From the State Government edition of September 28, 2026
Connecticut Digital States interview names Responsible AI Framework as largest employee AI impact
Connecticut Department of Administrative Services / Office of the CIO (Mark Raymond) · State enterprise IT governance / responsible AI policy / shared services · Connecticut, United States
- Publisher
- Government Technology — Digital States 2026 'A' State Interview: Mark Raymond, Connecticut
- Original publication
- 2026-09-23
- Source retrieved
- 2026-09-29
- Event date
- 2026-09-23
What happened
Why now: this was the last unarchived 2026 Digital States A-state CIO interview after peers (TX/IA/MO/HI/MS/OH/MI/NV/GA/UT) published Sep 25–27; yesterday's edition deferred it for lighter KPIs. CIO Mark Raymond states the largest employee AI impact is Connecticut's Responsible AI Framework (initially 2024)—rules of the road plus participative governance evolving policy, risk, and education with employees—rather than a production chatbot KPI. He also flags HR-1-driven federal program change compressing tech delivery windows toward value/outcomes, sees AI value reconciling divergent cross-agency data definitions (e.g., "family"), and describes AI accelerating cyber detect/remediate while raising automation and executive risk-awareness needs.
Why it matters
Completes the 2026 A-state interview set with a governance-first impact narrative useful when buyers ask what 'most impactful AI' means beyond hours-saved claims.
Evidence and measured results
Opened GovTech interview HTML (curl UA SLED-research-bot/1.0). Direct CIO quotes on Responsible AI Framework as largest employee impact; no new operational hours or accuracy metrics published in the page.
Limitations and uncertainty
Qualitative governance narrative without measured adoption, incident, or productivity KPIs. CART Act already archived; do not double-count as new statutory control. Digital States A grade is multifactor.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-09-28; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- State CIOs need a credible governance-first story when Digital States peers lead with chatbots and hours-saved claims.
- Stakeholders
- State CIO
- CDO / data governance
- Agency AI leads
- Privacy/security
- Workforce learning
- Discovery questions
- How is Responsible AI Framework compliance measured?
- Which high-risk uses require board review?
- What education completion rates exist?
- Where will AI reconcile cross-agency data definitions first?
- Value hypothesis
- A governance and data-semantics engagement can operationalize CT's framework narrative into intake, education, and measurable definition-reconciliation pilots.
- Potential engagement
- 4–6 week Responsible AI intake + cross-agency data-definition pilot design.
- Unsupported claims
- Measured hours saved from the framework alone
- Causal Digital States A from AI policy
- Production chatbot ROI in this interview
Pre-sales engineering
Role takeaway
- Fit
- Strong fit for policy/intake platforms and AI-assisted master-data reconciliation; weak as a standalone GenAI product reference.
- Architecture and integration
- Participative policy portal; AI-assisted data dictionary reconciliation; AI-augmented SOC detect/remediate with human approval.
- Prerequisites
- Published risk tiers
- Agency inventory of AI uses
- Data stewardship model
- Cyber playbooks for AI-assisted remediations
- Constraints
- No new KPIs in interview
- CT CART Act/privacy context
- Federal HR-1 program change windows
- Framework originated 2024
- Security
- AI-accelerated cyber tooling needs change-control and approval gates; framework principles must map to concrete DLP and logging.
- Proof of value
- Count of high-risk reviews completed
- Education completion metrics
- Before/after data-definition conflict samples
- MTTD/MTTR with AI assist vs baseline
Delivery
Role takeaway
- Work
- Refresh framework education, stand up intake checkpoints, run one data-definition reconciliation pilot, document cyber AI gates.
- Dependencies
- CIO sponsorship
- CDO/data stewards
- Agency SMEs
- Security operations
- Ownership
- State CIO with participative employee governance process; agencies own use-case risk acceptance.
- Skills and adoption
- Managers need risk-tier literacy; analysts need AI-assisted data-definition review skills.
- Governance checkpoints
- Intake for new AI uses
- Periodic framework revision with employees
- High-risk human sign-off
- Cyber automation approval
- Acceptance criteria
- Documented review cadence
- Published high-risk criteria
- Pilot metrics for data-definition reconciliation
- No unsupervised rights-affecting decisions
- Risks
- Policy theater without enforcement
- Double-counting CART Act
- HR-1 rush bypassing value metrics
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
Policy/governance layer preceding tool rollout; AI for cross-agency data definition reconciliation; AI-assisted vulnerability identification/remediation in cyber operations.
Governance
Who approves, reviews and stays accountable for outcomes?
Participative Responsible AI Framework as primary employee-facing control; CART Act/privacy rules already archived separately (Oct 1 effective). Framework dates to 2024—interview restates rather than introduces new statute.
Security and privacy
What data, permissions and controls need testing?
Cyber strategy shift toward automated detection/response and AI-assisted remediation; no chatbot data-handling controls in this interview.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Framework emphasizes employee education and safe engagement; accessibility of public services not quantified in interview.
Procurement
What should contracts, pricing and exit terms secure?
No vendor named; governance-first posture may slow tool purchases until intake/risk review completes.
Operating model
Which teams own the service once it runs?
Participative policy evolution with employees as co-owners of risk and education—not a centralized chatbot product team.
Publication history
- 2026-09-28State Government · Issue 235 resources
Stable resource ID: connecticut-digital-states-2026-raymond-responsible-ai-20260923