The exponential advance of AI opens the way for a new operating model for government entities in areas such as economic affairs, trade, and infrastructure. This AI-first model creates a once-in-a-generation opportunity to reset the operating baseline, transforming policy effectiveness and operational efficiency.
AI addresses two central challenges these departments face, delivering:
- Faster decision making, guidance, and coordination. These capabilities are essential amid geopolitical uncertainty and policy changes.
- Transformed day-to-day interactions with key stakeholders, such as citizens and businesses. As services in the private sector become seamless and responsive through AI, government departments must keep pace or risk being seen as out of touch.
These benefits, however, can be unlocked only by a bold, ambitious, vision that reshapes workflows and invents new ways of working with AI. In turn, that requires strategic direction from the top, driving cultural and organizational change.
Faster, More Coordinated Policy Responses
Recent advances mean AI can now process large amounts of data across functions.
The technology can also be embedded into decision making using autonomous AI agents, whose activities are orchestrated by other agents, with human oversight and intervention when needed. Within a few years, the AI-first economic affairs department could use agentic AI to make better, more integrated policy decisions in response to a shock, such as a disruption to critical energy imports. The agents would:
- Observe. The disruption would be immediately identified by agents that scan key economic inputs. The potential disruption would then be passed to other agents that would identify sectors at risk (including second- and third-level impacts) and ways of reducing energy demand.
- Process. The output from these agents would be fed into planning agents, which would frame key uncertainties and set priority workflows. Leadership assistant agents would frame the decision, set priorities, and calculate tradeoffs.
- Build action plans. Agents for each portfolio would then draft policy actions, such as new regulations to reduce energy use or support the most-affected sectors.
- Consolidate and decide. Portfolio-level outputs would be consolidated into a decision pack with the help of a leadership decision agent, combining options, impacts, and red lines. These would be presented to policymakers for action.
- Create the feedback loop. A feedback and learning agent would track outcomes and update playbooks. The leadership assistant agent would separately provide feedback for leadership to ensure better decision making in the next cycle.
These orchestrated AI agents could create policy responses to fast-changing external events far faster than human workflows, even when those humans have AI assistants.
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The Models for AI: Deploy, Reshape, and Invent
Three models drive value on the path to being AI-first.
Deploy. The most common model, using off-the-shelf, copilot-type products or lightly configured off-the-shelf tools to improve productivity and consistency without changing core processes embedded in traditional department workflows.
In the UK, an innovation agency built an AI-enabled grant integrity platform, mostly using commercial tools. The platform screens applications, flags duplicates and suspicious submissions, and routes complex cases to assessors with the most relevant expertise. Decision authority remains with human panels, but the system significantly reduces processing time and administrative costs.
Reshape. The real benefits emerge at this stage, when senior leaders drive the introduction of new workflows and processes redesigned around continuously running AI agents that hand off to humans at the appropriate moment.
For example, AI agents can assist citizens and businesses through complex license or permit applications. The agent can create a first-time-right plan that outlines which data is needed first and the critical path. After approval, another agent can provide extra guidance and verify compliance with conditions.
For citizens, this sends the signal that the government is working for them. For businesses, execution speed improves, and the government department gains a pro-business reputation that could help attract investment.
Invent. The most powerful benefits are delivered at this stage, when the department migrates largely to an AI-powered operating model. This operating model draws on AI developments that are firmly in view over the next three years. Our earlier example, of an AI-powered response to an energy shock, shows the benefits for internal decision making, but it can also drive significant improvements in public-facing services, creating processes so friction-free that they create “invisible government.”
Becoming an AI-First Department
Senior managers should not look at these as evolutionary stages to be followed in sequence. Instead, the first step in developing an AI-first mindset is deciding which model to apply to each key workflow. In some cases, cost-efficient use of the “deploy” model is appropriate; in many others, the more ambitious “invent” model delivers such significant public policy benefits that extensive investment is more than justified.
The second step involves three parallel processes:
- Launch lighthouse projects. Roll out approved copilot-type tools quickly. Soon after, launch “lighthouse” projects to small groups: quick, visible initiatives that show the way for AI innovation. These projects should be at scale in three months.
- Create the technical enablers. Build a secure, reusable AI platform with governed data access and shared APIs. This should include data discipline and traceability with functions such as audit logging.
- Build the organizational and people enablers. Assign project owners to drive accountability and facilitate workflow design. An AI enablement function can be the engine that speeds implementation. Build a delivery ecosystem with upskilled staff, codified data sharing, and interoperability with partners such as academics and industry.
Governments that build AI-first economic affairs departments will gain more than efficiency. Decision making can be improved structurally, with real-time reporting and always-on economic steering. They will gain the institutional agility to respond to economic shocks and trade disputes in real time. In this era of constant disruption, that capability will become a decisive advantage.
