Most U.S. companies already use AI agents or plan to increase their use. A 2025 PwC survey of 308 U.S. executives found that almost 79% said their companies use AI agents, including in healthcare where front-office tasks and patient contact are common targets for automation. Also, 88% of those surveyed plan to spend more on AI in the next year. This shows many believe AI helps improve work and stay competitive.
Still, how much AI is used daily varies a lot. Only 68% of companies said half or fewer workers use AI agents every day. This means many are still learning how to use AI. Among the companies using AI widely, 66% say they see better productivity, 57% say they save money, and over half say they make decisions faster and improve customer or patient experiences.
In healthcare, staff spend much time on tasks like scheduling, checking insurance, and answering patient questions. AI agents like Simbo AI’s phone services can take over these routine jobs. This helps staff focus more on patient care. But to grow these solutions well, organizations must change their workflows and plans, which is often missed.
Even though early AI tests are exciting, many groups, including medical offices, find it hard to expand AI fully. One main reason is that success comes less from having AI technology and more from being ready as an organization and having a clear plan.
Research from MIT Sloan Management Review and others shows that about 95% of AI projects do not give strong business results. This is mostly because there is no clear plan, workflows are not changed enough, and the culture inside the organization is not ready. Just adding AI tools without fitting them into daily work usually brings little benefit.
Healthcare leaders who try AI without changing work routines often face pushback from staff, patchy workflows, or poor patient experiences. These problems come from weak change management, poor communication, and unclear roles for AI within teams.
Other big challenges include trust issues, especially when AI works on its own without human checks. Concerns about AI being clear, protecting patient data, and following rules like HIPAA also weigh on leaders’ decisions. That is why healthcare groups must not only add AI tools but also make rules and plans that handle risks and ethics.
To grow AI use beyond tests, healthcare groups need to rethink their operating models. This means looking at how the organization is set up, how work happens, how rules are made, who does what, and what technology they use.
Embedding AI means fitting AI tasks with the current or changed clinical and admin workflows. For example, front-office phone automation by AI agents like Simbo AI works best when connected closely with patient scheduling and electronic health records (EHR). This lets AI get patient info, confirm appointments, and share timely details with little need for people to step in. Groups that change workflows often see twice the chance of better efficiency.
Changing workflows involves teams from different areas—clinical staff, admin workers, and IT experts—working together to make AI solutions. This teamwork helps AI tools solve real problems, not just ideas. Healthcare routines can be complex, with rules and insurance details, so it is important to map them carefully to find where AI can help most.
AI changes what workers do. People who used to do repeated admin tasks may move to roles that face patients or watch quality while working with AI. PwC found that 67% of bosses expect AI to change work roles a lot in one year, and 48% expect to hire more staff because AI changes work, not fewer.
This change needs workers to learn new skills and accept AI. Groups should train workers on what AI can and cannot do. Most worries come from not understanding AI, fear of losing jobs, or not trusting AI, not from the AI technology itself.
Leaders must be involved. Research from MIT and Svitla Systems finds that when CEOs guide AI work, results are better. Leaders who clearly share AI plans and set rules for its use help build trust and teamwork.
Healthcare has many rules, so using AI responsibly is very important. As AI handles more private patient info and works on its own, trust matters a lot.
PwC shows that people trust AI most when it handles data analysis (38%) and everyday teamwork (31%), but trust is lower when AI is in charge of finances or works fully alone. This shows there is a need for strict rules on data privacy, clear AI decisions, and following laws like HIPAA and new AI rules.
Governance often uses a hub-and-spoke system. A main AI Center of Excellence makes policies, while smaller teams put them in place locally. This keeps rules standard but lets each healthcare place have some freedom.
Good data quality is key for AI to work well. Medical offices need clean, easy-to-access, and well-ruled data so AI agents can act right. Many fall short here; only about 20% say their data is very ready, and half of failed AI projects say data problems caused it.
Medical offices should focus on strong data management. This includes removing duplicates, adding useful context, and building privacy into the system from the start. Good data systems help AI work well with other healthcare tools and improve study of results.
One strong use of AI in healthcare is automating front-office work. Tasks like answering phones, making appointments, checking patient needs, and giving insurance info create many repeated actions.
Simbo AI is an example. It offers AI phone systems made for health providers. Using natural language and context, Simbo AI’s agents can talk like humans, freeing staff from many admin tasks.
AI automation does several things:
Scaling this AI well needs good integration with IT systems, clear rules for sending tough cases to humans, and constant checking of how well it works and if patients are happy.
To grow AI agent use beyond specific workflows, healthcare groups must focus on both strategic and technical areas.
For healthcare leaders in the U.S., growing AI use beyond tests in medical offices is more about organization and planning than technology. Success needs:
By focusing on these areas, healthcare offices and groups in the U.S. can expand AI use from small projects to regular, helpful parts of daily work. Changing how they work and organize will help them manage more patient needs, work more efficiently, and keep up with changes in healthcare over time.
According to a 2025 PwC survey, 79% of US companies have already adopted AI agents, with 35% using them broadly and 17% fully adopting them across workflows and functions.
66% report increased productivity, 57% cost savings, 55% faster decision-making, and 54% improved customer experience, highlighting measurable and tangible value from AI agent deployment.
The primary barriers are mindset, change readiness, and workforce engagement, not technology. Organizational change, employee adoption, and connecting agents across workflows are major challenges.
88% of surveyed executives plan to increase AI-related budgets in the next year due to agentic AI, with over 25% expecting budget increases of 26% or more to fund ambitious plans.
Trust is critical; 28% cited lack of trust as a top challenge. Trust is higher in data analysis (38%) and collaboration (31%) but much lower in financial transactions (20%) and autonomous employee interactions (22%). Responsible AI strategies are essential.
Multi-agent models, where AI agents collaborate across functions, enable handling complex workflows, driving meaningful outcomes such as enhanced customer experience, operational speed, and cost reductions.
75% of executives believe AI agents will reshape workplaces more than the internet, and 67% foresee drastic transformations in roles within 12 months, often increasing headcount due to AI-related changes.
18% of respondents’ companies don’t use AI agents mainly due to a lack of clear use cases or perceived business value, indicating a need for stronger vision and strategic direction.
Less than half (45%) are rethinking operating models and only 42% redesign processes around AI agents, suggesting many companies have not yet embraced AI-driven workflow transformation fully.
Organizations should move beyond pilots to scale AI, rethink competitive strategies, focus on workforce integration and reimagining roles, orchestrate multiple AI agents via agent OS platforms, and embed trust through responsible AI practices.
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