Latest news with #digitalworkforce
Fast Company
a day ago
- Business
- Fast Company
Why this bank is hiring full-time AI employees
Banks are embracing the AI workforce —but some institutions are taking unexpected approaches to its deployment. The Bank of New York Mellon (BNY) has dozens of so-called digital employees working alongside its human counterparts. And distinguishing those AI -driven colleagues might become more difficult in the months ahead. Rather than quietly working in the background, digital workers are integrated into the broader BNY team—and soon they'll have their own email accounts and ability to participate in Microsoft Teams meetings. Whereas many companies are still working to determine how best to incorporate AI into their day-to-day operations, BNY has embraced it in the form of digital staffers who work under the auspices of human supervisors. There are, per The Wall Street Journal, two separate AI personas the bank has built: One focuses on coding; the other is dedicated to validating payment instructions. Each persona is deployed across specific teams, with data access siloed for security reasons. New personas, which will specialize in different areas, are in the works now. These aren't digital assistants. Armed with individual login credentials, NBY's AI workers can access the same tools as human workers. If they spot a problem or vulnerability, they're able to write and implement a patch (though it must first be approved by a human manager). In the future, BNY aims to give its digital workforce access to email and Microsoft Teams, enabling the AI to contact human managers when it faces a problem it can't resolve on its own. While other banks are utilizing AI, most still use the technology as a far less empowered support tool for the human workforce. For example, earlier this month Goldman Sachs launched the GS AI Assistant, an AI program that lets workers across its divisions communicate with large language models for efficiency gains. The tool offers coding suggestions, translates foreign languages, and summarizes complex documents for workers. That's a far cry from the semi-independent state of BNY's digital workers, and the tasks it undertakes are much more mundane. Despite its innovative tech, BNY emphasized to The Wall Street Journal that it's not adopting an AI-first approach. Human hiring is not slowing down at present, even as more AI workers are developed. That's understandable. Several companies that have gone all-in on AI have scaled back those efforts and resumed hiring humans amid pushback from customers who don't want human jobs to be assumed by AI. 'The possibility that AI tools might completely take over tasks previously handled by humans, rather than just assist with them, stirs up deep concerns and worries,' wrote Harvard University marketing professor Julian De Freitas earlier this year. There's some rationale behind those concerns. Anthropic CEO Dario Amodei said last month that he believed AI could eliminate half of all entry-level white-collar jobs within five years, a move that he said could cause unemployment to spike to between 10% and 20%. He's not the only one sounding an alarm. In April, Aneesh Raman, LinkedIn's chief economic opportunity officer, warned that AI is increasingly putting entry-level jobs under threat. And there are a growing number of stories from workers who saw their six-figure-earning jobs disappear without notice, bringing chaos to their lives. Meanwhile, venture capitalist Kai-Fu Lee has called predictions that AI will displace 50% of jobs by 2027 ' uncannily accurate.' But for BNY, the AI revolution is, at present, more about adding to the workforce without inflating the company's payroll budget. And as a bonus, these coworkers won't take the last cup of coffee from the break room either.
The Australian
2 days ago
- Business
- The Australian
Future hinges on bridging Australia's digital skills gap
Australia's economic future hinges on the strength and adaptability of its digital workforce. As technology continues to reshape industries, services and the nature of work itself, demand for tech talent is surging – and fast. To keep pace, we need to rethink how we attract, train and support the next generation of tech talent. Traditional career and education paths alone won't meet the scale or diversity of demand. From Agentic and Generative AI (GenAI) to quantum computing and advanced robotics, emerging technologies are redefining how we work, and the capabilities businesses need to stay competitive. Among these advances, GenAI, and now Agentic AI, stand out as the most transformative. Their rapid evolution and adoption are not only reshaping the tools we use, but the very nature of work. A recent Mercer report found that nearly three-quarters of Australian organisations are already experimenting with AI tools, and more than a quarter are actively developing formal AI strategies, particularly within IT functions. Yet from a skills perspective, many Australian organisations aren't prepared to make the most of AI. The Women in Tech report by RMIT Online and Deloitte Access Economics found that over a third of employers say their workforce either lacks or has outdated tech skills. As future-facing technology adoption grows, so too does the need for a workforce that can guide, collaborate with, and govern AI responsibly. To remain competitive in a digitally driven economy, organisations must go beyond building AI capabilities. They must invest in their people, equipping them with the skills, confidence and adaptability to thrive alongside AI, not be left behind by it. Clearly, AI implementation is contributing to a sense of uncertainty and instability in some workplaces. According to Deloitte's 2025 Human Capital Trends report, 75 per cent of thousands of workers surveyed globally feel they need greater stability at work in the future. As AI transforms how work gets done, the role of people leaders must evolve. Managing tasks and outputs is no longer enough. Leaders need to become coaches who help their teams navigate change and develop skills that have the greatest potential to create value for both the organisation and individual. Tina McCreery is Chief Human Resources Officer at Deloitte Australia. We also need more flexible and accessible entry points – especially for individuals from underrepresented or disadvantaged backgrounds. This includes women, who still represent just 30 per cent of the tech workforce. The Women in Tech report identified more than 660,000 women in Australia who could reskill into technology roles in six months through short courses or on-the-job training, boosting their earning potential by more than $30,000 annually. Inclusive, targeted programs have the power to turn this potential into real progress. One program helping lead the way is Deloitte's Digital Career Compass. Designed for people navigating life transitions or barriers to employment, the 12-week program equips participants with foundational tech training, industry-recognised certifications, business readiness skills and one-on-one mentoring. The goal isn't just to upskill, but to create genuine pathways into sustainable tech careers. Katherine, 43, learned about the Digital Career Compass program just a year after a family event had left her in significantly diminished economic circumstances. Through the program, she learned to build foundational technology knowledge and received expert support to apply that knowledge to complete a Salesforce certification. Upon completion, she was equipped not just with technical skills, but the confidence and business readiness to thrive. Though she initially applied for an entry-level role at Deloitte Australia, her performance and potential meant she progressed quickly, not only transforming her career but also bringing much needed talent, skills and capability. Programs like this show what's possible when we empower people with the tools and the opportunity to succeed because as GenAI becomes embedded across industries, the demand for digitally fluent and adaptable talent will only accelerate. Meeting this demand requires more than technical training. We need to continuously embed AI fluency across every level of the organisation. When employees feel confident using AI tools, they're more empowered to contribute, collaborate, and innovate. Equally important is fostering a culture of experimentation where people are encouraged to explore AI hands-on. This builds the resilience, adaptability, and problem-solving mindset that future-fit organisations will depend on. With bold thinking and collaborative action, we can close Australia's digital skills gap and build a workforce that's not only ready for what's next but equipped to shape it. While the challenge is urgent, the solution is within reach. Tina McCreery is Chief Human Resources Officer at Deloitte Australia. - Disclaimer This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte shall not be responsible for any loss sustained by any person who relies on this publication. About Deloitte Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee ('DTTL'), its network of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. Please see to learn more. Copyright © 2025 Deloitte Development LLC. All rights reserved. -
Forbes
2 days ago
- Business
- Forbes
Digital Employees: The Invisible Workforce Of The Future
Anton Alikov, CEO and Founder, Arctic Ventures. In our rapidly evolving digital world, AI employees (also known as AI agents) are already becoming a transformative force in traditional workplaces. It is possible that in the next two or three years, digital employees will cease to be a futuristic concept and become integral members of high-performance teams in various spheres of human activity. I believe this evolution of the workforce will also create opportunities for human workers to develop new skills and take on more meaningful responsibilities that require creativity, intuition, critical thinking, off-the-shelf skills and emotional intelligence. The Microsoft 2025 "Work Trend Index Annual Report" indicates the emergence of "frontier firms" that fully integrate AI agents into their business processes, leveraging "intelligence on tap" and hybrid human-agent teams where every human employee becomes a colleague or supervisor of a digital agent. It is not yet easy to assess the market for such technologies, but the Boston Consulting Group forecasts that the AI agent market will grow by an average of 45% and reach $52.1 billion by 2030. In my own experience, the combination of digital employees and human teams can increase efficiency without seriously reducing jobs for people, and a number of both established players and startups are at work developing the technology even further. What exactly is a digital employee? A digital employee is a complex AI-based software structure designed to autonomously perform tasks that are traditionally performed by trained people. Unlike traditional rule-based bots that follow simple instructions, well-designed AI employees are able to learn and adapt to a complex environment and make decisions similar to humans. In addition to AI technologies, digital employees also use machine learning, LLMs, deep learning, robotic process automation and cognitive computing technologies to "think" and propose solutions to complex problems. This powerful combination makes it possible for them to solve a wide range of tasks that previously required the intervention of qualified people. Digital employees can analyze large amounts of data, offer solutions to problems and even make complex decisions with high speed and accuracy, avoiding human errors and providing consistent task execution. They can work 24/7 and hyperautomate entire business processes end-to-end. In my opinion, one of the most impressive aspects of digital employees is their versatility in various roles and divisions of companies. They can perform various tasks in the field of customer service, personnel and financial management, etc. This feature can make them a valuable asset in any organization, as they can be configured to support teams from different departments simultaneously. Last but not least, thanks to natural language processing (NLP), AI employees can be designed to effectively communicate with customers and company staff, including detecting context. This allows them to provide accurate and relevant answers, helping to make their interactions more natural and productive. How does a digital workforce function? As AI grows in relevance, a number of developers are striving to seamlessly integrate AI employees with their existing systems in order to increase the efficiency of their entire organization. Many modern AI platforms use low-code/no-code software and offer plug-and-play deployment options, enabling enterprises to achieve immediate benefits without as many technical obstacles. AI employees are designed to work with people, with the latter providing training, control and feedback to fine-tune processes. In my experience, this collaborative approach can significantly increase team productivity by allowing employees to focus on strategic priorities while the AI program performs routine tasks. Humans can have constant access to huge amounts of information, process it quickly and receive recommendations. This can help transform them from narrow specialists into diverse professionals with both broad and in-depth knowledge, potentially increasing their productivity. Who will be the main customer for AI employees? Based on the above analysis, a wide variety of organizations can benefit greatly from the introduction of digital workers. For example, I've observed that they are already in high demand by small businesses right now, as AI can help these companies address a variety of common problems, such as: • Difficulty finding and retaining qualified people, which can lead to a hesitancy toward training human employees • Inability to serve customers 24/7 • Challenges with scaling a business without loss of quality Many of these problems are already being solved by the introduction of digital employees, helping to turn more SMEs into strong candidates to become frontier firms. What challenges can leaders expect? Although AI employees have great potential, their implementation may still be burdened with certain difficulties, including: • Risks of security breaches and data theft • Lack of emotional intelligence in AI programs • The formation of excessive trust in digital employees, which could lead to a lack of human oversight necessary to catch incorrect AI outputs • Technical problems of integrating AI employees into legacy systems I believe overcoming these challenges will be a top priority for developers and business leaders alike in the coming years. Will digital workers be able to completely replace people? This question scares many people, but I believe the answer is no: Despite their impressive capabilities, AI employees are not able to completely replace people. Most likely, we will never see fully automated and peopleless organizations. On the contrary, I see a future in which both digital and human employees will work in tandem, using their unique strengths to achieve various synergies. As history has shown many times, new technologies can destroy jobs, but they also usually go on to create many more new jobs. Conclusion In sum, AI employees are rapidly becoming important components of many organizations. By automating routine tasks and empowering staff, digital workers are revolutionizing the way businesses operate. I encourage business leaders to look at digital employees not as AI replacements for humans but rather as a way to create powerful partnerships that can help ensure high levels of productivity, innovation and job satisfaction. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
Arab News
18-06-2025
- Business
- Arab News
Deloitte Middle East advances AI integration with launch of Global Agentic Network
Deloitte has launched its Global Agentic Network, a strategic initiative designed to scale AI-driven digital workforce solutions for organizations around the world, with significant potential to transform business operations across the Middle East. As AI adoption accelerates in the region, Deloitte's agentic AI offering provides a future-forward solution that combines intelligent automation with human expertise. Through its global network spanning EMEA, Asia Pacific, and North America — and with a growing regional focus in the GCC — Deloitte is bringing AI-powered agents to enterprises looking to drive operational efficiency, accelerate growth, and reimagine how work gets done. Agentic AI refers to software agents capable of autonomously executing tasks, orchestrating workflows, and adapting based on input from users or other systems. These agents, powered by large language models and machine learning, are designed to learn and evolve — making them ideal for complex, dynamic business environments. In the Middle East, where government and private sector agendas alike are emphasizing digital transformation, the Global Agentic Network supports national strategies for AI innovation and economic diversification. Deloitte is already supporting regional clients in sectors such as energy, government, and financial services to implement agentic solutions that streamline decision-making, improve efficiency, and unlock value at scale. 'The Middle East is on a rapid trajectory toward AI-led transformation, and Agentic AI is a game-changer for how businesses operate,' said Yousef Barkawie, Deloitte Middle East Gen AI leader. 'At Deloitte, we're helping our clients navigate the world of AI transformation by architecting and building the capabilities and trust needed for them to scale out their AI deployments and transform at the core. Our clients are finding new efficiencies in their ways of working, streamlining their operations, and reimagining their entire value chains. This is an exciting moment to help shape what the future of work looks like in our region, especially as governments and industries double down on innovation and future-readiness.' The Global Agentic Network includes alliances with leading technology platforms and the launch of solutions like Zora AI, Deloitte's suite of proprietary AI agents that can autonomously perform complex business functions. These tools are already being deployed within Deloitte's own operations, as part of the firm's broader ambition to become an AI-fueled organization by 2030.
Forbes
10-06-2025
- Health
- Forbes
AI Agents Are Coming To Healthcare
A digital workforce is coming to healthcare. getty Y Combinator calls 2025 the 'year of AI agents,' and singled out healthcare as a key focus area. Bill Gates predicts that agents will 'upend the software industry, bringing about the biggest revolution in computing since we went from typing commands to tapping on icons." Nvidia CEO Jensen Huang recently proclaimed that agents "present a trillion-dollar opportunity.' You get the point. Agents are all the rage throughout the tech industry. But what exactly are agents? What role might they play in healthcare? And what's holding them back from realizing their potential? Because early chatbots used decision trees and scripted responses, they struggled with in-depth conversations. With ChatGPT's release, millions of people discovered how to interact with large language models across an almost unlimited range of topics. Agents build on that foundation. They are LLMs enhanced with capabilities such as retrieval, memory, and tools—enabling them to carry out narrowly defined tasks without human supervision (e.g., booking a flight or responding to a customer service request). While copilots assist humans, agents take over tasks entirely. Agentic AI coordinates multiple task-specific agents to accomplish multi-step goals. Many healthcare provider organizations are struggling. Their workforces are strained and shorthanded. Margins are thin, labor costs are rising, and many processes are inefficient and wasteful. Meanwhile, patients often struggle to access timely, affordable, and effective care. Numerous industries, such as banking, travel, and personal finance, have improved productivity and service by doing more with fewer people. Agents may unlock similar opportunities in healthcare. Always on, scalable, and tireless, agents could automate a range of administrative and even clinical processes. That's why many view agents not just as tech, but as operational infrastructure and digital labor. As Luminai CEO Kesava Kirupa Dinakaran explained, 'When you think about how computers can drive value, it's by improving operations. At their core, healthcare organizations are all about operations.' The hope is that agents can expand access, lower costs, improve experiences, and enhance outcomes. A burgeoning wave of companies is racing to deliver AI agents that enable provider organizations to do more with less. Call centers are today's leading use case. Most patients still make appointments by phone, and many skip them due to scheduling hassles. Agents can manage inbound calls by conversing with patients and taking the next best actions (e.g., scheduling an appointment or making a referral). When agents cannot handle an end-to-end call, they can verify key details, summarize the conversations, and hand off to human staff. Assort Health Co-CEO Jeff Liu explained that his company aims to help healthcare organizations 'train the best operators they ever had.' By turning scheduling protocols into rules engines and plugging agents into EHRs, his co-CEO Jon Wang reported that their company automates most inbound calls—routing the rest to call center workers and nurses in priority order. Similarly, Hello Patient CEO Alex Cohen reports that his company's 'fully generative voice and SMS agents handle real-time front‑office calls and proactively re-engage patients, letting clinics boost access and fill schedules without adding headcount.' Agents are also handling outbound calls. Notable's agents prepare patients for clinic visits by verifying their insurance benefits, clarifying (and documenting) their reason for the visit, and checking them in for their appointments. Qventus' agents help optimize patients for procedures and surgeries by providing preparation instructions, sending appointment reminders, answering general care questions, and coordinating pre-admission testing. Infinitus deploys agents to perform health risk assessments and help patients access specialty therapies and rare disease programs. Some agents support patients between and after visits. Ambience Healthcare is developing agents that send follow-up instructions, medication reminders, and scheduling prompts based on visit notes. Hippocratic AI provides a constellation of agents for nurse-level clinical tasks, such as making post-discharge follow-up calls and closing care gaps. The company calls this 'super staffing' since many organizations lack the staff to do enough of this work on their own. Sword Health, a digital physical therapy platform, uses agents to onboard patients, provide customer service, and even help them return hardware. And Cedar recently launched an AI voice agent, Kora, to handle billing inquiries, explain charges, surface payment options, and connect patients with financial assistance. Agents are not limited to patient-facing roles—they're also streamlining the back office. For example, one of Luminai's agents reads incoming faxes and automatically triggers downstream workflows like refills and referrals. VoiceCare AI automates communication between provider organizations, insurers, and patients. Its CEO, Parag Jhaveri, reported that their agent, Joy, can wait on hold for more than 30 minutes, navigate phone trees, sustain multi-hour conversations, and take actions like updating claims and filing requests. Building a well-scripted, polished demo is easy. Delivering reliable performance on real-world healthcare tasks is much harder. Agents often score far short of human performance. For one, healthcare is complex—filled with edge cases, exceptions, and contextual nuance. As legendary software engineer Steven Sinofsky noted, automation is ultimately about handling exceptions, not the routine. Several technical barriers stand in the way. Healthcare data is deeply siloed and fragmented. Lisa Bari, Head of External Affairs at Innovaccer, warns against deploying agents without full contextual data. Also, while LLMs enable agents to handle a wide range of inputs, they can produce uncontrolled outputs. Longer conversations and more contextual data can reduce accuracy and increase latency. Moreover, error rates compound across multi-step processes. For example, an agent with 98% per step will complete a five-step task successfully only 90% of the time (0.98⁵). Developers use various strategies to make agents more reliable. As Sword Health product lead Rik Renard, RN, emphasized, "Evaluating agents' output against pre-specified criteria is essential for deploying reliable agents, yet few people discuss this.' Many agentic systems use specialized knowledge graphs to contextualize information and 'coordinating agents' to link multiple, narrow task-specific agents. Technical guardrails help ensure agents stay within scope and flag questionable output for human review. Still, picking the right use cases is critical. Notable Chief Medical Officer Dr. Aaron Neinstein told me that his company first deploys agents in low-risk areas (e.g., patient intake) to build trust before expanding into more complex workflows. Even with clear use cases, deployment remains hard and no shortcuts exist. As Cedar CEO Dr. Florian Otto summed it up, 'Agents must be built workflow by workflow and only deployed when they reliably work well.' Agents must also integrate with other tech systems—like EHRs and CRMs—to access contextual data and execute tasks. Most use native API integrations, though some interact through the same point-and-click interfaces that healthcare workers use. Ultimately, in an 'agentic economy,' agents must interact with one another—communicating information, transferring resources, collaborating, and tracking transactions. This will require persistent identity and seamless communication protocols, which developers are now building. Several companies, including Salesforce, Microsoft, and Innovaccer, have launched platforms to orchestrate multi-agent healthcare workflows. "Any sufficiently advanced technology is indistinguishable from magic.' Arthur C. Clark famously explained, "Any sufficiently advanced technology is indistinguishable from magic.' If you haven't interacted with an AI agent or tried a modern voice model, you should. This isn't your mother's old pharmacy's IVR system. The technology has crossed the uncanny valley — it feels like magic. But unlike magic, it's not infallible. In high-stakes situations, unreliable AI can cause real harm. As extreme examples, consider how the National Eating Disorder Association's chatbot 'Tessa' encouraged users with eating disorders to diet, or how a companion allegedly pushed a teenager to commit suicide. Agents, however, are more than chatbots. They are tireless digital workers who are always ready to complete specific tasks. When stitched together, they can form multi-agent systems—or 'agent swarms'— that handle complex, interdependent processes and behaviors. Yet, US and EU regulators have approved exceedingly few healthcare AI agents. Hardian Health's Dr. Hugh Harvey warns, "Health systems and clinicians using unregulated AI agents must accept all the risk.' Will they be willing? While regulatory approval is cumbersome, it may be necessary to speed adoption. Also, unlike magic, AI agents aren't plug-and-play. Implementing agents is a massive change-management undertaking. Patients will need to adjust their expectations and learn to interact differently with technology and healthcare. In his book Alchemy, Rory Sutherland explains that in our 'unrelenting quest' for greater efficiency, we often forget to ask 'whether people like efficiency as much as economic theory believes they do.' Take the 'doorman fallacy:' a hotel that replaces doormen with automatic doors may save money, while overlooking the other valuable functions doormen provide, such as hailing taxis, providing security, welcoming guests, and signaling the hotel's status. Similarly, healthcare workers often do far more than their simple job description. For example, if agents automate scheduling, who will reassuringly mention that 'everyone loves Dr. Smith' or that she tends to run late at the end of the day? Of course, healthcare workers aren't perfect or always available. Several company leaders I spoke with say patients prefer interacting with their agents over healthcare workers. But this remains to be seen. Outside healthcare, Klarna—the buy now, pay later company—recently walked back its ambitious efforts to replace two-thirds of its customer service workforce with AI agents. It turns out many customers still want to talk to real people. Agents will also reshape how healthcare workers do their jobs. By offloading drudgery, agents could empower some. Yet others may resent having to babysit new digital coworkers that could potentially replace them. Interestingly, one company CEO shared that executives and managers–not frontline staff–are often the most resistant. Perhaps they worry agents will shrink their teams and reduce their influence. Or, more likely, they may feel daunted by all that responsible deployment demands: surfacing tacit knowledge, defining ground truths, streamlining workflows, and retraining workers for new forms of human-AI collaboration. Infinitus CEO Ankit Jain explained his company 'sells outcomes, not technology.' It focuses on supporting change management, recognizing that 'organizations must be able to crawl before they walk and then run.' For agents to succeed, organizations must look inward. Technology is an amplifier. It can boost productivity or magnify inefficiency. Healthcare organizations, which strongly pull towards inertia, must actively reexamine their operations, rebalance their workforces, and reinforce their digital governance. Otherwise, poorly integrated agents could cause confusion and chaos. Optimizing workflows is essential. So is relieving downstream constraints. For example, a flawless appointment-scheduling agent is of limited value if doctors' schedules are always full. An outreach agent that flags patient needs is only helpful if there are enough nurses and clinicians available to respond. Taken together, these developments point toward a future that's both promising and uncertain. 'Technology is neither good nor bad; nor is it neutral.' Healthcare's digital history has taught us hard lessons: technology can help, but it cannot miraculously solve all problems. And tech doesn't work in isolation—lasting change depends on rethinking people, processes, and priorities, not just deploying tools. Roy Amara famously observed, 'We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.' That seems likely with AI agents. In the near term, agents will make existing workflows faster and cheaper—answering calls, managing intake, and making appointments. Next, they may improve those workflows—coordinating across channels, adding personalization, and responding with context. Eventually, they may enable entirely new approaches, with networks of agents operating semi-autonomously across systems. At the heart of this evolution is a core tension: leverage versus certainty. Agents promise a kind of abundance—tireless labor at negligible cost. But that leverage introduces risk. For now, they'll likely remain in administrative domains, where errors are less costly and rarely dangerous. Still, care delivery is also quite inefficient. Care models for both acute and chronic illness have barely changed in decades—and the clinician-patient encounter remains healthcare's choke point. Here, too, agents may help: handling triage, guiding protocol-driven decisions, even managing chronic conditions. Much of this is already technically feasible. But real progress will require much more: rigorous evaluation, regulatory clarity, updated business models, cultural acceptance, redesigned teams, and seamless escalation paths to human care. Melvin Kranzberg's First Law reminds us: 'Technology is neither good nor bad; nor is it neutral.' The promise of agents is real—but conditional. Their impact depends on how we design, deploy, and govern them. Will agents make care more personalized—or more transactional? Will they return time to clinicians—or reduce their autonomy and turn them into machine supervisors? Will they bring people closer together—or insert more distance? Will they relieve burden—or hollow out the human core of care? Agents are coming. What they become depends on us. I thank the following people for discussing this topic with me: Ray Chen and Jonathan Fullerton (Ambience Healthcare), Jeffery Liu and Jon Wang (Assort Health), Florian Otto (Cedar), Hugh Harvey (Hardian Health), Alex Cohen (HelloPatient), Rick Keating (Hippocratic AI), Ankit Jain (Infinitus), Abhinav Shashank and Lisa Bari (Innovaccer), Pankaj Gore (Insight Health), Kesava Kirupa Dinakaran (Luminai), Aaron Neinstein and Tushar Garg (Notable), David Atashroo (Qventus), Rouhaan Shahpurwala ( Rik Renard and Kevin Wong (Sword Health), Maria Gonzalez Manso (Tucuvi), Parag Jhaveri (VoiceCare AI), Sergei Polevikov (WellAI), and Stuart Winter-Tear.
