Who I am
I'm Kate Andreeva, and I founded The Brained Inc. to work directly with mid-market executives and investors on high-stakes AI strategy and execution decisions.
I spent 10+ years in AI and data architecture — most recently at IBM, where I worked as a Data & AI Architect with financial services, telecom, energy, manufacturing, and logistics companies on AI strategy, vendor selection, governance, and implementation oversight.
I've also worked with startups and growth-stage companies across music, tourism, fashion, and SaaS.
I've seen AI succeed and fail across industries. The patterns are the same everywhere.
My default stance
I'm not impressed by tools. I'm impressed by decision ownership and operational clarity.
I'm skeptical of AI initiatives that:
- • Can't explain how work currently happens (you can't automate what you don't understand)
- • Outsource judgment to vendors (vendors are biased toward selling, not solving your specific problem)
- • Prioritize optics over outcomes ("AI innovation" that's really just theater to satisfy board pressure)
- • Start with tools before strategy (this is how companies end up with tool sprawl)
- • Assume urgency = importance (pressure without constraints leads to expensive mistakes)
How I think
A few principles guide my work:
Sequencing beats speed.
The wrong order turns good ideas into waste. Fast execution on a bad plan is worse than slow execution on a good one.
Governance precedes tooling.
Tools without ownership create drift. If no one owns the decision after I leave, it will fragment across departments.
Organizations absorb change slower than technology improves.
Your bottleneck is not the AI model. It's your team's capacity to adopt new workflows without breaking existing ones.
Clarity is measured by decisions that hold, not by persuasive strategy documents.
A good strategy survives contact with reality. A bad one looks great in the deck and falls apart in execution.
Independence matters.
If your advisor is paid to build, they're biased toward building. If they're paid by a vendor, they're biased toward that vendor. I'm paid for decision quality, not activity or tool sales.
Most AI failures are structural, not technical.
Tool sprawl, governance gaps, vendor lock-in, training theater — these happen the same way in fintech and manufacturing, in startups and $500M companies. What changes is the constraints. What doesn't change is decision quality.
Why I don't specialize by industry
I've worked across financial services, telecom, energy, manufacturing, music, fashion, tourism, and SaaS — both with enterprise clients at IBM and startups in high-growth environments.
Most AI decision failures aren't industry-specific. They're structural:
- • Tool sprawl happens the same way in fintech and manufacturing
- • Governance gaps appear whether you're a Series B startup or a $200M mid-market company
- • Vendor dependency risk doesn't care what sector you're in
- • Training that doesn't stick looks the same across industries
What changes by industry is the constraints — timeline, regulatory risk, internal capacity, stakeholder pressure.
What doesn't change is decision quality.
I work with executives and investors who value independent judgment over domain theater.
What I will challenge
If you work with me, expect pushback on:
- Urgency without constraints — "We need this fast" is not a strategy. Why is it urgent? What breaks if we wait? What's the real deadline?
- "We need AI" without a defined process — You can't automate or improve what you don't understand. If your team can't describe how work happens today, we start there.
- Delegating judgment to vendors — Vendors optimize for their outcome (selling), not yours (solving the problem). They're not neutral advisors.
- Strategy that exists only as a deck — If it's not executable by your team, it's theater. Slide decks don't change operations.
- Automating chaos and calling it transformation — Automating a broken process just gives you broken automation faster. Fix the process first.
- Tool selection before governance — If you don't know who owns the decision, buying tools will create fragmentation, not alignment.
Common failure patterns I help prevent
Based on 10+ years working with companies on AI strategy and implementation, these are the failure patterns I see most often:
Tool sprawl
5 different AI platforms across 3 teams, none of them integrated, no central ownership
Vendor lock-in
Committed to a partner who overpromised and underdelivered, now stuck for 18+ months with no exit path
Governance gaps
No one owns AI decisions, so every department does their own thing and the organization fragments
Training theater
Bought the tool, sent the team to a generic AI course, but they still don't know how to use it (or why they should)
Premature automation
Automated a process before understanding how it works, now spending $200k+ cleaning up the mess
AI theater
Spending money on "innovation" to satisfy board pressure, burning credibility with teams and stakeholders
Vendor dependency without oversight
Outsourced execution to a contractor, lost control of the roadmap, now paying 3x what you expected
If you've experienced any of these, you're not alone. This is how most AI initiatives fail.
Why I don't implement
I don't implement because implementation creates incentives that distort judgment.
If I'm paid to build, I'm biased toward building — whether you need it or not.
If I'm paid by a vendor, I'm biased toward that vendor — whether they're the right fit or not.
Advisory only works when your advisor is not paid based on what you choose.
I'm paid for decision quality and execution oversight, not for lines of code, software deliverables, or vendor commissions.
The point is not activity. The point is correct direction.
Teaching and training background
I've spent years teaching and training teams — both at IBM and in academic settings.
What I've learned:
- • Generic AI courses don't work. Training only sticks when it's built around the tools and workflows your team will actually use.
- • C-suite needs strategy context (why this matters, what can go wrong). Teams need hands-on practice (how to use the tool, what happens if it breaks).
- • Training without governance is theater. If no one owns the decision to adopt the new workflow, the training won't translate into execution.
I don't teach "AI 101." I teach your team how to execute on the specific AI strategy you've committed to.
Let's talk
If you're a mid-market executive or investor facing a high-stakes AI decision, reach out.
Include:
- • What you're trying to do with AI (or what you're evaluating, if you're an investor)
- • Your timeline
- • What's at stake if this goes wrong
If you're looking for someone to validate a decision you've already made, don't reach out.