Frequently asked questions
Everyone fears AI. These are straight answers on how the roadmap from Fear to Trust, and from Trust to Revenue, actually works: what an AI governance scorecard is, which frameworks enterprise programs should follow, how fractional AI governance leadership works, and what engagements cost.
Everyone fears AI, leaders fear missing out, getting it wrong, or being replaced. The roadmap from Fear to Trust to Revenue turns that fear into a sequence: name the specific fear, build the governance and guardrails that earn trust, then convert that trust into AI you can actually ship and monetize. Kennedy AI Scorecard runs every engagement along that path.
FOMO™, FOSO™, and FOBR™ are three free, three-minute assessments built around the three AI fears: fear of missing out, fear of screwing up, and fear of being replaced. Each returns a score, a maturity tier, and prioritized next steps, plus a downloadable PDF with citations. They are the fastest way to find your starting point on the roadmap from Fear to Trust.
An AI governance scorecard is a structured framework for evaluating how well an organization's AI systems meet standards for safety, fairness, transparency, and regulatory compliance. It translates technical and ethical risk into a format executives and boards can act on. Kennedy AI Scorecard uses this approach to move regulated enterprises from fear to trust, with a clear, outcome-based view of their AI governance maturity.
Kennedy AI Scorecard helps regulated enterprises build the roadmap from Fear to Trust — and from Trust to Revenue. That means consulting for responsible, sustainable AI governance and guardrails in healthcare and financial services, with outcome-based engagements focused on measurable governance maturity rather than hourly billing. The practice is led by Katharine Kennedy, a former enterprise technology executive with experience building large-scale go-to-market partnerships.
Kennedy AI Scorecard works with boards and executive teams in regulated industries navigating AI adoption, particularly healthcare and financial services. It's built for organizations that need to move fast on AI while maintaining defensible governance and regulatory alignment. Engagements typically involve both technical stakeholders and board-level decision-makers.
Enterprise AI governance programs should align with the EU AI Act, the NIST AI Risk Management Framework (AI RMF), and ISO/IEC 42001, the international standard for AI management systems. These frameworks cover risk classification, model oversight, transparency, and accountability requirements. Kennedy AI Scorecard designs governance programs that map directly to these standards so organizations can demonstrate compliance to regulators and boards alike.
Board-level AI translation is the practice of converting technical AI risk and capability into language and metrics that boards and non-technical executives can use to make decisions. It closes the gap between data science teams and governance bodies who are accountable for outcomes but may lack technical depth. This is a core part of how Kennedy AI Scorecard structures its engagements.
Fractional AI governance leadership is a part-time or advisory arrangement where an experienced executive provides governance oversight without a full-time hire. It suits organizations that need senior-level AI risk judgment but aren't ready for a full-time Chief AI Officer. This model is growing quickly as mid-market and regulated enterprises look for cost-effective ways to build governance maturity.
Fractional AI governance leadership engagements typically range from a few thousand to the low five figures per month, depending on scope, company size, and regulatory complexity. Pricing is usually structured around outcomes and time commitment rather than a fixed retainer. Contact Kennedy AI Scorecard directly for an engagement scoped to your organization's needs.
Regulated industries like healthcare and financial services face specific legal exposure if AI systems make biased, non-transparent, or non-compliant decisions. Strong governance reduces regulatory risk, protects brand trust, and creates a defensible audit trail as AI oversight rules tighten globally. It's no longer optional for organizations deploying AI in consumer-facing or high-stakes decisions.
Human-first AI leadership prioritizes trust, accountability, and organizational culture alongside technical AI deployment, rather than treating governance as a compliance afterthought. It addresses the human fears behind AI adoption — particularly the fear of being replaced — as a leadership and change-management challenge, not just an engineering one. This philosophy shapes how Kennedy AI Scorecard structures client engagements, training, and executive advisory work.
AI strategy consulting focuses on where and how to deploy AI for business value, while AI governance consulting focuses on the risk, oversight, and compliance structures needed to deploy AI safely. The two are complementary, a strong governance foundation actually enables faster AI adoption because trust is what turns AI pilots into revenue. Kennedy AI Scorecard specializes in the governance side, working alongside existing strategy and technical teams.
Timelines vary by scope. The free fear assessments take three minutes, initial governance assessments typically take a few weeks, while full framework implementation can run several months. Kennedy AI Scorecard structures engagements around measurable outcomes rather than fixed timelines, so pace is set by organizational readiness and regulatory deadlines. Fractional leadership arrangements are ongoing rather than time-boxed.
Kennedy AI Scorecard specializes in healthcare and financial services, two of the most heavily regulated sectors adopting AI at scale. Both industries face strict compliance requirements around data privacy, algorithmic fairness, and auditability. The firm's approach is built around the specific regulatory pressures these sectors face rather than generic AI advisory.