70% of Healthcare Companies Are Deploying AI-and Seeing Real ROI

How and Why 70% of Healthcare Companies Are Implementing AI

Healthcare and life sciences are moving from experiments to everyday execution with AI. A recent industry survey reports that 70% of organisations are actively deploying AI, up from 63% the year before, with clear returns in clinical, research, and operational work.

"The industry is also embracing open source software and AI models to tackle specific use cases, as well as exploring using agentic AI to speed knowledge retrieval and research paper analysis," says NVIDIA. That shift - from broad ambition to focused use cases - explains why ROI is showing up faster.

Adoption at a glance

Gen AI and LLM usage climbed from 54% to 69% in a year. Digital health leads the pack, with pharma, biotech, and medtech close behind.

• Digital healthcare: 78% active use
• Pharmaceuticals and biotech: 74%
• Medical technology: 70%

Even payers and providers are catching up, jumping from 43% to 56%. Clinical decision support is the top use case (42%), with medical imaging (38%) and administrative workflow optimisation (38%) close behind.

Where ROI is showing up

AI pays off fastest when it solves a specific job. In medtech, 57% report ROI from AI in medical imaging. In pharma and biotech, 46% report ROI in drug discovery and development.

"Scaling generative AI in healthcare starts with focusing on real clinical and operational problems, rather than the technology itself," says Dr Annabelle Painter, Clinical AI Strategy Lead at Visiba UK. John Nosta, President of NostaLab, echoes the operational priority: "The most visible and scalable impact of AI will come from logistics and administrative streamlining."

The business impact is material. Among management respondents, 85% say AI increased annual revenue and 80% report reduced annual costs. Notably, 44% saw revenue rise by more than 10%, with small companies benefitting most - 56% report revenue growth above 10%.

Agentic AI moves from idea to practice

Agentic AI - systems that can reason through tasks and take action - is moving into production. Forty-seven percent are using or assessing AI agents, and 22% have already deployed them.

• Top uses: knowledge management and retrieval (46%), literature review and analysis (38%), internal process optimisation (37%)
• Pharma and biotech: 55% use agents for literature review; nearly half for drug discovery tasks
• Open source matters: 82% say open-source models and software are moderately to extremely important to their strategy

Budgets are rising, but hurdles remain

Investment is set to grow, with 85% expecting bigger AI budgets and nearly half projecting increases above 10%. The priority now is scaling and smoothing operations, with 47% planning to optimise AI workflows.

• Smaller organisations: budget constraints (40%) and insufficient data for training (33%) lead the challenges
• Larger enterprises: data privacy and security dominate (39%), alongside governance and integration needs

Practical playbook: What to implement next quarter

• Pick two high-impact, measurable use cases. Proven bets: prior authorisation automation, discharge summaries, imaging triage, safety signal detection, clinical Q&A.
• Define hard metrics before you build. Examples: report turnaround time, denied-claim rate, model AUC/sensitivity, provider clicks per task, cost per case.
• Start with your data reality. For structured data, standardise and map to your models. For unstructured notes and PDFs, stand up retrieval-augmented generation with strict source citation.
• Choose the right model path. Use open-source models for fine-tuned, on-prem or VPC deployments where data control matters; consider hosted APIs for speed when PHI is de-identified and latency is critical.
• Bake in privacy and safety. Minimise PHI in prompts, enable audit logs, apply content filters, and run bias/performance tests across key patient cohorts.
• Prove value with a narrow pilot. Limit scope to one workflow and one specialty/site. Target a 4-8 week cycle with weekly checkpoints and pre/post metrics.
• Operationalise early. Set up role-based access, feedback loops for clinicians, model/version tracking, and clear escalation paths for failures.
• Make agents earn their keep. Start with literature review, policy lookup, or formulary search where agents can cite sources and automate summaries; add constrained action-taking only after reliability is proven.
• Level up imaging where ROI is strongest. Prioritise triage, segmentation, and quality control pipelines tied to throughput and reading time.
• Close the loop on finance. Route a portion of savings or revenue lift into the next phase of scaling to maintain momentum.

Executive takeaways

• Adoption is mainstream: 70% are deploying AI, with clear revenue gains and cost reductions.
• Focus beats breadth: narrow, well-defined use cases deliver faster ROI and stakeholder buy-in.
• Agentic AI is ready for knowledge-heavy work: start with retrieval and literature review before expanding to automated actions.
• Open source expands options: it enables fine-tuning for specialised clinical and research tasks under tight data controls.
• Governance is a growth enabler: privacy, security, and model oversight reduce risk and speed approvals.

Next steps and resources

If you need hands-on training to move from pilot to production, explore AI for Healthcare and best practices for autonomous workflows with AI Agents & Automation.

This shift is no longer theoretical. With targeted use cases, measurable metrics, and a clear operating model, AI is becoming a dependable part of clinical workflows, research pipelines, and day-to-day operations - and the organisations that execute now will compound those gains fastest.