Artificial intelligence is rapidly moving from experimentation to enterprise investment across the health care industry. The next challenge for executives is proving that those investments are actually creating value.
That is the central message of Deloitte’s recent report, “Momentum isn’t a metric: Many healthcare CFOs report they need clearer proof of AI value.” Deloitte surveyed 64 U.S. health care chief financial officers and finance leaders, evenly divided between health plans and health systems. The research found a notable disconnect between organizations scaling AI and their ability to financially measure the results.
For payer executives responsible for provider networks, the findings raise an important question. As AI becomes part of network intelligence and provider network contracting, how will health plans know whether it is actually improving performance?
AI Adoption Is Moving Faster Than Financial Measurement
Deloitte classified 44% of the surveyed organizations as “AI scalers,” meaning more than one-third of their generative AI initiatives launched over the previous two years had reached scaled deployment across multiple functions. Yet only 18% of those AI scalers reported mature financial attribution capabilities. Among organizations earlier in their AI journey, that figure was 31%.
In other words, some of the organizations moving fastest are not necessarily the ones best equipped to demonstrate financial results.
Confidence remains high. Deloitte found that 75% of AI scalers planned to increase generative and agentic AI investment during the following 12 months. At the same time, 85% expected their AI investments to break even in less than five years.
For payer leaders, optimism about AI should be paired with a clear definition of what success looks like.
A technology implementation is not a business outcome.
Provider Network Contracting Offers a Measurable AI Use Case
Provider network operations generate enormous amounts of information. Claims data, provider information, reimbursement rates, utilization patterns, and market benchmarks can all influence contracting decisions.
The challenge is turning that information into action.
TOG Network Solutions uses network intelligence to help payers examine the economics and performance of their health care provider networks. TOG’s approach connects analytics and market information with practical decisions involving contracting, access, and network strategy. TOG describes network intelligence as a way for insurers to evaluate market opportunities, negotiate effective provider rates, and optimize profitability while supporting quality care.
AI can strengthen this process, but the technology itself should never become the goal.
For example, a payer using AI to analyze contract and claims data should be able to determine whether that analysis helped identify meaningful unit cost variation. If technology highlights an opportunity for renegotiation, the organization should eventually be able to compare the recommendation with the financial result.
That creates a much stronger value proposition than simply measuring how many analyses an AI platform completes.
Start With the Business Question
Deloitte recommends establishing how success will be defined and measured before expanding AI initiatives. The report suggests that organizations identify baselines, assign ownership of key performance indicators, and connect operational improvements with a defined financial hypothesis.
That principle is particularly relevant to provider network contracting.
Before deploying AI, payer executives can begin with a specific business problem:
Where is reimbursement significantly above the competitive market?
Which contracts are contributing disproportionately to total medical expense?
Where could a network change create an access problem?
Once the question is clear, technology can help accelerate the analysis. The resulting decision can then be measured against a financial or operational baseline.
This keeps AI focused on solving problems rather than simply generating more information.
Data Quality Still Determines the Quality of the Answer
AI can process information quickly, but speed does not compensate for unreliable provider data.
A health plan cannot confidently use advanced analytics to evaluate its network if provider participation, contract information, or other underlying data is fragmented or outdated.
This is especially important in provider network contracting because decisions are interconnected. A payer may identify an expensive provider contract, but changing that agreement without understanding its impact on network adequacy could create a different business problem.
TOG’s network intelligence model brings provider and payer information together to support a more complete view of network performance.
The value of AI grows when the underlying data gives executives confidence in the decisions that follow.
TOG’s Perspective: Measure Decisions, Not AI Activity
Deloitte’s research suggests that the health care industry is entering a new phase of AI adoption. Investment alone will no longer demonstrate progress. Organizations will increasingly need evidence that technology is contributing to measurable results.
For provider network executives, that creates an opportunity to establish practical measures of AI value.
Three Questions Payers Should Ask
- What provider network problem is the technology solving?
- Can the outcome be connected to financial or network performance?
- Is the underlying network data reliable enough to support the decision?
AI has the potential to make provider network management faster and more informed. The business case becomes much stronger when payers can show how those capabilities improve actual decisions.
For payer executives, the next stage of AI maturity should not be measured by how much technology is deployed. It should be measured by whether better network intelligence leads to better provider network contracting and more sustainable financial performance.