AI has reached a turning point.
Most organizations have already experimented with AI tools, launched pilot projects, or introduced automation into portions of their operations. Yet many are discovering that technology alone is not enough to create lasting business value.
Increasingly, organizations are finding that success is not determined by AI tools alone, but by how effectively people, AI, automation, data, and systems work together across the enterprise. Leading organizations are rethinking how work gets done, building connected operating models that allow human employees and digital workers to work together toward shared outcomes. This shift is giving rise to what many are calling the Agentic Enterprise.
At the same time, labor shortages, rising operational complexity, growing customer expectations, and rapid advances in AI are forcing leaders to rethink how their organizations operate. The conversation is moving beyond individual technologies and isolated use cases toward a larger question: how do you create an organization that can continuously adapt, improve, and scale?
As we move through 2026, four trends are emerging that help answer that question. From digital workforce strategies and process orchestration to enterprise knowledge and operational transformation, these are four takeaways every leader should understand about the state of the Agentic Enterprise in mid-2026.
The State of the Agentic Enterprise in Mid 2026: 4 Takeaways to Know
1. The Labor Crisis Is Accelerating the Shift to the Digital Workforce
The most immediate catalyst driving digital workforce, automation, and AI adoption is necessity, and for many industries, this has been spurred by severe labor shortages and workforce burnout. Recent workforce trends underscore the urgency of this challenge:
- Public Sector Struggles: What do you do when you have jobs that serve a vital purpose, but which virtually nobody wants due to their repetitive, tedious nature? The public sector is feeling this strain, with over 50% of state and local governments reporting difficulty filling certain open positions and keeping them filled, impacting critical functions that serve the public.
- Manufacturing Gap: The manufacturing sector in America alone is projected to face 2 million unfilled jobs by 2030 due to a significant skills gap and extreme difficulty finding potential candidates.
- Healthcare Burnout: Almost 50% of healthcare workers are burned out, and this is largely due to the high volumes of administrative tasks and paperwork they must complete, which sometimes keep them away from the more fulfilling parts of their jobs: face-to-face patient interactions.
These challenges are being alleviated by intelligent automation and AI, from automating the tedious parts of jobs that once made them undesirable to using AI and automation to reduce the volume of administrative tasks that healthcare professionals are burdened with.
Consequently, 92% of companies plan to increase their spending on automation over the next three years, reflecting the rapid pace at which the market is evolving.
Leading organizations are not pursuing AI to reduce headcount. They are creating digital workforce capacity that allows employees to focus on judgment, relationships, innovation, and exception handling while digital workers take on repetitive operational work. As a result, AI initiatives are increasingly being evaluated based on their ability to create organizational capacity and improve outcomes rather than simply automate tasks.
2. The Shift to End-to-End Process Orchestration
The evolution of intelligent automation tools means moving past simple, task-based automation toward complete end-to-end process orchestration.
You’ve probably heard of UiPath, a company that originally rose to industry leader status through its Robotic Process Automation (RPA) technology, which mimics humans on a desktop to complete rule-based tasks. Today, UiPath, who Naviant now partners with, provides a platform containing a wide variety of capabilities, including RPA, AI, and IDP.
This evolution in technology is allowing organizations to think about processes that span multiple steps and applications, rather than just the simpler, rule-based tasks RPA was limited to.
Many organizations have accumulated disconnected AI tools, automations, and point solutions over time. The challenge is no longer finding new tools. The challenge is connecting them into a governed operating model that can scale. This is why process orchestration is becoming so important. Rather than optimizing isolated tasks, organizations are orchestrating entire workflows across people, AI, automation, systems, and data.
For example, a process might involve intake, data lookup, research, and analysis across numerous applications and systems. The UiPath platform provides the means to interact with these systems using a combination of back-end APIs (for data connections) and UI interaction (acting like a human user).
By orchestrating these technologies together, organizations can automate highly manual, cyclical workflows and integrate different applications into one seamless process.
The future belongs to organizations that build enterprise-wide capabilities instead of collections of disconnected point solutions.
3. AI Used to QA and Upskill the Human Workforce
While it’s true that some jobs that largely consist of tedious, rule-based tasks will be taken by AI, most will be augmented with AI, not completely overtaken. One example of this approach comes from a large Florida county court was struggling with high turnover among court clerks. The trouble was, it took a year or more of tenure before these clerks could fully master the complex docketing process.
To solve this problem, the county decided to use AI not as a replacement, but as a quality control and training tool. Under this plan, the human clerks continued doing the work, but the new AI system now acts as a Quality Assurance (QA) layer. If the AI flags an entry as incorrect, the human-in-the-loop system accelerates the clerk’s knowledge by teaching them what they did wrong from a docketing perspective. This strategy addresses the staffing issue by ensuring greater accuracy and accelerating employee knowledge, while mitigating the negative connotations associated with AI implementation by showing the tool’s efficacy as a teacher and assistant rather than replacing an employee entirely.
The most mature organizations are moving beyond the question of whether AI can complete a task. Instead, they are deciding which work should belong to people, which work should belong to digital workers, and how those responsibilities should be governed over time.
Agentic organizations intentionally design how humans and AI work together rather than treating AI as a standalone tool.
4. Enterprise Knowledge Is Becoming a Competitive Advantage
Organizations cannot build effective agentic workflows when critical information remains trapped in paper documents, disconnected systems, spreadsheets, or institutional knowledge.
The most successful organizations are making enterprise knowledge accessible, structured, and usable, creating the foundation that AI agents, automation, and employees can all work from. This foundation helps organizations reduce friction, improve decision-making, and create more consistent outcomes.
Kent County, Michigan provides a compelling example of this challenge in action. As Michigan’s fourth most populous county, Kent County’s court system was burdened by massive amounts of paperwork associated with criminal, civil, probation, and juvenile cases. Documents constantly moved between clerks, judges, and magistrates, creating a “Where’s Waldo” scenario where critical information was difficult to locate when it was needed.
To address the problem, the county launched an initiative called “Operation Paperless,” with the goal of making every piece of case information electronic and automating related processes through workflows.
Naviant partnered with the county to implement Hyland’s OnBase WorkView, a case management solution that replaced spreadsheets and Access databases by bringing critical information together in one place.
The results were immediate. The initial rollout processed 784 filings and achieved a 59% throughput rate, exceeding the 50% industry benchmark. This success created a repeatable framework that the county plans to scale across additional groups and departments.
Beyond reducing paper, initiatives like this demonstrate a broader principle: organizations create more value when information is accessible, connected, and usable across the enterprise. As AI, automation, and digital workers become more capable, the quality and accessibility of enterprise knowledge increasingly determine how much value those technologies can deliver.
Bonus Tip: The Organizations Winning with AI Have a Blueprint
One of the most common reasons AI initiatives stall is the absence of a clear strategy.
Organizations achieving sustainable results are approaching transformation through three critical disciplines:
- Strategic Blueprint: determining where AI and automation create measurable value before building.
- Enterprise Architecture: creating a governed foundation rather than accumulating disconnected tools.
- Continuous Optimization: continuously measuring, improving, and expanding successful initiatives.
Act Now
The organizations gaining the greatest advantage from AI are not simply deploying more technology. They are redesigning how work gets done.
The shift toward the Agentic Enterprise is about more than AI adoption. It is about building an organization where people, AI, automation, data, and systems work together to continuously improve outcomes, create capacity, and deliver measurable business value.
