Building Versus Buying AI Solutions in Healthcare: Trends, Customization Needs, and Strategic Implications for Enterprise AI Adoption

Healthcare is one of the top sectors using AI today, with $500 million spent on generative AI solutions in 2024 alone. This shows a shift from just trying out AI to using it for important tasks. AI is used for things like ambient AI scribes, automating clinical documentation, helping with triage, coding, and managing revenue cycles. Companies such as Eleos Health, Abridge, Ambience, and Notable provide AI solutions that help healthcare workers with their daily work.

The main reason AI is popular in healthcare is because it can lower the paperwork load for doctors, make documentation faster and more accurate, and automate simple tasks that take a lot of time. For example, Eleos Health has AI tools that summarize meetings and connect with electronic health records (EHRs). This allows healthcare providers to spend more time with patients instead of paperwork.

Build Versus Buy: A Strategic Question

Healthcare organizations face a big question: should they build their own AI tools or buy ready-made products from vendors? In 2024, about 47% of organizations build AI tools themselves, while 53% buy from outside vendors. This is a big change from 2023 when 80% mostly bought AI tools.

Several reasons affect this choice for healthcare groups in the U.S. These include:

• Cost: Making AI tools internally can be costly and needs special skills, technology, and time. Buying from vendors usually means paying licensing fees and ongoing costs.
• Speed to Market: Buying tools lets medical practices start using AI faster. Building takes longer to finish.
• Talent Availability: It’s hard to find people with skills in both AI and medicine. This makes building AI harder because of competition and high salaries.
• Customization Needs: Healthcare work is complex. Off-the-shelf AI may not fit all clinical or admin tasks. Customization is needed for better results.
• Scalability and Long-Term Maintenance: Custom AI tools need ongoing support and updates, which can strain internal teams. Vendor solutions may offer scalable platforms with outside support.

Why Buying AI is Favored for Foundational Tasks

Experts like those at Salesforce suggest buying common AI tools for basic tasks instead of building them. Phil Mui, Salesforce’s SVP of Engineering, says many groups find it hard to make AI models more than 80% accurate. Buying proven AI tools for simple jobs saves resources and lets teams work on more advanced projects.

Examples where buying AI may be better include:

• Routine documentation automation
• Basic clinical coding help
• Support chatbots for front desk or IT help
• Data extraction from medical records

These uses often have reliable vendor products. Medical practices can quickly start using these AI tools without trying a lot of fixes.

The Case for Building Custom AI Solutions in Healthcare

Even though buying AI tools is faster, many healthcare groups say custom solutions work better for their unique needs and privacy rules. Healthcare has strict rules about patient data privacy and security, such as HIPAA. AI must follow these rules carefully.

Experts like Raphael Ouzan, founder of A.Team, say a big advantage is “the last mile” of making AI tools fit specific needs of the organization.

Custom AI in healthcare helps by:

• Making workflows that work well with existing EHR and practice systems
• Creating automation for special documentation, like oncology or cardiology records
• Embedding data privacy and security at detailed levels
• Adding clinical decision support without interrupting doctor-patient time
• Improving coding accuracy for faster billing and revenue management

Using both vendor tools and custom-built AI lets practices take advantage of proven AI while also getting tailored features.

Customization and ROI Are Priorities Over Price

When picking AI tools, healthcare groups mostly care about measurable return on investment (ROI) and fitting the AI well to their work, not just the price. Studies show 30% of organizations focus on value-based results and 26% say fitting AI to workflows is very important. Only 1% say price is a key factor. This means accuracy and impact matter most.

Practices that use AI well with their workflows see benefits like:

• Less time spent on data entry and paperwork
• Better coding accuracy and faster billing
• Improved patient triage and appointment scheduling
• Stronger patient communication through AI-driven help at the front desk

These benefits save money and improve patient care.

AI and Workflow Automations in Healthcare Operations

AI-driven automation is helping make healthcare workflows smoother, especially for administrative and front-office tasks. Phone automation and answering services that use AI, like Simbo AI, show how this works in the U.S.

Phones are still important for patient contact but take up a lot of staff time with repeated questions and scheduling. AI answering services can handle many calls, answer questions about office hours, symptoms, insurance, and even schedule or change appointments.

Automation is also used in:

• Patient Intake: Automating data collection before visits to cut down paperwork and mistakes.
• Clinical Documentation: AI helps with transcription or summarizing doctors’ notes to save time.
• Coding and Billing: AI finds the right codes for services, which lowers claim denials.
• Revenue Cycle Management: AI helps follow up with patients and insurers to get payments faster.

New AI systems can perform complex tasks by themselves. About 12% of AI uses now have this advanced setup. This may mean some jobs need less human help in the future.

For healthcare groups in the U.S., using AI automation improves efficiency, compliance, patient satisfaction, and cost control. These are crucial in value-based care and under strict regulations.

Challenges in AI Implementation and Adoption

Many healthcare leaders are hopeful—72% expect AI use to grow soon—but there are still problems. Common reasons projects fail include:

• High Implementation Costs: 26% of stalled projects didn’t plan enough for integration and scaling expenses.
• Data Privacy Concerns: HIPAA and other rules affect 21% of projects, needing careful data security.
• Disappointing ROI: 18% do not get the expected returns, often due to poor fit or low user acceptance.
• Technical Issues: Errors or “hallucinations” in AI output worry 15% of organizations.

To handle these problems, providers must plan well for systems, staff, support, and rules. Using both build and buy approaches can help keep balance between new ideas and steady operations.

Infrastructure and Technical Trends Supporting Healthcare AI

Healthcare AI often uses designs like Retrieval-Augmented Generation (RAG), used by 51% of enterprises in 2024. RAG helps AI access and summarize clinical knowledge from big datasets like unstructured EHR notes, documents, and images. This is key for correct clinical decisions.

Many groups use multiple language models, three or more, to perform different tasks well. Also, Anthropic, a company focused on safe AI, doubled its enterprise share to 24% in 2024, challenging leaders like OpenAI.

Healthcare organizations need AI systems that can work on local servers, cloud, or edge devices. Companies like Dell support “bringing AI to the data” to lower costs of moving data and speed up processing.

The Talent Gap and Its Impact on Healthcare AI

A big challenge is the lack of workers who know both AI and healthcare well. This shortage makes salaries for such people two or three times higher than average. Small healthcare groups especially find it hard to build and keep advanced AI systems inside.

This shortage leads many healthcare groups to work with vendors and use hybrid strategies. This way, they get outside AI experts while using their own staff to manage AI rollout and clinical use.

Strategic Guidance for Healthcare Practices in the U.S.

Medical administrators and IT leaders should think about these points when adopting AI:

• Check core needs to decide which AI tools to buy and which to customize.
• Pick AI solutions that show clear ROI and fit with EHR and practice systems.
• Plan total costs, including systems, compliance, and ongoing support to avoid surprises.
• Consider mixing vendor AI with in-house work to balance speed and customization.
• Invest in training or partnerships to handle talent shortages.
• Check vendors’ ability to meet HIPAA rules, data security, and scale for long-term use.

AI use in healthcare is growing fast. By choosing between building and buying AI tools wisely, and focusing on solutions that fit their clinical and administrative work, U.S. healthcare providers can reduce staff workload, improve efficiency, and better patient care.