You cannot clearly explain the return on your AI spending.
The invoices are clear. The economic value is not.
Independent AI diagnosis & executive advisory
Before you spend another dollar on AI, find out what is actually working.
Your organization may have AI pilots, vendors, licenses, infrastructure, consultants and growing costs.
Everybody has an opinion. What do you actually know?
I help executives establish what their AI investments are producing, what they are costing, which assumptions hold up and which do not, and what deserves further investment.
I do not begin with a technology recommendation. I begin with the evidence.
You should call me if
Your AI program has become a collection of pilots, vendors and initiatives without a coherent picture.
You need to know what exists, what matters and what should stop.
You are being asked to approve more AI spending and are not satisfied with the business case.
You want the assumptions tested before more capital is committed.
The technical story sounds impressive, but you are not convinced it translates into business value.
You need the technology, economics and operating reality translated into one decision.
The implementation is called “successful,” but you are not sure anyone is actually benefiting from it.
Project completion is not the same thing as economic or operational success.
You want someone independent to ask the questions that are not being asked.
No implementation quota. No preferred cloud. No obligation to defend the original decision.
The first conversation
Bring me one AI investment, deployment or decision that is bothering you.
Thirty minutes.
Tell me what you expected, what you spent, what happened and what you are being told now.
I will ask questions. If I think I can help, I will tell you how. If I do not, I will tell you that too.
No pitch deck required.
If the problem needs more work
Establish the facts.
What was expected? What was bought? What does it cost? Who uses it? What changed? What evidence supports the claimed value?
Test the assumptions.
Separate vendor claims, internal narratives, technical performance and economic reality. Identify what still needs to be proven.
Make the decision legible.
Reduce the noise to the few questions leadership actually needs to answer: continue, change, stop, investigate or invest.
The objective is not to sell you more AI. It is to understand the problem well enough to make a better decision.
Independence matters
I have interests in AI. You should know exactly what they are.
I previously founded and served as CEO of Novawerke AI, an AI infrastructure venture that is no longer operating. I am also Founder and Interim Director of VOX Felix Laboratory, a nonprofit public-interest AI research organization.
If my current role at VOX Felix or any other interest creates a material conflict with the question you are asking me to examine, I will tell you before accepting the engagement.
And if I cannot give you an independent assessment, I will not take the engagement.
You are asking me to distinguish evidence from narrative. You should expect the same standard from me.
What happens next
Define the question.
We agree on the specific decision, investment, deployment or uncertainty that needs a clean answer.
Identify the evidence.
We determine what financial, technical and operating information matters, and who I need to speak with to understand what is actually happening.
Bound the work.
Before anything begins, you know the scope, timeframe, deliverable and agreed fee. I prefer defined work to open-ended consulting engagements.
Make the decision legible.
The output is not more AI theater. It is a clear account of what the evidence supports, what remains uncertain, which assumptions hold up and which do not, and what leadership actually needs to decide.
How I look at an AI investment
A company tells me its AI deployment is successful. I want to know what that means.
What was the original business case?
What changed after implementation? What is the fully loaded cost — not merely the model, license or cloud bill? What measurable work disappeared, accelerated or improved? Who is actually using it? What human work was added to support it? What new dependencies did it introduce? What would happen if it disappeared tomorrow?
A successful implementation and a successful investment are not necessarily the same thing.
This is the standard I apply to my own work as well: separate the claim from the evidence, identify the assumptions, and make uncertainty visible rather than decorating it.
The questions behind the work
01
Does it pay?
AI economics
Where is the money going? What value is being created? Are the assumptions behind the investment still valid once inference, infrastructure, integration and organizational costs are included?
02
Can we depend on it?
AI dependency
What dependencies have been introduced? What happens when cloud connectivity, vendors, APIs, data or supporting infrastructure are degraded or unavailable?
03
Are we ready for it?
AI consequences
Does the organization have the operating model, governance and understanding required to use AI effectively and responsibly as adoption accelerates?
What I am working on
Can we depend on it?
AI Continuity — previous work at Novawerke AI
I founded and previously served as CEO of Novawerke AI, where I explored AI Continuity: maintaining useful AI capability when cloud connectivity, communications, external services or supporting infrastructure become degraded, constrained, compromised or unavailable. Novawerke is no longer operating.
During that work, Novawerke explored the ABBIE concept — the Adaptive Bi-level Brain for Integrated Edge — and was selected as a finalist in the 2026 TechConnect MOSA Innovation Challenge. This is background experience, not a current Novawerke product or service offered through seankerr.ai.
Are we ready for it?
VOX Felix Laboratory
I am Founder and Interim Director of VOX Felix Laboratory, an independent nonprofit research organization examining how artificial intelligence is affecting organizations, communities and institutions.
Its work includes research into AI adoption among U.S. municipalities and efforts to understand the developing landscape of AI ethics, standards, oversight and governance.
Does it pay?
Independent research & advisory
I investigate the economics and operating reality of AI adoption independently: what organizations expected, what they spent, what actually happened and what they learned.
This work informs both my writing and focused advisory work for executives confronting difficult AI investment, economics and deployment questions.
Why this perspective?
My route into artificial intelligence has not been conventional.
My professional life has crossed capital markets, defense intelligence, enterprise technology, project and product delivery, software and data, entrepreneurship and public-interest research.
I have worked in environments where complicated systems, risk, infrastructure and institutional decision-making have real consequences. Along the way I continued building technically through project and agile disciplines, data science, Python, decentralized finance and artificial intelligence.
That history has left me less interested in AI as spectacle than in what happens after the demonstration.
Does it create value? Can we depend on it? What happens when institutions begin depending upon systems they do not completely understand?
Analysis & writing
The AI Continuity Brief
I write about the economics, infrastructure and consequences of artificial intelligence — and I am assembling earlier work that shows how I approach markets, technology, risk and institutions over time.
Not AI news for its own sake. Not predictions for their own sake. Not another list of tools you should be using this week.
I am interested in evidence, contradictions, operating experience and difficult questions.
As the archive is assembled, individual analyses and earlier published work will appear here rather than as a list of claims about what I think.
Does it pay?AI economics, ROI & commercialization
Can we depend on it?AI infrastructure, dependency & continuity
Are we ready for it?AI governance, institutions & public interest
Background & credentials
- Former Founder & CEO
- Novawerke AI — prior work on AI infrastructure and AI Continuity; no longer operating.
- Founder & Interim Director
- VOX Felix Laboratory — independent public-interest AI research.
- 2026 finalist
- Novawerke AI selected for the TechConnect MOSA Innovation Challenge.
- Municipal AI research
- Research into how U.S. municipalities are adopting and experiencing artificial intelligence.
- Capital markets & financial technology
- Earlier professional experience spanning derivatives markets, financial systems and technology.
- Defense & intelligence
- Professional experience in defense and intelligence technology environments.
- Technical development
- Artificial intelligence, data science, Python, agile delivery and decentralized finance.
Contact
Bring me the AI problem you cannot get a clean answer to.
If you are trying to determine whether an AI investment is working, whether another investment makes sense, or how to untangle a deployment that has become harder to understand than it should be, I would like to hear from you.
Start with one problem. Tell me what you expected, what happened, and why the answer you are getting does not satisfy you.