New Year, New Priorities: Resetting Focus, Strategy & Momentum for 2026
As public-sector organizations enter 2026, leaders face a different operating environment than even a year ago. Artificial intelligence has moved from experimentation into daily workflows, policy and governance expectations are tightening, and the pressure to deliver real outcomes has intensified. The pace of change has not slowed, but expectations around clarity, accountability, and execution have increased.
Leadership Connect’s January 29 webinar, New Year, New Priorities: Resetting Focus, Strategy & Momentum for 2026, brought together leaders from government, industry, and academia to explore how organizations are recalibrating their priorities in this moment. The discussion focused less on technology hype and more on leadership decisions, organizational readiness, and the practical realities of turning innovation into impact.
Below are the key themes that shaped the conversation, along with the implications public-sector leaders should carry into the year ahead.
Couldn’t attend the session live? Watch the whole webinar here and make sure to follow our Events Page to get in on the next conversation. Below are the key themes that shaped the discussion!
Moving beyond AI prototyping toward operational impact
A central theme of the discussion was the growing tension between experimentation and execution. While prototyping has played an important role in helping agencies explore AI capabilities, many organizations remain stuck in extended pilot phases. Panelists emphasized that experimentation without a path to deployment creates diminishing returns and erodes confidence among stakeholders.
What feels different about 2026 is not just improved technology, but increased urgency. Leaders are under pressure to demonstrate measurable outcomes, whether in efficiency, decision support, or mission delivery. The conversation highlighted that pilots should be treated as learning tools with defined exit criteria, not endpoints. Successful organizations are asking early how an initiative will scale, who will own it, and how it will integrate into existing operations.
Importantly, the discussion also reinforced that a high failure rate at the prototype stage is not inherently negative. Rapid learning in low-risk environments can be a strength, as long as lessons translate into better-designed implementations. The shift for 2026 is moving from “can this work?” to “are we prepared to operate this responsibly and sustainably?”
AI as a leadership and business strategy, not a technical initiative
Another recurring insight was that AI strategy has outgrown its traditional home within IT organizations. Panelists emphasized that AI decisions increasingly shape workforce structure, risk posture, procurement strategy, and mission outcomes. As a result, AI must be treated as a leadership and organizational strategy, not a standalone technology project.
This reframing places responsibility squarely on senior leaders. AI initiatives require clear alignment with mission priorities and an understanding of how technology changes decision-making and accountability. The discussion underscored that focusing too narrowly on tools or vendors can distract from the harder questions of readiness, governance, and cultural adoption.
In practice, leaders are being asked to define what success looks like beyond technical performance. That includes identifying which decisions should remain human-led, where automation adds value, and how AI fits into broader modernization efforts. Treating AI as infrastructure rather than novelty helps organizations prioritize investments that support long-term goals.
Who needs to be involved in transformation decisions
The panel strongly agreed that successful AI and modernization efforts depend on who is included in the conversation. While technical expertise is essential, limiting decision-making to engineers or data teams creates blind spots that surface later as delays, risk, or resistance.
The discussion highlighted the need for multidisciplinary participation from the outset. Legal, procurement, human resources, communications, and mission owners all play critical roles in determining whether an initiative can move from pilot to production. Frontline staff and domain experts were also identified as essential contributors, particularly in identifying edge cases, operational constraints, and real-world impact.
For public-sector leaders, this reinforces the importance of shared ownership. When stakeholders are engaged early, organizations are better positioned to manage risk, plan for adoption, and align incentives. In 2026, transformation efforts that remain siloed are increasingly likely to stall.
Workforce implications, trust, and communicating change
Workforce impact and trust emerged as some of the most complex challenges discussed. As AI reshapes how work is performed, uncertainty around roles, skills, and job security can quickly undermine adoption efforts. Panelists emphasized that resistance often stems less from technology itself and more from a lack of clarity about what is changing and why.
The discussion highlighted transparency as a leadership responsibility. Clear communication about goals, success metrics, and expected impacts helps reduce uncertainty and build trust. Leaders were encouraged to articulate where human expertise remains essential, how roles may evolve, and what support exists for reskilling and career development.
Rather than framing AI solely as a cost-saving tool, the conversation emphasized positioning it as a capability that augments decision-making and productivity. Organizations that invest in education, change management, and honest dialogue are better positioned to maintain morale and sustain momentum.
Governance, guardrails, and responsible AI use
Governance and trust were central to the discussion of AI at scale. Panelists stressed that responsible use depends on clear guardrails, accountability, and ongoing oversight. Without these foundations, AI systems risk becoming sources of operational and reputational risk rather than value.
The conversation emphasized that good governance should feel intuitive over time. Effective frameworks balance innovation with protection, enabling progress while reducing unintended consequences. Rather than attempting to anticipate every risk scenario, leaders were encouraged to define desired outcomes and build governance structures that support learning, monitoring, and adjustment.
At the organizational level, this includes policies around data use, model access, and human oversight. Treating AI as part of core infrastructure rather than a one-off deployment helps ensure that governance evolves alongside capability.
Policy and regulatory considerations shaping 2026
The panel also explored how policy and regulation continue to influence AI adoption. While approaches differ across jurisdictions, there was broad agreement that fragmented or inconsistent rules create challenges for both users and developers. Leaders must navigate existing regulations while preparing for evolving standards at the federal and state levels.
The discussion reinforced that organizations cannot wait for perfect regulatory clarity before acting. Instead, many are aligning internal practices with the most stringent applicable standards and established frameworks. Doing so provides a stable foundation for innovation while reducing compliance risk.
For public-sector leaders, this means staying informed, engaging policy stakeholders, and ensuring internal teams understand how rules translate into daily practice. Policy literacy is becoming an operational necessity rather than a specialized function.
Action-oriented takeaways for 2026
As leaders reset priorities for the year ahead, several practical takeaways emerged from the discussion:
First, focus on outcomes rather than activity. AI initiatives should be tied to clear mission goals, with defined measures of success and ownership.
Second, reassess what to scale, rethink, or sunset. Not every pilot deserves expansion, and legacy efforts that no longer support priorities should be phased out to make room for higher-impact work.
Third, broaden stakeholder alignment early. Multidisciplinary involvement reduces friction and accelerates adoption.
Finally, ask hard internal questions. Where does AI meaningfully support decision-making? What risks are acceptable, and which are not? How prepared is the workforce for change? These questions help leaders move from experimentation to execution with confidence.
Looking ahead
The discussion made clear that 2026 will reward clarity and discipline. AI and modernization are no longer emerging trends but ongoing leadership responsibilities. Organizations that succeed will be those that balance innovation with governance, move decisively while communicating transparently, and treat transformation as a continuous process rather than a single initiative.
Leadership in this environment requires focus, trust, and a willingness to adapt. By grounding decisions in mission outcomes and engaging people across the organization, public-sector leaders can convert complexity into momentum.
Continue the conversation
Watch the on-demand webinar to hear the full discussion and explore additional Leadership Connect resources for insights on public-sector leadership, technology, and policy. Stay connected to upcoming events as we continue convening conversations that help leaders navigate change with confidence.
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