Health plans are heading into 2026 with a mix of confidence and uncertainty. Regulatory pressure is rising, administrative costs remain high, and member and provider expectations keep increasing.
Deloitte’s 2026 US Health Care Outlook highlights three strategies healthcare leaders are prioritizing:
empower consumer health with digital experiences and technology
scale generative AI and agentic AI to modernize operations across all functions
join forces with other industries to unlock innovation and impact
At Productive Edge, we view this as validation of the shift we’ve been working on with payer clients for the last few years: moving from AI experiments to operational modernization.
But there’s a key detail that determines whether this shift succeeds or stalls.
In payer operations, the real bottleneck isn’t the AI model.
It’s the fact that critical business logic still lives in documents.
In most health plans, the “source of truth” for decisions lives in places like:
medical policies
clinical criteria
provider manuals
authorization guidelines
benefit documents
regulatory guidance
internal SOPs and job aids
And most of it is still stored and distributed as PDFs.
That creates a structural problem:
If your policies and criteria are not machine-readable, your workflow can’t be fully machine-executable.
So even if you introduce AI, you still end up with:
inconsistent decisions
slow turnaround times
heavy manual review
difficulty proving compliance
constant rework and escalation
This is why payers can run pilots forever and still struggle to scale.
When we talk about document types, we’re not talking about classification for its own sake.
The highest-value document type in payer operations is the policy.
Because policy documents contain:
the rules
the exceptions
the decision criteria
the rationale requirements
the documentation requirements
the “if/then” logic that drives outcomes
So the unlock is:
This means turning policy language into structured components like:
conditions
thresholds
required evidence
exclusions
timing constraints
dependencies (e.g., prior therapies tried, diagnosis confirmed)
Once rules are structured, you can lay them out into a decision model so the plan can:
apply the same logic every time
validate outcomes
identify gaps in evidence
support reviewers with traceable rationale
This is the missing link in most AI projects.
It’s not enough to recommend a decision.
You need to connect the logic to workflow steps like:
intake and triage
evidence gathering
criteria matching
routing and exception handling
documentation generation
communications
audit trail creation
That’s how you arrive at the same decision twice, in a predictable and compliant way.
And it’s where agentic AI becomes operational.
Deloitte calls out prior authorization as a major operational bottleneck and highlights the impact of new CMS requirements tightening timelines and increasing transparency.
This is exactly the kind of workflow where machine-readable policy and decision logic becomes essential.
Because prior auth isn’t just a document problem. It’s a rules problem.
Health plans are expected to be faster and more consistent, while also being able to prove:
decisions were grounded in policy
criteria were applied correctly
rationale was documented
processes were compliant
That combination (speed, repeatability, and compliance) is hard to achieve with manual review at scale.
A lot of AI talk in healthcare focuses on summarization and drafting. That’s useful, but it doesn’t change the operating model.
Agentic AI is different because it can execute across workflows.
For payers, that means AI agents that can:
ingest incoming documents (fax, portal, attachments, clinical notes)
identify what policy applies
map evidence to criteria
determine what’s missing
recommend next-best actions
route work correctly
generate compliant documentation
maintain traceability and audit trails
But none of that scales unless the policy logic is machine-readable.
Our focus isn’t simply “add AI to a process.”
It’s helping payers modernize workflows so decisions can be made consistently, predictably, transparently, and compliantly.
But we also recognize an important reality: not every payer wants (or needs) to tear down and rebuild their workflows from scratch.
That’s why we typically assess two paths with clients:
This approach keeps the current workflow structure in place, but adds AI to reduce friction and improve speed and quality, for example:
extracting evidence from documents
mapping evidence to policy criteria
automating documentation and communications
improving routing and exception handling
strengthening audit trails and compliance traceability
This approach redesigns the workflow end-to-end around machine-readable policy logic, decision models, and agentic execution. It’s best when current processes are too slow, inconsistent, or expensive to scale.
In both cases, the foundation is the same:
unlock rules from policy documents
formalize them into decision models
connect decision logic to workflow execution across systems
include human oversight where required
build governance and traceability from day one
That’s how you modernize utilization management and authorization operations without forcing a rip-and-replace transformation.
Deloitte is right: scaling generative AI and agentic AI is a 2026 priority.
But the practical payer takeaway is this:
Scaling agentic AI depends on converting policy documents into machine-readable decision logic.
That’s what makes decisions repeatable.
That’s what makes workflows predictable.
That’s what makes compliance provable.
And that’s what separates AI that demos well from AI that actually runs payer operations.
One more point Deloitte’s report reinforces indirectly: scaling AI isn’t just a technology challenge. It’s an operating challenge.
Most payers aren’t pursuing five use cases. They’re pursuing 50, 100, or more across UM, claims, service, care management, and compliance. Without a clear system for intake, assessment, prioritization, and governance, AI efforts quickly become fragmented, duplicative, and hard to scale.
Productive Edge uses a structured methodology for enterprise AI adoption that starts with intake and prioritization and leads into delivery of high-impact workflows like prior authorization and utilization management.
As part of this approach, we provide an AI Portfolio Manager tool that serves as a command center for enterprise AI adoption.
It helps payer teams manage the full lifecycle of AI initiatives, including:
use case intake
assessment and feasibility scoring
prioritization and roadmap planning
value tracking and governance
We include the AI Portfolio Manager in our engagements.
If you’d like, we can walk you through the tool and show how it helps payer organizations bring order to hundreds of AI ideas and focus execution on the few that actually drive measurable operational impact. Contact us to schedule a walkthrough.