Please enable JavaScript.
Coggle requires JavaScript to display documents.
ARTIFICIAL INTELLIGENCE Artificial-intelligente-and-consultancy-1200x675…
ARTIFICIAL INTELLIGENCE
DEFINITION AND CRITERIA
:check:
Entry through overlooked / non-consumer segments
Early AI tools targeted tasks humans avoided or found uneconomical: spam filtering, basic rule-based chatbots, simple image tagging.
Initial users were developers, startups, and back-office teams, not premium enterprise decision-makers.
AI also entered non-consumption spaces (e.g. small firms automating tasks they could never afford to outsource).
Trajectory of improvement
Rapid improvement driven by:
Exponential increases in data and compute
Feedback loops from widespread adoption
Better algorithms (deep learning, transformers)
AI performance now matches or exceeds humans in several domains (e.g. pattern recognition, text generation, translation).
Displacement of the dominant business model
Traditional models based on:
Human labor (analysts, junior consultants, content creators)
Hourly billing and headcount scaling
AI enables:
Automation at near-zero marginal cost
Output-based or subscription pricing
This threatens labor-intensive and expertise-based business models.
Initial inferior performance (cheaper / simpler)
“Good enough” performance was acceptable for low-stakes use cases.
However, they were dramatically cheaper, faster, and available 24/7.
Early AI systems were:
Narrow in scope (single-task, brittle systems)
Less accurate than human experts
EVIDENCE FROM THE CASE
:warning:
Has any dominant model been displaced?
Partial displacement already visible:
Content creation
Customer support
Software development assistance
Data analysis and reporting
Full displacement is ongoing rather than complete, indicating active disruption.
Who were the initial users?
Developers and researchers
Startups and tech-native firms
Internal corporate teams using Al as a support tool
Not initially adopted by regulated or mission-critical industries
How did performance compare to incumbents?
Inferior to human experts on complex or nuanced tasks
Superior on:
Speed
Scalability
Cost
Performance steadily improved until Al became viable for core activities.
Who adopted it first: new entrants or established players?
New entrants first
Al-native startups embedded Al into their core offering
Incumbents adopted later, often defensively or incrementally
Many incumbents initially underestimated Al or treated it as "just another too!"
VEREDICT
:star:
Disruptive (and still unfolding)
In some areas, AI may also act as a sustaining innovation, but its broad impact is fundamentally disruptive.
AI fits all key criteria of disruptive innovation:
Initially inferior but cheaper and simpler
Improves rapidly
Enters low-end and non-consumption markets
Begins to displace established business models
BUSINESS IMPLICATIONS
:!!:
Impact on established players
Pressure on cost structures and margins
Risk of commoditization of expert knowledge
Need to redesign workflows and value propositions
Firms that rely on junior labor are especially exposed
Opportunities for new entrants
Ability to compete without scale or legacy infrastructure
Al-first business models outperform traditional ones
Faster experimentation and global reach from day one
Strategic implications
Compete on:
Human-Al collaboration, not pure automation
Trust, ethics, and governance
Domain-specific expertise layered on Al
Incumbents must:
Cannibalize their own models early
Invest in Al capabilities beyond efficiency gains
Strategy shifts from owning resources to orchestrating intelligence
