An AI strategy your whole company will stand behind.
Vendor-neutral AI strategy for governed enterprises — readiness assessments, risk-tiered case prioritization, and a roadmap with a responsible-AI governance framework, before you fund a single model.
Our Methodology
AI Readiness & Maturity Assessment
Structured evaluation across five dimensions — data quality and lineage, talent, technology, business processes, and existing AI investments — so you start from an honest current-state baseline, not an aspirational one.
Use Case Discovery & Risk-Tiered Prioritization
Facilitated workshops surface candidate use cases, then score each on business value and feasibility and classify it by AI-risk tier — so the portfolio you fund is sequenced by impact and screened for regulatory exposure before anything starts.
AI Roadmap & Business Case
The priority use cases are sequenced into a 12–24 month phased roadmap, each paired with a business case and a model-risk view your finance and risk functions can evaluate — including the honest build-vs-buy-vs-partner call.
Responsible-AI Governance Framework
Responsible-AI principles, an AI risk taxonomy, an oversight operating model, and model-validation protocols — designed at the strategy stage and mapped to the NIST AI RMF and EU AI Act, so governance is architecture, not a retrofit.
Not the ordinary list of ideas — we generate a portfolio.
Every candidate use case is plotted on the value it creates against how ready you are to build it — and tagged with its AI-risk tier mapped to the EU AI Act and the NIST AI RMF. The output is a sequence: fund the defensible quick wins now, invest to enable the strategic bets, and gate the high-risk decisions behind governance before they ever reach your board.
Customer Support Copilot
EU AI Act — transparency obligations; human oversight
High value, data already exists in your ticketing and knowledge systems. Limited-risk under the EU AI Act because users must be told they are interacting with AI and an agent stays in the loop — a transparency control, not a conformity assessment.
A plan built for your auditor’s approval.
Every safeguard is wired into the architecture, documented for the assessor, and mapped to the frameworks you need to pass — not reconstructed the week before the audit.
AI Use-Case Risk Register & Tiering
Every candidate use case classified by AI-risk tier — mapped to EU AI Act risk categories and NIST AI RMF impact levels — and recorded in a risk register your governance team can adopt directly, so high-risk use cases are flagged before, not after, a pilot is funded.
Responsible-AI Principles & Policy
A responsible-AI policy tailored to your organization — fairness, transparency, accountability, human oversight, and acceptable-use boundaries — written so it can be ratified by your AI governance body and referenced in your regulatory file, not left as an aspiration on a slide.
Model Risk & Validation Framework
A model-risk framework covering bias and fairness testing, explainability expectations, drift monitoring, and validation gates — structured along established model-risk-management lines (SR 11-7-aligned for financial-services buyers) so each model has a defined review and challenge process before it influences a decision.
Data Governance & Privacy Alignment
Data lineage, classification, PII and PHI handling, residency, and retention reviewed against the use cases on the roadmap — so the data foundations an AI program depends on are mapped to your privacy obligations (GDPR/CCPA, HIPAA, GLBA) before models are trained on them.
AI Governance Operating Model
Roles, an AI oversight committee charter, RACI for AI decisions, and escalation paths — so accountability for model approval, monitoring, and incident response is established and owned before the first model reaches production, not improvised after an incident.
Independent, Reseller-Free Advisory
We do not resell models, platforms, or licenses, take vendor referral fees, or run a partner program. Foundation-model and infrastructure recommendations are the ones the evaluation produced — documented so your procurement and internal audit teams can verify the independence of the reasoning.
Audit-ready on day one
Our deliverables are written for the people who review AI decisions: your AI governance body, model-risk and internal-audit functions, and — where applicable — your regulator. The risk register, responsible-AI policy, model-risk framework, and governance operating model are produced in formats your governance team can adopt directly. We design strategy and governance frameworks; validation, conformity assessment, and regulatory filings remain owned by your organization and its counsel.
Partner agreements in place
Our Implementation Process
AI Ambition & Governance Scoping
Align with executive sponsors, the risk/compliance liaison, and business unit leaders on AI ambitions, constraints, the regulatory regimes in play, and what a successful, defensible outcome looks like — before the assessment begins.
AI Readiness & Landscape Assessment
Structured review of your data assets and lineage, talent, technology, existing AI investments, and competitive context. We map what you have — and what is missing — across all five readiness dimensions before recommending where AI belongs.
Use Case Discovery & Risk-Tiered Scoring
Facilitated workshops with each business unit surface candidate use cases. Every one is scored for business value and feasibility and classified by AI-risk tier, so the portfolio is prioritized by impact and screened for regulatory exposure in the same pass.
Roadmap, Business Case & Model-Risk View
Priority use cases are sequenced into a 12–24 month phased roadmap, each with a business case, a build-vs-buy-vs-partner recommendation, and a model-risk view. The roadmap accounts for data dependencies, team capacity, and governance gates.
Responsible-AI Framework & Executive Handover
Responsible-AI principles, AI risk taxonomy, governance operating model, and model-validation protocols designed for your organization and mapped to the NIST AI RMF and EU AI Act. Concludes with an executive readout and a handover package built for your team to execute.
Engagement Models
AI Strategy Quick Scan
- AI readiness evaluation across 5 dimensions
- Stakeholder discovery interviews
- Top 5–8 AI use case candidates identified
- High-level value, feasibility, and AI-risk-tier screen
- Executive readiness summary with priority recommendations
AI Strategy & Governance Assessment
- Full 5-step process above
- AI maturity scorecard across all five dimensions
- Business-unit use case workshops and discovery
- Risk-tiered prioritization matrix for all candidates
- 12–24 month AI roadmap with phased initiatives
- Business cases and model-risk view for top 3–5 use cases
- Responsible-AI framework and governance operating model
Enterprise AI Transformation Advisory
- Everything in the Strategy & Governance Assessment
- Multi-business-unit workshop series
- Detailed financial and model-risk modeling for top use cases
- AI operating model and talent development plan
- Change management and adoption framework
- Foundation-model and infrastructure evaluation guidance
- Executive steering-committee and AI governance-body facilitation
Frequently Asked Questions
How is this different from your AI Strategy & Consulting service?
Both produce an AI strategy, but the lens differs. Our AI Strategy & Consulting engagement is the exploratory, AI-pillar offering for organizations getting started with AI. C10 AI Strategy is the Advisory-practice engagement for governed and regulated enterprises — financial services, healthcare, life sciences, public sector, and SOX-reporting companies — where AI risk and governance is the gate. Here, every use case is risk-tiered against the EU AI Act and NIST AI RMF, the deliverables include a model-risk framework and governance operating model, and the whole engagement is built to survive board and risk-committee review. If AI governance is a board-level concern for you, this is the engagement designed for it.
How do you stay vendor-neutral when every consultant claims to be?
Structurally. We do not resell AI models, platforms, or licenses, take vendor referral fees, or run a partner program — independence is designed into the business model, not claimed in marketing copy. Scoring criteria for any model or infrastructure recommendation are defined before evaluation, the reasoning is documented so your procurement and internal-audit teams can verify it, and any vendor relationship is disclosed up front. If the evidence points at an open-source model, a hybrid, or no AI at all, we document that with the same rigor.
Do we need an existing AI team or mature data to start?
No. The assessment is designed for wherever you are today — from zero internal AI capability to a team already running experiments. The maturity assessment specifically evaluates talent, data lineage, and organizational readiness, and the roadmap accounts for your actual baseline rather than an idealized future state. Where data foundations are not ready, the roadmap sequences the data engineering work ahead of the models that depend on it, instead of assuming the data is fit for use.
How do you handle AI regulations like the EU AI Act and model-risk rules?
Regulatory exposure is assessed during prioritization, not bolted on afterward. Each use case is classified by AI-risk tier — mapped to EU AI Act risk categories and NIST AI RMF impact levels — and high-risk use cases (for example, those affecting credit, employment, or essential services) are flagged for a governance gate before any build. For financial-services buyers, the model-risk framework is structured along established model-risk-management lines (SR 11-7-aligned). We design the strategy and governance frameworks to these regimes; formal conformity assessment, validation, and regulatory filings remain owned by your organization and its counsel.
How do you handle the sensitive information shared in discovery workshops?
The assessment uses facilitated workshops, structured interviews, and document reviews — not direct access to your production data systems. Information discussed is treated as confidential under an NDA agreed before the engagement begins and is not retained after it concludes. Where the engagement touches regulated data, a DPA (and a BAA, if PHI is involved) is put in place. Information-handling procedures and access boundaries are documented in the scope document delivered at kickoff.
Can you implement the roadmap after the assessment, or only advise?
We are an advisory service — the deliverable is decisions and documentation, not shipped systems. Most organizations that complete the assessment continue into implementation, because the prioritized use cases, business cases, and governance framework we produce flow directly into engineering engagements with our enterprise AI, data, and security teams. You are never obligated. The deliverables are yours regardless, and the roadmap is written so any qualified team — internal or external — can execute against it.
Ready to build an AI strategy your board and your regulator can both live with?
Book a 30-minute call. We will discuss your AI ambitions, current capabilities, regulatory context, and timeline — and outline what a vendor-neutral, governance-first assessment looks like for your organization.