genai Archives - Digital Health Wire

Healthcare can cover a lot of ground when it’s moving at the speed of AI, and a new report from McKinsey found that the AI conversation is quickly shifting from proof-of-concept to proof-of-return.

The analysis was based on a survey of U.S. healthcare execs spanning payors, providers, and health services/technology groups.

AI adoption is skyrocketing at all of them. For the first time since McKinsey began tracking the metric in 2023, the orgs that have already implemented GenAI outnumbered those that haven’t.

Half of respondents have deployed at least one GenAI use case at their organization, up from just 25% two years ago. Here’s a nice graphic on AI adoption by org type.

McKinsey found that leadership teams are no longer questioning whether and where GenAI is relevant, they’re focusing on how it can be used responsibly at scale.

Agents are also building momentum. Despite being the new kid on the AI block, 19% of orgs reported that they’ve deployed agentic AI capabilities.

That’s not a huge percentage considering all the new agents we’ve been covering, but another 51% of orgs are actively pursuing agentic AI proofs-of-concept.

Administrative efficiency is the priority. This chart breaks down the areas that respondents see the most potential for GenAI and multiagent systems.

87% ranked administrative efficiency as their leading GenAI use case.
76% said it was also their top priority for multiagent systems.
Software infrastructure and engagement trailed as distant contenders for both categories.

Adoption varies by org type. Here’s the overview.

Providers are leaning in on clinical productivity (54% are using GenAI to help).
Payers are prioritizing administrative efficiency (34%).
Health services and tech firms are using GenAI as software infrastructure (52%).

Adoption barriers had more overlap. Across all org types, the chief concerns with GenAI were difficulty integrating with existing workflows, risk/liability, and inaccuracies/bias.

The other shared belief? Nobody implements AI for fun. Everyone expects an ROI.

The Takeaway

AI has arrived in a big way, and McKinsey’s report confirmed that ROI is now the name of the game in every corner of the industry.

It only takes one look at the key findings from MIT’s GenAI Divide report to see why it made such a big splash this week: 95% of GenAI deployments fail.

MIT knows how to grab headlines. The paper – based on interviews with 150 enterprise execs, a survey of 350 employees, and an analysis of 300 GenAI deployments – highlights a clear chasm between the successful projects and the painful lessons.

After $30B+ of GenAI spend across all industries, only 5% of organizations have seen a measurable impact to their top lines. Adoption is high, but transformation is rare.

While general-purpose models like ChatGPT have improved individual productivity, that hasn’t translated to enterprise outcomes. Most “enterprise-grade” systems are stalling in pilots, and only a small fraction actually make it to production.

Why are GenAI pilots failing? The report suggests that it’s not the quality of the models, but the learning gap for both the tools and the organizations that’s causing pilots to fail.

Most enterprise tools don’t remember, don’t adapt, and don’t fit into real workflows. This creates “an AI shadow economy” where 90% of employees regularly use general models, yet reject enterprise tools that can’t carry context across sessions.

Employees ranked output quality and UX issues among the biggest barriers, which both directly trace back to missing memory and workflow integration.

What’s driving successful deployments? There was a consistent pattern among organizations successfully crossing the GenAI Divide: top buyers treated AI startups less like software vendors and more like business service providers. These orgs:

Demanded deep customization aligned to internal processes and data
Benchmarked tools on operational outcomes, not model benchmarks
Partnered through early-stage failures, treating deployment as co-evolution
Sourced AI initiatives from frontline managers, not central labs

There’s always a catch. Most of the pushback on the report was due to its definition of “failure,” which was not having a measurable P&L impact within six months. That definition would make “failures” out of everything from the internet to cloud computing, and underscores why enterprise transformation is measured in years, not months.

The Takeaway

The GenAI growing pains might be worse than expected, but that’s helped startups realize that they need to ditch the SaaS playbook for a new set of rules. In the GenAI era, deployment is a starting line, not a finish line.

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