In the past three years, the UK has witnessed something remarkable: artificial intelligence has gone from a specialist research topic to an everyday business tool. Firms now use AI to draft emails, summarise meetings, write marketing copy, analyse sales pipelines, automate customer service, and increasingly—though less visibly—to monitor their competitors.
Among all available AI systems, ChatGPT has become the most widely adopted. Its ability to synthesise, summarise and generate human-like text has made it a natural candidate for tasks that require rapid information gathering and interpretation. One area where this capability has made a particularly sharp impact is competitor analysis, the backbone of strategic planning in virtually every sector.
Yet even as usage grows, questions persist:
How accurate is ChatGPT? What are the risks? How should businesses deploy it responsibly? Will AI replace human analysts? And what does this shift mean for the competitive landscape of UK industries?
This article aims to answer these questions not from the perspective of a technologist selling a tool, but from the position of a UK academic who studies how technology reshapes decision-making. My goal is to arm British readers—professionals, workers, policymakers and the curious public—with a realistic understanding of what ChatGPT can and cannot do, and how to use it wisely.
What follows is a comprehensive guide to using ChatGPT for competitor analysis: its benefits, blind spots, practical methods, ethical considerations and long-term implications for the UK economy.
Competitor analysis has traditionally required time, human judgment and a tolerance for incomplete information. Analysts gathered data from annual reports, market studies, trade publications, social media, customer reviews, job listings, patents, conference presentations, and—less glamorously—thousands of pages of grey literature.
The challenges were well-known:
Slow and labour-intensive
A typical full competitive landscape report could take weeks or months.
High cost
Market intelligence databases, consultancy fees and subscriptions could run into tens or hundreds of thousands of pounds.
Fragmented information
Data lived in siloed repositories—finance, marketing, R&D, sales—with no integrated view.
Human bias
Analysts often interpreted ambiguous signals differently.
AI does not magically eliminate these challenges, but it shifts the balance. It can read thousands of lines of text per second, summarise disparate sources, generate strategic frameworks, and propose hypotheses that analysts might not have considered.
More importantly, it democratises access. A tool once reserved for FTSE 100 firms is now available to small charities, family-run businesses and local councils.
The effect is profound: competitor analysis is becoming faster, cheaper and more accessible—but also riskier if misused.
ChatGPT excels at scanning large volumes of text and extracting themes. If you provide recent articles, websites or reports, it can instantly summarise them and highlight patterns.
Example tasks:
Summarise a competitor’s new product launch.
Extract key claims from multiple press releases.
Compare customer reviews across platforms.
Identify recurring strategic themes in public speeches or interviews.
Its ability to distil complexity into concise narratives saves analysts hours of repetitive work.
With appropriate prompts, ChatGPT can generate structured profiles such as:
SWOT analyses
Porter’s Five Forces
Value-chain breakdowns
Business model canvases
Customer segmentation
Risk matrices
While these outputs require human validation, they offer a starting point that previously took days.
ChatGPT can simulate plausible competitive scenarios by recombining known signals.
Examples:
“How might Competitor A respond if Competitor B enters the UK market in 2026?”
“Construct three scenarios for how the sector might react to new regulation.”
“Give me likely customer pain points that Competitor X could target.”
These are not predictions but structured thought exercises—useful as inputs to human judgement.
AI is particularly helpful in identifying analogies between industries.
For instance:
What a telecom company can learn from airline loyalty programmes.
How grocery retail trends can inform banking UX design.
What fashion brands can learn from software subscription models.
These analogies stimulate creativity and strategic innovation.
ChatGPT can help compare competitors across:
pricing models
product features
market positioning
brand messaging
sustainability claims
customer support structure
It will not produce proprietary data, but it can structure what is publicly visible and explain the implications.
ChatGPT cannot “browse the live internet” unless explicitly connected through an API or plugin. Even then, quality varies. Analysts must never assume ChatGPT’s information is up-to-date without verifying sources.
AI can fabricate plausible-sounding facts, numbers or quotes. This is not dishonesty but a statistical artefact: the model predicts probable text rather than verifying truth.
To mitigate:
Ask for sources.
Validate claims independently.
Use ChatGPT as a supplement, not a replacement.
ChatGPT cannot reveal private sales numbers, internal strategy documents or unpublished financials. Any tool claiming to do so is either misleading or illegal.
AI can present uncertainty with unwarranted certainty. Businesses should interpret its answers as hypotheses—not gospel truth.
Misuse may involve:
Inputting confidential internal data into ChatGPT.
Generating competitive intelligence that violates fair-use policies.
Misinterpreting AI-generated profiles as factual.
In the UK, regulators are watching closely. Responsible use matters.
This section offers a detailed framework for responsible and effective competitor analysis using ChatGPT.
Before prompting ChatGPT, define:
What business question are you solving?
What decision will be informed by this analysis?
What timeframe matters?
A clear objective prevents vague or misleading results.
Provide ChatGPT with:
URLs
Press releases
Product descriptions
Annual reports
Customer review summaries
News articles
Policy documents
AI quality improves dramatically when it works from good evidence.
Examples:
“Analyse these five sources and produce a competitor profile including:
– product strategy
– pricing approach
– brand positioning
– customer segments
– identified risks
– strategic opportunities.”
Or:
“Create a comparison table of the top five competitors based on:
– pricing
– features
– market claims
– sustainability commitments
– recent innovations.”
Structured prompts produce structured outputs.
Every insight needs human review:
Validate claims.
Assess plausibility.
Interpret nuance.
Consider cultural, regulatory and sector-specific context.
Think of ChatGPT as a second analyst—not the final authority.
AI outputs should be logged with:
date
prompt
data sources used
level of certainty
risks and caveats
This builds transparency and supports UK regulatory expectations.
AI insights become valuable only when acted upon. Use them to:
sharpen product differentiation
identify gaps in competitor offerings
craft clearer value propositions
anticipate market trends
strengthen investor presentations
The point is not to admire analysis but to improve decisions.
(All examples anonymised for confidentiality.)
A 45-person manufacturing company used ChatGPT to map competitors in the EU market. By analysing publicly available brochures, tenders and job listings, ChatGPT highlighted a trend: competitors were recruiting for robotics integration teams. This helped the firm anticipate automation trends and win a contract by adapting early.
A national charity used ChatGPT to benchmark fundraising messaging across peer organisations. The AI revealed that others had shifted towards cost-of-living-focused storytelling. The charity updated its own messaging, resulting in stronger public engagement.
A start-up used ChatGPT to model three competitive scenarios for a product launch. The AI identified an overlooked risk: a US rival had filed patents hinting at similar features. The warning allowed the founders to adjust their roadmap and avoid potential IP conflict.
UK institutions increasingly emphasise responsible AI. As an academic council member, I highlight four key principles:
Know when an insight comes from AI. Document your use.
Do not upload confidential data without organisational approval.
Competitor analysis must remain legal and ethical.
Humans—not AI—remain responsible for decisions.
Looking ahead, several shifts are likely:
AI lowers the barrier to entry, intensifying competition.
Organisations will iterate more quickly—both offensively and defensively.
Demand for strategic analysts will grow, but roles will evolve from data-collection to data-interpretation.
Expect clearer UK standards on transparency, safe use and accountability.
Tools are accessible; capability is the differentiator.
ChatGPT is neither a magic crystal ball nor a threat to professional analysts. It is a powerful assistant—capable of accelerating competitor analysis, stimulating new ideas, and democratising strategic insight across the UK economy.
The best outcomes arise when AI augments human intelligence, not replaces it.
For UK businesses, charities, public bodies and independent professionals, the message is clear:
Those who learn to use AI responsibly will gain a competitive edge. Those who ignore it risk falling behind.
The future of competitor analysis is not automated. It is collaborative—human and AI working together.
