analytics reports that traffic from AI-generated sources to U.S. retail
sites surged 1 200 per cent year-on-year in early 2025 (Adobe 2025). The implications are
clear:
Three forces are converging to redefine marketing and go-to-market (GTM) strategy.
1. Zero-click journeys. Bain & Company (2025) describes AI agents as the “new
middlemen” compressing the buyer funnel. Where discovery once spanned ten
touchpoints, an LLM may deliver a shortlist in one prompt.
2. Trust through structure. Models favour content that is precise, structured, and
verifiable (INSEAD Knowledge 2025). Broad, emotive copywriting no longer suffices;
the model rewards specificity.
3. Augmented humans. Rather than replacing marketers, AI expands their leverage.
Harvard DCE (2025) concludes that “a marketer’s job will not be taken by AI, but bysomeone who knows how to use AI to shape what the model learns andrecommends.”
Together, these dynamics signal the emergence of a new discipline: AI Optimisation
(AIO)—the systematic practice of making a brand intelligible, trustworthy, and
recommendable within generative-AI environments.
While the terminology is still taking shape—with variants such as AI Engine Optimization
(AEO), Generative Engine Optimization (GEO), Large Language Model Optimization (LLMO),
and Generative Search Optimization (GSO) gaining traction across industry
research—the underlying objective remains consistent: ensuring that AI systems can
reliably learn, interpret, and represent a brand with precision.
Case in Point: SaaS Co’s Next Sales Rep
One of our clients, a North American Software as a
Service (SaaS) platform for asset tracking, confronted
this shift early. Its leadership noticed that buyers were
increasingly prompting LLMs to draft RFPs for
“asset-management software compliant with ISO
27001.” In one exercise, ChatGPT not only generated a
full RFP but also listed vendors—and, for the first time,
displayed screenshots from our client’s application.
That was the wake-up call. The team realised the LLM
had effectively become an autonomous sales.
Our website used to be the front door.
Now ChatGPT is the front desk. We
need to train LLM Models the same
way we train our sales reps.
Our website used to be the front door.
Now ChatGPT is the front desk. We
need to train LLM Models the same
way we train our sales reps.
Within six months of implementing an AIO framework,
the company’s visibility in AI responses increased
exponentially, and demo requests initiated inside AI
interfaces rose in high double digits. More importantly,
sales teams could reallocate time from repetitive
qualification to high-value consulting.
StrategiaGTM’s Six-Step Playbook for AI Visibility
StrategiaGTM has developed a structured playbook for improving visibility inside AI
systems. The methodology is actionable, repeatable, and designed for organisations
seeking to build a durable competitive advantage in the age of intelligent discovery.
The six steps are:
1.Diagnose your current model presence
2. Define ICPs and prompt intents
3. Engineer content for AI ingestion
4. Activate across surfaces
5. Measure and iterate
6. Combine human and AI strengths
Each is explained in detail below.
1. Diagnose your Model Presence
Start by auditing how models perceive your brand:
Run prompts your buyers would use (“best [category] for [use case]”).
Record whether the model mentions you, what facts it retrieves, and what sources it
cites.
Compare visibility across ChatGPT, Gemini, Claude, and Perplexity.
This baseline forms your Share-of-Model index. Treat absent or inaccurate results as
you would broken SEO links.
2. Define ICPs and Prompt Intents
Visibility must align with buyer intent. For each ideal customer profile (ICP):
Identify the “jobs-to-be-done” they search for, not product names.
Map the prompt language they use. Example: “How do I track IT assets across 50
locations?” rather than “asset management software.”
Note the channels whose data models ingest most: review sites, credible trade
journals, and university-affiliated research (Columbia Business School 2025).
Understanding these linguistic and contextual nuances ensures your content mirrors
the way buyers actually query AI.
3. Engineer Content for AI Ingestion – Feed the machine that feeds
the human
Generative systems reward clarity, granularity, and context. Effective AIO content
includes:
Structured knowledge: FAQs, glossaries, technical tables, and use-case libraries
formatted with schema markup.
Utility assets: downloadable RFP templates, ROI calculators, compliance checklists.
Visual-text pairing: labelled screenshots and diagrams that AIs can reference.
Cross-domain corroboration: identical facts echoed across website,
documentation, and third-party listings—reinforcing trust scores within LLMs.
INSEAD (2025) emphasises that models interpret consistency as credibility.
Fragmented messaging confuses algorithms and humans alike.
4. Activate Across Channels
Optimisation extends beyond your domain. Prioritise five channels where models
source their knowledge:
1. Main web domain (Human Focus) — the canonical source; ensure
crawlability and schema precision.
2. Review ecosystems — maintain recency, depth, and balance in review sites;
these are heavily indexed.
3. Expert communities — publish in association newsletters, trade magazines,
and conference proceedings; models weigh these high for authority (Bain
2025).
4. Thought-leadership — articles in academic-style venues feed the credibility
graph that AIs learn from.
5. LLM focused sub-domains — build a structured AI-ready content library on
your blog or resource centre. Clear, well-organised assets such as FAQs,
glossaries, documentation, and how-to guides help position your site as an
authoritative source that models can draw from. Structured, consistent, and
crawlable content increases the likelihood that AI systems reference accurate
information about your brand (more about it later in the article).
Capgemini (2025) found that 71 per cent of consumers now expect generative AI
integration in shopping experiences, reinforcing the value of omni-channel presence.
5. Measure and Iterate
New visibility demands new metrics:
Model Mentions:frequency of appearance in AI responses.
AI-Origin Traffic: visitors or leads referred by generative interfaces.
Prompt Conversion: proportion of AI interactions leading to demo or quote requests.
Knowledge Accuracy: correctness of facts models present about your product.
Adobe Analytics (2025) observed that AI-source sessions exhibit 2.3× higher
engagement than organic search traffic—quality improving even faster than quantity.
6. Combine Human and AI Strengths
The goal is not substitution but amplification. As our client’s CEO notes, AI will handle
“everything repetitive,” freeing humans for creativity and empathy. The analogy he
cites—radiologists and AI—illustrates this perfectly. When algorithms mastered image
interpretation, demand for radiologists rose because they could now focus on
complex diagnose.
In sales and marketing, the same logic applies: automation increases capacity. The
human–AI flywheel works as follows:
AI answers early-stage queries instantly.
Humans step in at high-value junctures—negotiation, integration, relationship.
Feedback from both refines the model, improving future visibility.
Together, these interactions create a compounding loop in which AI accelerates the
front end of the journey and humans elevate the moments that matter, resulting in a
system that is more effective than either could achieve alone.
A Behind-the-Scenes Example: Training AI
Through “Invisible” Knowledge
Architecture (LLM-focused Sub-domains)
During recent work with a North American SaaS client, our team at StrategiaGTM
observed a powerful but counterintuitive phenomenon: the content most valuable for AI
visibility often looks nothing like traditional marketing.
To help large-language models (LLMs) retrieve highly specific, technical answers,
StrategiaGTM helped our SaaS Co client create a series of structured knowledge
repositories, dense LLM-friendly “walls of text” containing exhaustive questionnaires, with
hyper-detailed internal checklists, troubleshooting sequences, compliance definitions,
and edge-case scenarios. These assets were never intended for human readers. In fact,
most were placed deep within resource centres or forum subpages where they would
be nearly invisible to a conventional website visitor.
Generative models do not rely on aesthetics or UX, they rely on density, consistency, and
clarity. Long-form technical artifacts that humans might skim past are often the exact
inputs that allow a model to respond confidently under niche, high-intent queries.
StrategiaGTM’s approach underscores a broader principle of AI Optimisation:
Feed the machine that feeds the human: What trains the
LLM model is rarely what persuades the human.
Companies must therefore architect dual-purpose content ecosystems: polished,
concise assets for people; and deep, structured, machine-readable content for LLMs. By
embracing both layers, organisations dramatically increase the likelihood that an AI
system can “find” them, understand them, and recommend them at the precise
moment a buyer asks a complex question.
What Success Looks Like
Companies embracing AIO report three consistent outcomes:
1. Faster Discovery. Prospects encounter the brand within one or two prompts
instead of multiple ads or searches.
2. Higher Intent. Leads arriving via AI channels convert at rates 1.5–2× higher than
web search (KPMG Digital Pulse 2025).
3. Lower Cost. Marketing teams redirect 30–40 per cent of spend from paid
search to knowledge-engineering and community presence—achieving
similar or greater reach at lower cost.
These results mirror the early 2000s e-commerce curve: initial scepticism, followed by
exponential advantage for first movers.
Four Immediate Actions for Executives
Companies embracing AIO report three consistent outcomes:
1. Run a Model Audit – Within a day, discover whether your company exists in AI
answers.
2. Create an AIO Task Force – Blend SEO, content, product, and data specialists;
empower them to own model visibility.
3. Rebuild Content Architecture – Prioritise structure and depth over volume.
4. Track and Publish Transparency Reports – Monitor how accurately models
describe you; correct misinformation proactively.
As McKinsey (2025) observes,
Companies that treat
AI as a channel, not a
feature, capture
disproportionate early
returns.
The Future Is Already Indexed
The Neiman Marcus anecdote is no longer nostalgia—it’s a mirror. What seemed
implausible then is inevitable now. In a world where buying decisions begin inside AI,
invisibility is failure.
StrategiaGTM’s approach helps firms diagnose, structure, and activate their content so
that AI agents can find, understand, and recommend them.
Because when the buyer opens chat and asks the question that defines your market,
only one answer will appear. Make sure it’s yours.
A Top 3 Cloud vendor wished to
validate the appetite for a new
product and service offering for
enterprise customers. The
company asked StrategiaGTM for
help.
A global FinTech needed to
transform its Sales Org and
Go-To-Market strategy to
leverage platform selling. The
company reached out to
Strategia Partners for help.
