LLMs: The Source of All the AI Noise — A CEO’s Guide
AI has entered the business conversation with a level of intensity we haven’t seen in decades.
For many CEOs, the last time technology created this much noise was:
when desktop computing became mainstream in the 1990s,
when the internet exploded during the dot-com boom, or
when smartphones reshaped customer expectations almost overnight.
Today’s AI moment — driven almost entirely by large language models (LLMs) — has that same feeling of inevitability, velocity and uncertainty.
Like previous waves, this one contains:
genuine capability,
exaggerated claims,
vendors moving faster than customers, and
leaders trying to determine what actually matters.
You don’t need to understand the science behind LLMs to lead effectively.
You do need to understand:
why they are creating so much noise,
what they are genuinely good at,
what they will not do,
who is shaping the market, and
where they can create real value in your business.
This guide gives you the CEO’s version — strategic, grounded and commercially relevant.
1. What LLMs actually do (in business terms)
LLMs are the reason AI suddenly appears capable rather than theoretical.
In practical, operational terms, they excel at:
interpreting documents or structured information
summarising large volumes of text
extracting key points
comparing two or more documents
producing first-draft emails, reports and explanations
answering “what, why, how” questions
classifying and triaging high-volume work
They don’t think. They don’t understand.
They recognise patterns at scale.
The simplest and most useful definition for a CEO is:
LLMs allow you to automate the thinking behind routine work.
So the commercial question becomes:
Where do people in your organisation repeatedly answer the same questions or make the same judgements?
That is where LLM value begins.
2. Who is driving this wave: the major LLM players
You are unlikely to “buy an LLM” directly.
But you will see their influence everywhere in your organisation.
OpenAI (ChatGPT / GPT series)
The catalyst for the current wave. Deeply integrated into enterprise tools via partnerships, especially with Microsoft.
Google (Gemini)
Tightly integrated with Google Workspace, cloud services and search; influential across large organisations and the public sector.
Anthropic (Claude)
Focused on reliable, predictable behaviour — increasingly favoured for enterprise-grade use.
Meta (Llama models)
Open models forming the backbone of many vendor tools and bespoke internal solutions.
Microsoft
The main distribution channel for AI into large organisations, especially through Microsoft 365 Copilot and Azure.
xAI (Grok)
A new entrant focused on rapid reasoning and real-time insight; expect growing presence in open-information and conversational workflows.
Inflection / x.ai (Pi)
Influencing enterprise assistant tools through personalised, memory-driven interaction and contextual awareness.
How they enter your organisation
LLMs are already entering organisations from both directions — through staff using their own tools and through vendors embedding LLMs into products — long before any formal adoption plan exists.
That is why CEOs need clarity early.
3. How to think about the current AI moment (a CEO’s anchor point)
Seen through a leadership lens, today’s AI landscape fits neatly into technology waves you’ve already lived through.
Like desktop computing in the 1990s:
LLMs take work that was previously manual and make it instant.
Like the early internet boom:
The hype-to-value ratio is high.
Many ideas will fail.
A few will reshape industries.
Like the early cloud era:
Vendors will move far faster than customers.
Capabilities will appear inside your tools long before you have a strategy for them.
Unlike previous waves:
This one affects thinking work.
It sits inside processes, decisions and communications.
Its impact is broad, quiet and compounding.
Smart CEOs approach this moment the way they approached earlier waves:
calm
focused on fundamentals
sceptical of hype
aware of structural risks
ready to move when the right patterns emerge
4. Where CEOs should actually look for value: repeated thinking work
The early wins from LLMs are not transformational.
They are operational.
Look for places where your teams repeatedly:
• Interpret similar documents
(e.g. reviewing month-end or year-end reports, reading supplier contracts, checking policy documents, interpreting customer case notes)
• Explain the same policies or processes
(e.g. answering recurring HR policy questions, onboarding guidance, explaining compliance requirements to staff or customers)
• Classify cases, tickets or requests
(e.g. categorising service desk tickets, triaging customer queries, assigning priority levels in operations)
• Make decisions guided by clear criteria
(e.g. approving low-risk expenses, interpreting credit control thresholds, applying pre-defined compliance checks)
• Prepare information before real work begins
(e.g. gathering key details for meetings, producing summary packs, preparing briefs from large documents)
• Summarise long materials for others
(e.g. condensing market reports, legal documents, project updates, audits or customer communications)
• Handle high volumes of similar queries
(e.g. finance clarifications, internal IT questions, product or service FAQs, customer status updates)
These patterns appear consistently across:
Finance – recurring commentary, reconciliations, approval flows
Legal & Compliance – contract comparisons, clause checks, policy interpretation
Customer Support / Internal Service – triage, explanations, common queries
Operations & Planning – schedules, forecasts, resource allocation logic
HR – onboarding guidance, policy explanations, repeated training content
These are high-value hunting grounds because LLMs reduce the repetitive cognitive load, not the expertise.
5. What LLMs cannot replace
LLMs do not replace:
strategic judgement
accountability for decisions
political or commercial nuance
cross-functional consensus
ill-defined workflows
decisions without clear success criteria
They accelerate predictable reasoning.
They do not substitute for leadership.
6. What CEOs should expect over the next five years
A grounded view — not hype:
1. AI becomes invisible inside business software
Drafting, summarisation and reasoning features appear everywhere.
2. Sector-specific AI tools mature
Contract review, planning support, customer intelligence and analysis tools become mainstream.
3. Integration becomes the differentiator
The winners are those whose systems and data enable LLMs to plug in.
4. Expectations rise quietly
Clients, regulators and partners expect faster, clearer, more consistent reasoning.
5. Governance becomes essential
Policies, oversight and responsible use become part of leadership.
6. Organisations with clean, accessible data accelerate away
LLMs thrive where data is structured and available.
They stall where it is fragmented.
7. Employees will use LLMs with or without permission
LLMs follow the path of least resistance: they are fast, accessible and immediately useful.
Expect staff to use them informally — long before any policy exists.
Your role is to ensure that enablement and guardrails keep pace with reality.
What to Do Next
If this article has raised questions about where LLMs are already influencing your organisation — through staff use or the software you rely on — the next step isn’t to “adopt AI.”
It’s to understand how your business currently works, where judgement is repeated, and where systems or data will either enable or limit AI-driven progress.
If you want a practical starting point, you can read my AI Summary Guidelines from my previous article — it outlines the structural steps that make AI useful rather than distracting.
And if you’d like help identifying where LLM-driven value actually sits in your business, I’m always happy to have a conversation.
