Egghead AI helps enterprise leaders and insurance agencies identify the data, governance, risk, workflow, and operating-model issues preventing AI and automation from producing measurable business results.
Enterprise organizations and independent insurance agencies face different operating realities. Egghead AI applies the same disciplined approach while tailoring the language, scope, and recommendations to each environment.
For chief data officers, chief AI officers, CIOs, governance leaders, and regulated organizations moving from AI experimentation toward responsible enterprise scale.
For agency owners and brokerage leaders who need practical help improving workflows, data quality, client servicing, producer productivity, and responsible adoption of AI and automation.
AI and automation initiatives lose momentum when accountability, trustworthy data, operational processes, controls, and adoption are not designed to work together.
Business, data, technology, risk, and operations lack clear decision rights and escalation paths.
Incomplete, inconsistent, or inaccessible data reduces confidence in AI outputs and business decisions.
Policies exist, but teams lack usable intake, review, monitoring, exception, and accountability processes.
Privacy, security, compliance, legal, audit, and operational expectations are not translated into one workable model.
Disconnected systems, duplicate work, and poor handoffs consume time and create avoidable customer-service issues.
Leadership receives disconnected findings rather than a sequenced plan tied to value, risk, and accountable owners.
Egghead AI helps enterprise teams move beyond broad principles and build the governance, data, controls, and accountability required to scale AI responsibly.
Rapid assessment of governance maturity, data fitness, decision rights, controls, adoption barriers, and immediate priorities.
Clear forums, roles, intake, review, escalation, monitoring, and exception processes that teams can actually use.
Practical improvements to quality, metadata, lineage, stewardship, ownership, and business confidence in critical data.
Prioritized 30-, 60-, and 90-day actions aligned to business value, risk reduction, adoption, and accountable ownership.
Ongoing executive support for leaders who need senior guidance without adding another full-time leadership role.
Specialized advisory support for consulting firms and technology partners serving enterprise data and AI programs.
Independent agencies often have strong people working around fragmented data, manual processes, inconsistent system use, and missed automation opportunities. The Insurance Agency Operations & AI Diagnostic identifies the highest-value improvements without turning the engagement into a theoretical AI exercise.
A focused engagement for owners and leadership teams who want a clear view of where time, revenue, service quality, and employee capacity are being lost.
The process is designed to minimize disruption, create executive alignment, and produce recommendations that can be acted on immediately.
Clarify objectives, scope, stakeholders, current initiatives, and the decisions leadership needs to make.
Review the relevant governance, data, workflows, controls, systems, and operating practices.
Separate urgent issues from lower-value noise and rank opportunities by business impact, effort, and risk.
Present findings and provide a practical roadmap with owners, sequencing, and near-term outcomes.
Michael G. Davis brings more than two decades of experience leading enterprise data governance, data quality, metadata, stewardship, risk, operating-model, and AI-readiness initiatives.
Michael G. DavisMichael has built and led governance capabilities across organizations including Cigna, Aetna, Voya, and SAIC. His work spans AI governance, responsible AI, data governance, data quality, metadata, stewardship, operating models, executive roadmaps, automation, and practical adoption.
His approach combines executive-level strategy with the practical mechanisms required to turn policy, data, technology, and operational priorities into measurable execution.
Start with a focused 20-minute conversation. We will identify whether the primary issue is AI governance, data quality, operational workflow, risk controls, ownership, or executive alignment—and determine whether a diagnostic is the right next step.