By Hezron Ochiel
For years, many Kenyans associated the Kenya Medical Training College (KMTC) with only two courses: Nursing and Clinical Medicine.
The perception became so widespread that many students, parents, and even professionals rarely discussed the institution beyond those programmes.
Earlier this month, in an effort to correct that misconception, we organised a press conference to highlight the wide range of specialised health programmes offered at KMTC.
The institution used the opportunity to showcase courses such as Physiotherapy, Medical Laboratory Sciences, Radiography and Imaging, Occupational Therapy, Nutrition and Dietetics, Pharmacy, Dental Sciences, Orthopaedic Technology, Public Health, Emergency Medical Technology, Mortuary Science, and many others.
The story received extensive coverage across television, newspapers, radio, digital media platforms, blogs, and social media channels.
What fascinated me most was the insight the campaign revealed into the future of reputation management in the age of artificial intelligence, because the press conference was not only correcting public perception but also reshaping KMTC’s AI narrative.
That shift matters enormously today.
AI Systems Are Silently Rewriting Institutional Reputation
For decades, organisations focused on one major question: “What appears on Google when people search our name?”
Today, a new question is rapidly emerging: “What answer does artificial intelligence generate when people ask about us?”
That transformation is accelerating quickly as millions of people increasingly rely on platforms such as OpenAI’s ChatGPT, Google Gemini, Perplexity, and Google AI Overviews to understand organisations, leaders, universities, brands, and institutions.
A 2025 Pew Research Centre study found that nearly six in ten Google users encountered AI-generated summaries during their searches, highlighting how quickly AI-generated answers are becoming part of everyday information discovery.
People are increasingly asking AI systems direct questions such as:
- “What courses does KMTC offer?”
- “Is this institution reputable?”
- “What do people say about this organisation?”
- “Can this university be trusted?”
- “Who is the best expert in this field?”
The answers generated by AI systems are increasingly shaping public understanding long before someone visits a website, reads a brochure, or speaks to an institution directly, because AI systems continuously summarise information from media reports, blogs, public discussions, institutional websites, reviews, social platforms, and archived digital content.
This creates what I call the Machine Reputation Layer.
The Machine Reputation Layer refers to the version of an organisation that artificial intelligence systems repeatedly generate based on dominant digital patterns across the internet.
This layer is rapidly becoming one of the most powerful influences on public trust.
How the “KMTC Equals Nursing” Narrative Formed
The misconception about KMTC did not emerge overnight, as online discussions had long associated the institution with Nursing, Clinical Medicine, and general healthcare training.
Media coverage frequently highlighted nursing admissions and nurse shortages, while students discussing KMTC online often focused on nursing opportunities. Parents advising students commonly mentioned Nursing and Clinical Medicine first, and over time, that repetition gradually created what I call Entity Compression.
Entity Compression occurs when artificial intelligence systems reduce a complex institution into a narrow identity because repeated digital associations continuously reinforce the same perception. In KMTC’s case, the institution gradually became digitally compressed into “Nursing and Clinical Medicine.”
AI systems learn heavily from repetition, and repeated associations eventually become machine-generated assumptions.
The Press Conference Interrupted the Pattern
The recent media campaign disrupted that digital repetition cycle because major media platforms suddenly began publishing stories emphasising course diversity, emerging medical professions, and broader healthcare workforce development.
The framing changed rapidly.
Before the press conference, AI-generated responses to KMTC-related prompts heavily emphasised Nursing and Clinical Medicine. Following widespread media coverage of the campaign, newer responses increasingly began associating KMTC with specialised healthcare fields such as Physiotherapy, Pharmacy, Radiography, Occupational Therapy, and Medical Laboratory Sciences.
The shift demonstrated how AI-generated institutional understanding evolves through repeated digital reinforcement.
Articles published online shape how AI systems understand institutions because headlines create entity relationships, interviews reinforce associations, and searchable explanations improve machine understanding.
The internet essentially began teaching artificial intelligence a broader story about KMTC.
A Real Example of AI Narrative Drift
One of the most interesting developments in artificial intelligence today is what I call AI Narrative Drift.
AI Narrative Drift occurs when outdated or incomplete online narratives continue shaping machine-generated responses even after reality changes.
For example, many institutions address operational challenges internally, while longstanding complaints continue to dominate online discussions. An organisation may improve customer service, digital systems, or student communication, and those improvements may be real and measurable.
The internet may still carry years of unresolved complaints.
When artificial intelligence scans the web, it sometimes prioritises the historical narrative because that version appears more repeatedly across digital sources. This explains why some institutions become frustrated when AI systems continue surfacing old controversies years later.
The challenge increasingly lies in digital narrative imbalance because the operational reality may already have changed.
Why Many Organisations Lose Control of Their AI Reputation
One of the biggest mistakes institutions make is solving problems silently.
Organisations often improve systems, address complaints, modernise operations, and implement internal reforms, yet very few create sufficient public digital evidence to document those improvements online.
Artificial intelligence cannot summarise progress that was never published.
This creates what I call a Digital Memory Imbalance, as the internet often remembers the controversy more loudly, while the resolution itself receives very little searchable visibility.
As a result, AI systems inherit the older narrative.
This is exactly why modern institutions must rethink strategic communication: public relations is increasingly evolving beyond visibility to shape how machines interpret, summarise, and understand organisations.
Media Coverage Is Now AI Training Material
Every press release, media interview, FAQ page, policy explainer, success story, and leadership article now acts as potential AI training material.
That reality is significantly changing the role of communication departments, as the strongest institutions in the coming years may increasingly be those capable of publicly documenting improvements, quickly correcting misinformation, reinforcing consistent narratives, strengthening digital authority, and continuously publishing structured explanatory content.
This is where Generative Engine Optimisation (GEO) becomes extremely important.
I recently explored this shift further in my article on how AI search is changing trust, visibility, and public relations in Africa, where I explained how AI systems are increasingly becoming gatekeepers of trust and discoverability.
I also discussed the growing shift in visibility affecting digital creators in my article on why some LinkedIn creators are losing reach and engagement, in which platform algorithms are increasingly prioritising authenticity, expertise, and trust signals.
The same transformation is now happening across institutional reputation systems.
The Future of Reputation Management Has Already Changed
The KMTC press conference demonstrated something far larger than a media campaign: how organisations are increasingly fighting for narrative accuracy within artificial intelligence systems.
That shift will define the future of communication.
Institutions must now regularly audit prompts such as:
- “What is our reputation?”
- “What are we known for?”
- “What controversies surround us?”
- “What complaints exist about us?”
- “What improvements have we made recently?”
The goal is increasingly shifting from managing search rankings to shaping AI-generated understanding.
This is the new frontier of digital authority.
Organisations that understand this early will build enormous long-term visibility advantages because those that ignore it may eventually discover that their greatest reputation challenge is no longer what people say about them.
It is what machines repeatedly learn to say about them.
Hezron Ochiel is a strategic communications and public relations professional with over 15 years of experience in media, digital communication, and reputation strategy. He serves as the Deputy Corporate Communications Manager at the Kenya Medical Training College and is the founder of Hezron Insights, where he writes about AI visibility, Digital PR, SEO, GEO, and digital authority. His work has appeared on Reuters, The New Humanitarian, and The Standard Group.