Pennsylvania Expands Generative AI for State Employees with Copilot and ChatGPT Enterprise

Pennsylvania is moving from pilot to purchase order: Governor Josh Shapiro told more than 900 technology, academic and business leaders at the AI Horizons Summit in Pittsburgh that the commonwealth will expand access to advanced generative AI tools for qualified state employees — adding Microsoft Copilot to an existing ChatGPT Enterprise rollout and wrapping the expansion with training, oversight and economic development commitments. (pa.gov)

Background​

The Shapiro administration’s effort builds on a year-long, state-run pilot with OpenAI’s ChatGPT Enterprise that began in January 2024. That pilot — run by the Office of Administration in partnership with Carnegie Mellon University and OpenAI — involved roughly 175 employees across 14 agencies and produced headline metrics the administration now uses to justify wider deployment. Participants reported large perceived time savings and broadly positive experiences during the pilot. (pa.gov)
Pennsylvania’s announcement at the AI Horizons Summit frames the work as part of a three‑pronged strategy: increase government productivity, protect citizen data and build the state’s AI economy through public‑private partnerships and workforce training. The administration also points to an independent assessment that ranks Pennsylvania among the top three U.S. states for AI readiness. (pa.gov)

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What the state announced at AI Horizons​

• Commonwealth employees who qualify will be given access to a vetted suite of generative AI tools: continuing ChatGPT Enterprise access and adding Microsoft Copilot Chat as part of the new, expanded offering. The administration called this “the most advanced suite of generative AI tools offered by any state,” a characterization rooted in its dual‑vendor approach. (pa.gov)
• The rollout will be accompanied by official governance structures: the continuation of the Generative AI Governing Board (established by Executive Order 2023‑19), the creation of a Generative AI Labor and Management Collaboration Group to involve unions and workers, and mandatory training for employees who use the tools. (pa.gov)
• The summit also included new industry and academic commitments: a five‑year, $10 million research partnership between BNY (Bank of New York) and Carnegie Mellon University to found the BNY AI Lab focused on governance and accountability, and a Google‑run AI Accelerator aimed at bringing free training and tools to Pennsylvania small businesses. (bny.com)

These moves represent a shift from experimental pilots toward a managed, enterprise‑grade deployment model with explicit workforce engagement and external research funding. The administration positions the work both as operational modernization and as economic development. (pa.gov)

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The pilots and the numbers: what happened, and how to read the claims​

The “95 minutes a day” headline​

The most attention‑grabbing figure from Pennsylvania’s pilot is the reported average time savings: employees who used ChatGPT in the pilot said they saved 95 minutes per day on tasks such as drafting emails, summarizing long documents, researching policy, and basic coding assistance. That figure comes from the state’s exit surveys, interviews and structured feedback collected during the pilot and has been repeated by the governor, Carnegie Mellon University and state press materials. (pa.gov)
Independent public‑sector reporting also examined the pilot methodology and results: coverage in industry outlets notes the pilot’s mixed methods (surveys, telemetry, interviews) and highlights that while reported time savings were large, outputs frequently required human verification and editing — a common reality for today’s generative models. In short, the 95‑minute figure is a self‑reported, pilot‑derived metric that indicates substantial perceived productivity gains, but it does not substitute for an independent, audit‑level productivity study across all state roles. (govtech.com)

How the administration’s economic claims compare​

At the summit, the administration reiterated that its economic strategy — which includes AI and energy investments — has helped the state attract major private commitments. The administration’s materials around the event cite more than $25 billion in private‑sector commitments and roughly 11,000–12,400 new jobs since the governor took office, though the exact totals differ slightly across press releases and departmental pages. Some official pages list the total as about $25.2 billion and nearly 11,000 jobs, while other briefings and summit materials cite $25.6 billion and 12,400 jobs. Those differences appear to reflect fast‑moving announcements and aggregated reporting windows; readers and procurement officials should treat headline totals as rolling figures that are best verified against the Department of Community & Economic Development’s project database for any specific project or job claim. (dced.pa.gov)

“Most advanced suite of generative AI tools offered by any state” — a state claim, not an independent rating​

The administration’s characterization of Pennsylvania’s toolset as the “most advanced” among U.S. states is an aspirational, comparative claim based on the state running both ChatGPT Enterprise and Microsoft Copilot in government contexts. That phrasing is best understood as a promotional positioning: there is no single, objective checklist published by an independent authority that ranks every state by the exact combination of vendor products, tenancy models, governance guardrails and training programs. The claim therefore stands as a defensible marketing statement by the commonwealth, but one that should be treated as the state’s self‑assessment rather than an independently audited fact. (pa.gov)

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Technical and procurement details: what IT leaders need to know​

Which products, and what they mean in practice​

• ChatGPT Enterprise: a commercial OpenAI offering that provides tighter administration controls, stronger data protections, and enterprise management features compared with public consumer accounts. Pennsylvania’s pilot used ChatGPT Enterprise to limit training reuse of state data and to apply administrative controls over access and usage. (pa.gov)
• Microsoft Copilot Chat / Microsoft 365 Copilot: an assistant integrated across Office apps that can summarize email threads, draft documents, generate slides and automate repetitive tasks inside Word, Outlook, PowerPoint, Excel and Teams. Deploying Copilot in government environments typically involves Microsoft’s secure tenancy options (including Azure Government or GCC equivalents) and enterprise governance controls like Purview classification, Data Loss Prevention policies and audit logging. The addition of Copilot gives state staff a productivity assistant that is deeply integrated into the Microsoft 365 workflow many agencies already use. (pa.gov)

Both product classes emphasize “human‑in‑the‑loop” usage: models assist with drafting and analysis, but final decisions and official documents remain the responsibility of trained employees and reviewers. That approach is consistent with the executive order’s principles for accuracy, transparency, privacy and human oversight. (pa.gov)

Security and compliance checklist (practical)​

1. Classify data and apply sensitivity labels before you permit AI access.
2. Route high‑sensitivity and controlled unclassified information (CUI) only through cleared tenancy (e.g., Azure Government or equivalent).
3. Enable robust audit logs, retention policies and eDiscovery to support transparency and FOIA responses.
4. Deploy least‑privilege access and phishing‑resistant MFA for accounts that can prompt Copilot or ChatGPT.
5. Require prompt provenance logs and mandate human verification steps for legal, benefits, licensing or safety‑critical outputs.

These measures are consistent with best practices seen in other public‑sector pilots and federal guidance: tool integration must be combined with data classification, DLP enforcement and well‑documented review processes to limit risk.

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Governance, labor and oversight: the administration’s approach​

The Shapiro administration is explicit about worker involvement. The new Generative AI Labor and Management Collaboration Group is designed to give unions and employees a formal voice in how AI is introduced across roles, an arrangement intended to reduce resistance and to design augmentative workflows rather than wholesale replacements. That aligns with the governor’s public line that AI is a “job enhancer, not a job replacer.” (pa.gov)
Governance also rests on the existing Executive Order (2023‑19) that established a Generative AI Governing Board and codified principles of accuracy, privacy, equity and transparency for state AI use. The governing board is responsible for policy, vetting vendor contracts, and approving expansion plans — a model that puts central control in the Office of Administration while allowing agencies to pilot specialized use cases under established guardrails. (pa.gov)
This hybrid approach — strong central policy plus worker collaboration and agency‑level pilots — is increasingly common among states that have moved beyond exploratory skunkworks into enterprise deployments. Independent assessments have rewarded states that pair policy with capacity building, and Code for America’s Government AI Landscape Assessment highlighted Pennsylvania as an “advanced” state in leadership and capacity building. (codeforamerica.org)

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Partnerships and ecosystem building​

BNY‑CMU: $10 million for an AI lab focused on governance​

BNY and Carnegie Mellon announced a five‑year, $10 million collaboration to establish the BNY AI Lab at CMU’s School of Computer Science. The lab will concentrate on governance, trust and accountability for mission‑critical AI — an investment that both advances academic research and creates a local pipeline of applied expertise for financial and government systems. The announcement was carried by both the institution and national press outlets. (bny.com)

Google’s AI work and the small‑business accelerator​

Google announced a statewide accelerator and training effort for Pennsylvania small businesses tied to the summit, part of a broader commitment to workforce and infrastructure investment in the region. Google’s outreach materials and state press briefings describe free training and toolkits aimed at helping entrepreneurs reduce costs and scale operations with AI: a classic public‑private skills initiative that couples vendor expertise with SME support. (blog.google)
Taken together, these partnerships — academic, corporate and governmental — form the scaffolding for a regional AI cluster: research funding, skilling programs and vendor partnerships that both accelerate public deployments and build private‑sector opportunity around the state. (bny.com)

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Risks, limitations and the accountability imperative​

No public‑sector AI rollout is risk‑free. The Pennsylvania pilot and subsequent public commentary surfaced several recurring concerns that any state must manage proactively:

• Accuracy and hallucinations: generative models can make confident but incorrect assertions. Human verification is non‑negotiable for legal, medical, or benefits decisions. The pilot explicitly emphasized the need for human oversight and additional verification steps. (pa.gov)
• Privacy, FOIA and data residency: inputs and outputs may be subject to public records laws. Contracts must explicitly define retention, exportability, training reuse restrictions and vendor obligations for FOIA responses. Agencies must classify data before permitting any AI interaction.
• Vendor lock‑in and portability: heavy dependence on a single cloud or assistant risks long‑term lock‑in. Procurement should require data egress clauses, measurable SLAs and clear audit rights. Federal and municipal pilots have repeatedly underscored this as a governance priority.
• Equity and bias: models trained on imbalanced data can produce biased outputs. Regular fairness testing, diverse red‑team reviews and publicly reported audit results are needed to maintain trust. (govtech.com)
• Workforce impacts and reskilling: while the administration frames AI as augmentative, role redesign will be required. The Labor and Management Collaboration Group is a step toward equitable transition, but robust retraining, redeployment plans and measurable outcomes will be necessary to keep the promise. (pa.gov)

These hazards are not hypothetical; they are the operational realities seen in federal and international pilots. Good governance is therefore not optional — it’s the central variable between productive modernization and a public relations misstep. (govtech.com)

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Practical steps for state and local IT teams​

For CIOs, procurement leads and digital services teams planning similar rollouts, the Pennsylvania experience offers a practical checklist:

1. Start with a short, instrumented PoV (proof of value) focused on a few high‑impact, low‑risk workflows.
2. Document baseline metrics (AHT, throughput, error rates) so claimed savings can be quantified rather than self‑reported alone.
3. Mandate human‑review thresholds and require versioned prompt logs for auditability.
4. Build training programs with clear competency goals (prompt engineering, verification, privacy hygiene).
5. Negotiate contracts with portability, audit rights, and explicit non‑training clauses if you cannot allow vendor retraining on sensitive data.
6. Coordinate with unions and human resources to design role‑redesign pathways and reallocation of saved capacity to higher‑value public services.

These steps mirror recommendations in federal and state evaluations and respond directly to common pitfalls seen in other Copilot and generative AI pilots.

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What to watch next​

• Execution: moving from pilot to broad deployment is an operational challenge. The state must deliver on training, DLP enforcement, tenancy configuration, and centralized auditing to make the admin’s claims credible at scale. (pa.gov)
• Measured outcomes: independent third‑party audits and open, repeatable metrics will be crucial. The 95‑minute figure is compelling, but wider adoption warrants longitudinal measurement across functions, not only exit surveys. (govtech.com)
• Procurement posture: watch contract language for portability and data‑use limitations. States that swallow commercial convenience risk longer‑term constraints on policy, cost and sovereignty.
• Public transparency: to sustain public trust, the state should publish red‑team results, governance minutes and annual transparency reports detailing deployments, incidents, and outcomes.

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Conclusion​

Pennsylvania’s announcement at the AI Horizons Summit is a significant, carefully staged example of how a U.S. state can move from experimentation to enterprise adoption with generative AI. The administration paired tools (ChatGPT Enterprise and Microsoft Copilot), governance (executive order, governing board, labor collaboration), and ecosystem investments (BNY‑CMU lab, Google training programs) to create a coherent narrative about productivity and economic growth. (pa.gov)
The pilot’s headline metric — an average reported time savings of 95 minutes per day — is impressive and supported by state and university reporting, but it should be interpreted as pilot‑level, self‑reported evidence rather than a definitive audit of system‑wide productivity. The administration’s broader economic and readiness claims are backed by third‑party assessments (Code for America) and multiple corporate commitments, but some headline economic totals vary across official pages and should be verified at the project level for procurement and budgeting decisions. (pa.gov)
For other public organizations watching closely: Pennsylvania’s playbook offers a balanced path — combine focused pilots, workforce engagement, robust procurement safeguards and independent research partnerships — but success will be measured in disciplined execution, transparent metrics and relentless attention to privacy, equity and oversight.

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Source: fox43.com https://www.fox43.com/article/news/local/shapiro-pennsylvania-expands-generative-ai-tools-state-workers-ai-horizons-pittsburgh/521-49beaf97-1f77-41f2-bdbf-b3c0d3b33519/