Healthcare CIOs: Build AI-Driven, Technology-First Action Plans as Uncertainty Mounts
Regulatory shifts, cyber threats, shrinking margins, and staffing gaps are pulling healthcare in different directions at once. Many organizations are still reacting to crises instead of setting a clear path forward.
Info-Tech Research Group has released its Adapt to Uncertainty With a Technology-First Action Plan for Healthcare blueprint, a step-by-step approach for using AI, automation, and FinOps to reduce risk, protect margins, and improve care quality and equity.
"Healthcare uncertainty is inevitable, but it doesn't have to be paralyzing," says Sharon Auma-Ebanyat, principal research director at Info-Tech Research Group. "By applying a technology-first mindset, CIOs can shift from firefighting to foresight, transforming today's pressures into opportunities that strengthen patient outcomes and long-term resilience."
Why a technology-first plan now
- Reduce operational and cyber risk with consistent governance and automation.
- Protect margins through FinOps, cloud optimization, and vendor right-sizing.
- Support clinicians with AI copilots, better data flows, and streamlined workflows.
- Improve equity with privacy-preserving analytics and more reliable access to care.
Info-Tech's four phases to build resilience
Phase 1: Assess uncertainties and opportunities. Identify policy shifts, cyber exposure, demographic trends, and cost pressures. Map them to specific technology bets that improve efficiency, quality, and value.
Phase 2: Review budgets, staffing, and vendor dependencies. Analyze spend, capacity, and contracts. Cut redundancy, tighten financial discipline, and redirect funds to modernization and AI-driven improvements.
Phase 3: Build a technology-first action plan. Create a prioritized 12-month roadmap. Focus on AI, automation, analytics, interoperability, and risk reduction aligned to clinical and operational priorities.
Phase 4: Prepare for execution and adaptation. Stand up governance, change management, and clear communication. Track outcomes and adjust quickly as conditions shift.
High-impact use cases to prioritize
- AI-powered claims automation to cut denials, reduce rework, and improve cash flow.
- Generative AI copilots for documentation, coding assistance, and patient communication (with guardrails).
- FinOps and cloud optimization to curb waste and fund clinical innovation.
- Privacy-preserving analytics for quality improvement and health equity insights.
- Interoperability upgrades to reduce manual effort and improve care coordination.
Governance and trust: set the guardrails
- Align AI programs to a clear risk framework such as the NIST AI Risk Management Framework.
- Reinforce HIPAA Security Rule safeguards and audit trails for AI, analytics, and automation workloads. See guidance from HHS OCR.
- Implement model governance: data lineage, testing, bias checks, human oversight, and rollback plans.
Your 90-day starter plan
- Run a risk-and-opportunity workshop with clinical, operations, and IT leaders. Rank top 5 uncertainties and matching tech responses.
- Complete a FinOps baseline: cloud spend, contract commitments, and immediate savings levers.
- Select two pilots: 1) claims automation or prior auth; 2) a clinician copilot in a low-risk setting. Define success metrics up front.
- Stand up AI and data governance: approvals process, model registry, security reviews, and communication playbook.
- Publish a simple, visual 12-month roadmap that ties projects to clinical and financial outcomes.
Metrics that matter
- Financial: cost per claim, denial rate, days in A/R, cloud unit costs, vendor consolidation savings.
- Clinical operations: documentation time per encounter, length of stay drivers, referral turnaround time.
- Patient and equity: appointment lead times, no-show rate by ZIP code, access to language services.
- Risk and trust: audit findings resolved, model drift events, PHI access exceptions, incident response time.
Common pitfalls (and how to avoid them)
- Scattered pilots with no shared metrics. Fix with a single prioritization and benefits tracking process.
- Tech-first without clinician input. Co-design workflows, test in the field, and iterate.
- Security as an afterthought. Bake in privacy and threat modeling from day one.
- Vendor lock-in. Keep data portable, use open standards, and track switching costs.
Bottom line
Uncertainty isn't going away. A technology-first action plan lets CIOs move from reaction to intent-reducing risk, defending margins, and improving patient outcomes with measurable results.
For commentary from Sharon Auma-Ebanyat and access to the full blueprint, contact Info-Tech Research Group's media team.
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If you're planning AI pilots or rolling out FinOps practices, focused training can speed up execution. Explore role-based options here: Complete AI Training - Courses by Job.
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