Bound the question
Define the decisions the institution needs to make, the outcomes it values, the constraints it must respect and what success would look like.
The AI Readiness Foundation
A readiness engagement that ends in a document, not a dependency.
Before your institution chooses another model, vendor or use case, map the people, knowledge, processes, data and AI already in the building. You leave with a practical, model-independent brief that belongs to the institution.
The board is asking what the AI plan is.
Vendors are pitching before priorities are clear.
Employees are already using AI informally.
No one owns a complete AI inventory.
How the work moves
Inventory before recommendation.
This is not a generic maturity score and it is not a disguised software demo. The scope is set in writing before work begins, and every recommendation is tied to what the institution actually has.
Take inventory
Map people, institutional knowledge, critical processes, data, systems, vendors, current AI capabilities and informal employee use.
Trace the decisions
Identify who owns each decision, what evidence informs it, where judgment enters and which handoffs or dependencies create risk.
Prioritize and document
Rank opportunities and gaps, state what should not move forward, and deliver a sequenced plan the executive team and board can use.
What the institution owns
A useful answer to “where are we, and what should we do next?”
The deliverables are intentionally portable. Use them with LLM Squared, another provider or your own internal team.
AI and automation inventory
Known models, agents, embedded vendor capabilities, employee use, approved purposes, data touched and named human owners.
Human and knowledge map
Skills, key-person dependencies, critical institutional memory, undocumented thresholds and the knowledge most at risk of leaving.
Process and data map
Decision pathways, handoffs, source systems, sensitive data, contracts, access, lineage and where human review must remain.
Opportunity and constraint register
Candidate use cases ranked by value, readiness and risk—including a clear list of what should wait or should not be bought.
Vendor question set
The questions to put to core providers, AI vendors and technology partners about models, data use, controls, changes and exit rights.
Board-ready action plan
A concise executive readout and sequenced ninety-day plan with owners, dependencies, decision gates and measurable next steps.
The engagement rules
The plan has to be valuable even if the answer is “do not build yet.”
That is how readiness stays independent from the platform decision that may follow it.
Start without customer dataEarly work focuses on interviews, policies, metadata, systems and decision pathways. Any data movement is separately scoped and documented.
No platform requirementThe readiness brief stands alone. The institution can execute it internally or hand it to another provider.
Conflicts disclosedIf a recommendation points toward something LLM Squared sells, that commercial interest is stated where the recommendation appears.
Reasons not to proceed countA decision to wait, narrow the scope or reject a vendor is a valid outcome when the evidence supports it.
What can follow
The readiness map decides the next door.
No one path is presumed. The institution may need governance work, one tightly bounded AI outcome, an executive context layer or no new technology at all.
One governed outcome
Apply the foundation to one workflow or decision with a clear owner and a test the institution chooses.
See outcome patternsOffice of the CEO
Extend the context into an executive office that supports situational awareness, briefing and vendor evaluation.
See the platform modelKYA Advisory
Route independent governance, fractional leadership, sponsor banking, embedded finance and fintech pre-diligence to the advisory firm built for it.
Visit KYA Advisory ↗Common questions
Plain answers before the first call.
Is this only for banks?
No. The work is designed for financial institutions and their technology partners, including banks, credit unions and fintechs. The scope is tailored to the institution's size, complexity, risk profile and role.
Does customer data have to move?
Not to begin. Readiness work can start with interviews, policies, system inventories, vendor materials and metadata. If later work requires sensitive data, the handling, access, hosting, retention and exit terms are documented before transfer.
Do we have to buy the LLM Squared platform?
No. The readiness brief is an institution-owned deliverable. It can be executed internally, with LLM Squared or with another provider.
How long does it take and what does it cost?
The scope and fee are fixed in writing after a short working session. Institution size, interview count and the depth of the inventory determine the final scope; the site does not publish a number before those facts are known.
Is this an examination, legal opinion or certification?
No. It is a readiness and decision-support engagement. It does not replace legal counsel, compliance advice, regulatory communication, an audit or an examination.
Start the conversation
Bring the question your institution keeps circling.
Tell us what prompted it. We will tell you whether AI readiness is the right first engagement—and if it is not, we will say so.