And what sets apart those who actually win with it
These days, it’s hard to find a business that isn’t talking about artificial intelligence. Presentations, pitches, reports, strategies — AI is everywhere.
But dig a little deeper, and in 80% of cases, it’s just for show. There are no real implementations, no measurable results — just talk.Why is this happening?
Despite the accessibility of the technology, many businesses still struggle to get started.
The AI Paradox: Everyone Talks, Few Implement
Entrepreneur and AI founder Dmitry Karpov calls this stage the “zero cycle of maturity”:
“Today, AI is like a gym membership. Everyone’s bought one, but hardly anyone actually goes.”
One of his slides cites a study of large corporations:
- 85% of companies are experimenting with AI
- But only about 10% have real production use cases
- In critical areas like HR, legal, and finance — there are virtually no full-scale deployments
But why?
1. AI ≠ a Fancy Dashboard
The first mistake: seeing AI as a “magic button.” Expectations are sky-high, understanding is surface-level.
So companies build MVPs, run pilots, do integrations — and then… nothing happens.
The reality: AI is not a product — it’s infrastructure. Its value only emerges when deeply embedded into real business workflows.
2. No Real Business Need — Just Hype
One of the most common requests:
“Build us a bot like OpenAI’s.”
Why? What business outcome will it improve?
Silence.
What works:
Start with the question:
“What could we improve in our business with AI — in a way that impacts revenue, time, or quality?”
AI for the sake of AI is like a car with no steering wheel — looks powerful, goes nowhere.
3. Lack of Infrastructure
Many companies try to integrate AI into processes that… don’t actually exist.
Like a “bot to analyze the CRM” — when the CRM is only 20% filled out, and half the data lives in Google Docs.
Or “automated marketing” — without a content database or clear success metrics.
AI only amplifies what already works.
It doesn’t build systems from scratch. That’s why it demands maturity — even if only in one focused area.
4. People Aren’t Ready
AI is intimidating — and that’s normal.
Employees fear layoffs.
Managers fear losing control.
Executives fear being responsible for uncertain experiments.
What helps:
- Explain that AI doesn’t replace people — it redistributes workload
- Launch first projects that support specific teams
- Let people experiment, explore, and see the value firsthand
AI isn’t just about code — it’s about culture.
5. Wrong Level of Implementation
There are two ways to approach AI:
Bottom-up: Automate tasks
- Invoice analysis
- Responding to customer queries
- Aggregating data from multiple systems
These cases are simple, useful, and deliver quick ROI — but their impact caps out at 5–10% efficiency gain.
Top-down: Automate business goals
- Grow revenue
- Shorten sales cycles
- Improve diagnostic accuracy in healthcare
Companies that truly win with AI start with the goal, not the tool.
They don’t ask “What kind of bot should we build?”, they ask:
“How can we grow revenue by 20% while doing less?”
What Sets Real AI Leaders Apart
- A strong product or tech founder/CTO who connects business and tech
- A clear problem with measurable results
- Flexibility: test → adapt → scale
Focus on value today, not disruption tomorrow
Where AI Is Already Delivering Results
Sales: uncovering untouched leads, nudging reps, improving scripts
Marketing: personalized landing pages, content refreshes, audience segmentation
Healthcare: protocol compliance tracking, avoiding insurance penalties
Legal firms: contract pre-analysis, draft generation, anomaly detection
Real estate: property recommendations, automated communications
We’re Still at the Beginning
AI isn’t a hype wave. It’s a new operating system for business.
But like any OS — it needs apps, processes, and users to make it work.The question isn’t if you should use it.
The question is: When will you start using it for real?
Ready to move from talk to action?
Join the San Francisco Innovation Hub Bootcamp —
We help entrepreneurs:
- Identify where AI can bring the most value
- Find the right partners and integrators
Move from theory to real-world implementation.
