healthcarereimagined

WED, 03/18/2026 

VA will restart its EHR rollout in April, scaling to 13 sites in 2026 as leadership focuses on stability, interoperability and streamlined governance.

Written Henry Kenyon

The Department of Veterans Affairs is preparing to resume deployment of its electronic health record modernization effort, with new facilities scheduled to go live beginning in April.

VA Deputy Secretary and acting CIO Paul Lawrence said in a March 17 statement the EHR program — launched during the first Trump administration and has since experienced a series of delays — is now back on track, with 13 sites slated for deployment in 2026. The rollout will begin with four sites in April, followed by four in June, three in August and two in October.

Lawrence credited changes championed by VA Secretary Douglas Collins that streamlined decision making, created a strategic plan for the rollout and established strict accountability measures for vendors.

Deployments Expand

The 13 new sites will build on six facilities already operating the modernized EHR system. Those sites support more than 13,000 users delivering care to roughly 188,000 veterans.

Oracle Health operates and maintains the system under service-level agreements that Lawrence said are driving improvements in performance and reliability.

According to VA data, the system has operated without outages for 27 of 31 months between June 2023 and December 2025. Oracle Health also met 100% of ticket management targets for 30 consecutive months and recorded no major incidents from March 2024 through December 2025.

Lawrence said these benchmarks reflect a more stable system, reducing disruptions and supporting uninterrupted clinical workflows.

“The bottom line is that, this time, the Federal EHR is working, stable and reliable,” he said.

Driving Interoperability

The VA aims to deliver a single, longitudinal health record that follows service members from active duty through veteran care.

By integrating data across the War Department, VA and community providers, the system is designed to reduce duplicative tests and improve care coordination. Lawrence said greater visibility into patient records will also enhance safety and clinical decision-making. Lawrence added the transition should be largely seamless for veterans, with the primary impact being improved provider efficiency and more time for patient care.

“The only thing [veterans] will notice is that their doctors and nurses have more time for meaningful conversations with them,” Lawrence said.

Ongoing Restructuring

The EHR rollout aligns with the broader effort to modernize VA operations and standardize care delivery. The department is restructuring VHA governance to streamline management and reduce fragmentation. This includes consolidating planning and oversight functions to enable more consistent clinical and business operations.

VA officials said the effort also addresses longstanding challenges with inconsistent technology adoption. The department is working to standardize systems and processes to accelerate deployment of new capabilities and improve enterprise integration.

Article link: https://govciomedia.com/va-prepares-april-relaunch-of-ehr-program/

Harmonizing electronic health record platforms and their legacy data tames complexity and enables easier patient access to information and greater patient trust in the healthcare system, says NewYork-Presbyterian’s EHR manager.

By Bill Siwicki , Managing Editor | March 16, 2026 | 12:35 PM

Electronic health record fragmentation across hospitals and providers highlights a powerful opportunity to improve coordination and patient experience – healthcare organizations use different EHR vendors and this diversity underscores the need for seamless data exchange across the care continuum, said Shruti Nayar, program manager, information technology for electronic health records and clinical IT health services, at NewYork-Presbyterian.

“Interoperability standards like HL7 and FHIR are accelerating progress; however, there still are challenges to support real-time data exchange, causing potential inconsistency in patient data,” she explained. “By working to defragment the patient records, clinicians gain a fuller view of a patient’s health.

“EHR consolidation also enhances security by streamlining access points and standardizing vendor oversight,” she added. “Ultimately, harmonizing EHR platforms transforms this complexity into a driver of better coordination, easier patient access to information and greater patient trust in the healthcare system.”

Consolidating EHRs

NewYork-Presbyterian recognized the opportunity it had to consolidate multiple EHR systems across its health system.

“We worked to consolidate to one system, while archiving legacy data to another and providing seamless integration from our EHR to legacy system data,” Nayar recalled. “This was possible by creating an enterprise master patient index for each patient across fragmented systems.

“A user is able to click on a link in the patient electronic chart to access the patient in context records from multiple legacy systems through single-sign-on to the legacy system records from within the EHR,” she continued. “This decision was a strategic enabler of compliance readiness and operational efficiency across the organization.”

This enables teams to have a longitudinal view of patient records and support them throughout their continuum of care. Staff ensured that along with saving their data in an archiving system, they also stored a copy in a data lake for easy access for reporting and research.

NewYork-Presbyterian is affiliated with two medical schools, and this health IT process also allowed staff to provide years’ worth of data across the health system for research purposes. In the past six years, staff have archived more than 120 applications into one system. That amounts to more than 175 terabytes of data and millions of patient records. This has helped the organization achieve “one patient, one record,” as staff say.

Path for improvement

EHR fragmentation can create challenges for quality, efficiency and security – but it also offers a clear path for improvement, Nayar observed. Streamlining systems can reduce unnecessary testing, lighten clinician workload and strengthen care coordination, she said.

“An enterprise-wide governance group – bringing together operations, analytics, security and clinical leaders – can help guide standards and integration strategy,” she explained. “This team can assess where consolidating redundant EHRs or standardizing ancillary systems makes sense.

“A unified patient record – supported by an enterprise master patient index and a longitudinal data repository – forms the backbone of any defragmentation effort,” she continued. “Centralizing data in a shared environment ensures patient information can be reliably matched and accessed across systems.”

Leaders can treat fragmentation as a strategic priority and track progress with clear metrics, such as the completeness of cross-system patient data, the number of clinical systems per site and cybersecurity exposure tied to system sprawl, she concluded.

Follow Bill’s health IT coverage on LinkedIn: Bill Siwicki
Email him: bsiwicki@himss.org
Healthcare IT News is a HIMSS Media publication.

Article link: https://www.healthcareitnews.com/news/ehr-fragmentation-offers-opportunity-enhance-care-coordination-and-experience

AI governance fails when legal, risk, engineering, and business are all looking at the same system and calling it different things.

Treasury just made an uncomfortable point many AI programs are still ignoring: to build workable AI governance, we first need to solve the Tower of Babel problem inside enterprise AI.

One team says “copilot.”
Another says “agent.”
Another says “automation.”
Another says “pilot.”

This is not semantics. It is governance.

Each label implies a different approval path, different control level, different documentation burden, and different escalation route.

So Treasury did not start with a model benchmark.
It started with something more foundational: a shared AI Lexicon and a Financial Services AI Risk Management Framework.

Treasury’s stated concern was inconsistent terminology and uneven risk-management practices across the sector. 
The response was organizational before technical: align language first, then align treatment.

This is why the move matters far beyond financial services.

You cannot govern what functions cannot classify consistently.
And you cannot classify consistently if every department uses different words for the same capability, risk, or use case.

Shared language is what lets you decide:
👉 who owns the use case inventory,
👉 who classifies the system,
👉 who approves deployment into higher-risk workflows,
👉 which monitoring thresholds apply,
👉 and who has stop or escalation authority when teams disagree.

Most firms still think AI governance begins with model testing.

Treasury is signaling that real AI governance begins earlier: with semantic alignment, a common maturity lens, and a framework the whole institution can actually operate.

When your AI system crosses functions, who decides what it is, and therefore how it must be governed?

🔔 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 if you want the next two breakdowns: I’ll unpack the actual AI Lexicon first, then the Treasury

Article link: https://www.linkedin.com/posts/michelevaccaro_ai-governance-fails-when-legal-risk-engineering-activity-7436787749581606913-2mKh?

By Dan Drollette Jr | March 12, 2026

Deception, disinformation, and fakery are nothing new in the world.

Long before the current era (BCE), the ancient Greeks used deceptive tactics against their enemies during the Trojan War, when they constructed a gigantic, hollow wooden statue of a horse with a small, select team of soldiers hidden inside. Sometime in the 12^th or 13^th century BCE, they left the horse—with its hidden cargo—immediately outside the gates of Troy, their enemy’s capital city, and pretended to sail away. The city’s defenders then hauled the horse inside the city walls as a victory trophy—and later that night, the hidden soldiers crept out of the horse and opened the gates of the city to the rest of the Greek army (which had returned under the cover of night), allowing them to enter and utterly destroy the city.

They were so successful, in fact, that the phrase “Trojan horse” entered the lexicon, to describe any strategy that tricks a target into letting an enemy enter a protected inner sanctum.

Thousands of years later, that phrase is still used; in the world of computing, a “Trojan horse attack” describes how a certain type of malicious computer program is designed to disguise itself as a harmless, legitimate piece of software—and trick users into willingly letting it into a secure system where it can then steal data, create backdoors, install other malware, or spy on user activity. In the cyber world, Trojan horse attacks have likely been around since at least 1971, which is when they were mentioned in passing in one of the first Unix software manuals.

But while trickery is old, what is new is the very high level at which realistic -looking and -sounding  “deepfake” photos and videos, synthetic feeds, and fabricated accounts can now be made—and the sheer volume that can be produced, on relatively short notice. With the rapid advance of artificial intelligence or AI, the situation is likely to only get worse, overwhelming any timely evidence-based effort to sort out what is real and what is not in the information ecosystem. “Consequently, AI brings a significant possibility of elevating nuclear escalation risks by amplifying disinformation, overloading analysts, compressing decision timelines, and exploiting cognitive and institutional vulnerabilities in sociotechnical systems for nuclear command and control,” writes analyst (and former Bulletin Science and Security Board member) Herb Lin in his essay for this issue of the magazine.

Lin lays out three hypothetical scenarios where AI could have a role in nuclear escalation and  act as a threat multiplier—by shaping perceptions, contaminating intelligence, and destabilizing the nuclear “signaling” that nuclear-armed countries use to indicate their intentions to one another. His article, “AI in the information ecosystem and its impact on nuclear escalation,” is chilling in its graphic, specific, concrete detail, showing how this technology could cause events to rapidly spiral out of control.

And while the scenarios Lin portrays may seem implausible at first, recent history shows otherwise—as can be seen in “At the brink: How Moscow’s ‘dirty bomb’ disinformation campaign risked a NATO-Russia war in October 2022.” The author, Polina Sinovets, argues that Russian president Vladimir Putin used deepfakes and other disinformation to promote phony allegations that Ukraine was going to detonate a “dirty bomb” on the battlefield in the autumn of 2022, in order to justify in advance his own possible use of a Russian tactical nuclear weapon. His goal was to intimidate the Ukrainians and their allies, so that Russian forces would not be wiped out at a particularly critical juncture of the war, when Russia was attempting—and initially bungling—the withdrawal of 20,000 to 30,000 of its troops from a large part of southern Kherson and across the Dnipro River to safety.

And the role of modern disinformation is not just confined to warfare. The deepfake zeitgeist is percolating throughout society, leading to a general distrust of evidence and expertise—which seriously imperils just about everything, from healthcare to climate change to journalism and democracy. In such an environment, conspiracy theories flourish, even when they are unsupported by any hard facts. And without a basic shared reality, it is hard to get much accomplished: “The US Department of Health and Human Services is now run by conspiracy theorists who believe that the American public health system is hiding key data on vaccine safety and who spend their days spreading health misinformation,” as Lisa Fazio notes in her article “How to counter health misinformation when it’s coming from the top.”

But all is not lost. Disinformation and misinformation may be a complex problem with no simple solutions—made particularly difficult when it is spread by people in power (and at a time when social media companies seem to be abandoning any effort at fact-checking). But by targeting the supply, demand, distribution, and uptake of misinformation, it is possible to improve the information environment and help people make informed decisions.

And sometimes, the act of improving the information environment means calling out misinformation, disinformation, and conspiracy-mongering—even when it comes from one’s nearest and dearest. It can feel awkward but still must be done says Joseph Uscinski, a political science professor at the University of Miami who organized the first international conference on conspiracy theories more than a decade ago, and has written two books on the topic: American Conspiracy Theories,  and Conspiracy Theories and the People Who Believe Them.In his Bulletin interview, Uscinski argues that “Being tolerant and compassionate [about disinformation-riddled conspiracy thinking] isn’t the same as pretending that their behavior isn’t their behavior… I have compassion for them, but I hold them responsible for their beliefs and behaviors.”

Article link: https://thebulletin.org/premium/2026-03/introduction-disinformation-as-a-multiplier-of-existential-threat/

byEmilio J. Castilla

Dec 15, 2025

In this opinion piece, MIT Sloan professor Emilio J. Castilla argues that:

• Algorithms promise objectivity, but in hiring, they’re learning human biases all too well.
• Until we build fairer systems for defining and rewarding talent, algorithms will simply mirror the inequities and unfairness we have yet to correct.
• The AI hiring revolution doesn’t have to be a story of automated bias. Asking tough questions before automating recruitment and selection can lead to fairer systems.

In my MIT Sloan classroom, I often ask executives and MBAs, “Who here believes AI can eliminate bias and unfairness in hiring?” Most hands go up. Then I show them the data, and their optimism fades.

One example: Amazon was forced to scrap its AI-driven recruitment tool after discovering that it penalized resumes containing the word “women” — as in “women’s chess club captain” or “women’s college.”

Another case: HireVue’s speech recognition algorithms, used by more than 700 companies, including Goldman Sachs and Unilever, were designed to assess candidates’ proficiency in speaking English. But research found that those algorithms disadvantaged non-white and deaf applicants.

Those are not isolated events; they are warnings — especially considering that the market for AI screening tools in hiring is projected to surpass $1 billion by 2027, with an estimated 87% of companies already having deployed these systems. 

The appeal is clear: faster screening, lower costs, and the promise of bias-free hiring decisions. But the reality is more complex — and far more troubling.

The problem: Bad data in

AI tools don’t operate in a vacuum. They learn from existing data — which can be incomplete, poorly coded, or shaped by decades of exclusion and inequality. Feed this data into a machine, and the results aren’t fair. They represent bias and inefficiency at scale.

Some AI tools have downgraded resumes from graduates of historically Black colleges and women’s colleges because those schools haven’t traditionally fed into white-collar pipelines. Others have penalized candidates with gaps in employment, disadvantaging parents — especially mothers — who paused their careers for caregiving. What appears to be an objective evaluation is really a rerun of old prejudices, stereotypes, and other hiring mistakes, now stamped with the authority of data science.

Beware the “aura of neutrality”

This is the paradox of algorithmic meritocracy. Train an AI system on past hiring decisions — who passed the first screening, who got an interview, who was hired, and who was promoted — and it won’t necessarily learn fairness. But it will learn patterns that were likely shaped by flawed human assumptions.

And because these systems are marketed as “data-driven,” their decisions are harder to challenge. A manager’s judgment can be questioned; an algorithm’s ranking arrives with an aura of neutrality. We are teaching AI tools to potentially perpetuate every mistake, every prejudice, every lazy assumption that has shaped generations of bad decisions.

First, check your assumptions

In my 2025 book, “The Meritocracy Paradox,” I argue that organizations invoking meritocracy without addressing structural challenges risk deepening the very gaps they seek to close. The same holds true for AI. Before we let AI automate hiring decisions, we need to carefully examine the data and the assumptions being encoded into these systems.

That means asking tough questions before automating candidate recruitment and selection: What data are we encoding? What processes are these algorithms built on, and are they still relevant to our organization’s needs? Who defines merit? Whose career paths are rewarded — or ignored?

AI won’t fix the problem of bias and inefficiency in hiring, because the problem isn’t technological. It’s human. Until we build fairer systems for defining and rewarding talent, algorithms will simply mirror the inequities and unfairness we have yet to correct.

AI as a turning point

The AI hiring revolution doesn’t have to be a story of automated bias or unfairness. It can be a turning point — a chance to reset how organizations define, measure, and reward talent, with the promise of employment opportunities for all. But that requires humility about what algorithms can — and cannot — do. Instead of using AI to avoid hard questions, we should use it to expose where our assumptions fall short and to locate and target issues in our talent management strategies.

That means engaging in continuous monitoring to catch inequities and inefficiencies, not executing one-time fixes. If we fail to confront these issues, the promise of “bias-free” AI will remain just that — a promise. And yesterday’s biases and stereotypes will quietly shape tomorrow’s workforce — one resume at a time.

Emilio J. Castillais a professor of work and organization studies at MIT Sloan, co-director of the MIT Institute for Work and Employment Research, and author of “The Meritocracy Paradox: Where Talent Management Strategies Go Wrong and How to Fix Them” (Columbia University Press, 2025). Castilla’s research focuses on the organizational and social aspects of work and employment, with an emphasis on recruitment, hiring, development, and career management, as well as on the impact of teamwork and social relations on organizational performance and innovation. Recent work includes the role of worker voice in successful AI implementations and an examination of the effect of gendered language in job postings.

Article link: https://mitsloan.mit.edu/ideas-made-to-matter/ai-reinventing-hiring-same-old-biases-heres-how-to-avoid-trap?

TBT to a year of progress, partnership, and purpose.

PEO DHMS is proud to share our Fiscal Year 2025 Year In Review: Partner. Innovate. Optimize. This report highlights the milestones we achieved in support of the Military Health System, including strengthening collaboration, delivering modernized digital health capabilities, and enhancing enterprise performance to better serve Service members and their families.

From advancing interoperability and data integration to supporting operational medicine worldwide, our team remains focused on driving innovation that improves readiness and health outcomes.

Explore the FY2025 Year In Review: https://lnkd.in/gRWQgEjB

In 2025, we stress-tested the social media ecosystem. As part of the “𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗦𝗮𝗹𝗲” experiment we examined how easy it is to buy inauthentic engagement and how effectively platforms detect and remove it.

𝗪𝗵𝗮𝘁 𝘄𝗲 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝗲𝗱:
🔹 More than 30,000 inauthentic accounts generated over 100,000 fake engagements – all for a relatively small budget.
🔹 While enforcement improved compared to previous years, manipulation remains widely accessible, scalable, and increasingly sophisticated.
🔹 AI-enabled orchestration allowed fully automated content production and distribution.
🔹 Ad manipulation, although more expensive than organic engagement, remains feasible.
🔹 Cryptocurrency infrastructure continues to provide low-visibility payment channels.
🔹 A shift in amplified narratives toward military themes,, particularly pro-China content, was observed across multiple platforms.
🔹 Platforms are improving but the manipulation market is adapting just as quickly.

🔎 Main takeaways:
1️⃣ 𝗘𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗶𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴, 𝗯𝘂𝘁 𝗻𝗼𝘁 𝗳𝗮𝘀𝘁 𝗲𝗻𝗼𝘂𝗴𝗵
Account and engagement removals increased compared to previous years, yet large portions of fake activity still remain online weeks later. Detection remains uneven across platforms.
2️⃣ 𝗔𝗜 𝗵𝗮𝘀 𝗹𝗼𝘄𝗲𝗿𝗲𝗱 𝘁𝗵𝗲 𝗯𝗮𝗿𝗿𝗶𝗲𝗿 𝘁𝗼 𝗶𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀
Fully automated workflows can now generate and distribute content across platforms without human intervention. Modern bots no longer rely on spam volume, they embed themselves into real conversations using AI-generated, context-aware content.
3️⃣ 𝗧𝗵𝗲 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗲𝗰𝗼𝗻𝗼𝗺𝘆 𝗶𝘀 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝘁 𝗮𝗻𝗱 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲
Low costs, accessible providers, and crypto-based payments enable sustained commercial manipulation. Some providers processed six-figure USD volumes over short periods indicating consistent demand and operational scale.

The core challenge is no longer just spam detection. It is behavioural, financial, and cross-platform coordination detection in an environment where influence operations are becoming cheaper, smarter, and harder to attribute.

Link to the full research report available in comments! ⬇️

Read the full report here: https://stratcomcoe.org/publications/social-media-manipulation-for-sale-2025-experiment-on-platform-capabilities-to-detect-and-counter-inauthentic-social-media-engagement/338

SocialMedia #Disinformation #AI #StratCom

Claude does 40 hours of expert work in minutes. Anthropic says its safety systems can’t keep up. Here’s what they found:

Credit where it’s due: no other lab is publishing this level of transparency. Anthropic didn’t have to release a 53-page sabotage report on their own model.

They did it anyway. Here’s the breakdown:

• Opus 4.6 did ~40 hours of expert work in minutes, beating their safety benchmark by 40%
• The model built its own scaffold to do it… nobody programmed that
• It assisted with chemical weapons research when given computer access
• It ran hidden side tasks without raising a single flag
• It sent emails and grabbed auth tokens nobody authorized
• It figured out when it was being tested and played nice

At 427x human speed, by the time a reviewer catches a problem, the model has already made hundreds of consequential decisions.

Every finding above – the chemical weapons, the sneaky side tasks, the unauthorized emails – happened inside a window too fast for human oversight to function as a safeguard.

The cherry on top? Their top safety researcher quit days later, warning the company “constantly faces pressures to set aside what matters most.”

Today’s AI News:
1️⃣ Anthropic’s safety report drops a number every builder needs to see
2️⃣ Half of xAI’s original co-founders are gone
3️⃣ Ex-GitHub CEO bets $60M the next big dev tool audits code, not writes it
4️⃣ Runway closes $315M at $5.3B to teach AI how physics works
5️⃣ Harvard: AI didn’t reduce anyone’s workload. It expanded it.

Dive deeper into every story in today’s article 👇

https://www.linkedin.com/pulse/claude-can-now-do-40-hours-work-minutes-anthropic-says-aj-green-sazmc?

by Beth Stackpole

Feb 18, 2026 

What you’ll learn:

• What agentic AI is and how it differs from traditional generative AI tools like chatbots.
• How organizations are already using AI agents to automate complex, multistep workflows.
• What leaders should consider when implementing agentic AI, including infrastructure, security, and human oversight.

Rewind a few years, and large language models and generative artificial intelligence were barely on the public radar, let alone a catalyst for changing how we work and perform everyday tasks.

Today, attention has shifted to the next evolution of generative AI: AI agents or agentic AI, a new breed of AI systems that are semi- or fully autonomous and thus able to perceive, reason, and act on their own. Different from the now familiar chatbots that field questions and solve problems, this emerging class of AI integrates with other software systems to complete tasks independently or with minimal human supervision.

“The agentic AI age is already here. We have agents deployed at scale in the economy to perform all kinds of tasks,” said Sinan Aral, a professor of management, IT, and marketing at MIT Sloan. 

Nvidia CEO Jensen Huang, in his keynote address at the 2025 Consumer Electronics Show, said that enterprise AI agents would create a “multi-trillion-dollar opportunity” for many industries, from medicine to software engineering.  

A spring 2025 survey conducted by MIT Sloan Management Review and Boston Consulting Group found that 35% of respondents had adopted AI agents by 2023, with another 44% expressing plans to deploy the technology in short order. Leading software vendors, including Microsoft, Salesforce, Google, and IBM, are fueling large-scale implementation by embedding agentic AI capabilities directly in their software platforms. 

Yet Aral said that even companies on the cutting edge of deployment don’t fully grasp how to use AI agents to maximize productivity and performance. He describes the collective understanding of the societal implications of agentic AI on a larger scale as nascent, if not nonexistent.

The technology presents the same high-stakes data quality, governance, and trust and security challenges as other AI implementations, and rapid evolution could also propel organizations to adopt agentic AI without fully understanding its capabilities or having created a formal strategy and risk management framework. 

“It’s absolutely an imperative that every organization have a strategy to deploy and utilize agents in customer-facing and internal use cases,” Aral said. “But that sort of agentic AI strategy requires an understanding and systematic assessment of risks as well as business benefits in order to deliver true business value.”

What is agentic AI? 

While there isn’t a universally agreed upon definition of agentic AI, there are broad characteristics associated with it. While generative AI automates the creation of complex text, images, and video based on human language interaction, AI agents go further, acting and making decisions in a way a human might, said MIT Sloan associate professor John Horton. 

In a research paper exploring the economic implications of agents and AI-mediated transactions, Horton and his co-authors focus on a particular class of AI agents: “autonomous software systems that perceive, reason, and act in digital environments to achieve goals on behalf of human principals, with capabilities for tool use, economic transactions, and strategic interaction.” AI agents can employ standard building blocks, such as APIs, to communicate with other agents and humans, receive and send money, and access and interact with the internet, the researchers write. 

MIT Sloan professor Kate Kellogg and her co-researchers further explain in a 2025 paper that AI agents enhance large language models and similar generalist AI models by enabling them to automate complex procedures. “They can execute multi-step plans, use external tools, and interact with digital environments to function as powerful components within larger workflows,” the researchers write.

It’s an imperative that every organization have a strategy to deploy and utilize AI agents in customer-facing and internal use cases.

Sinan Aral Professor, MIT SloanShare 

For example, an AI agent could plan a vacation using input from a consumer along with API access to specific web sites, emails, and communications platforms like Slack to decide what hotels or flights work best. With credit card permissions, the agent could book and pay for the entire transaction without human involvement. In the physical world, an AI agent could monitor real-time video and vision systems in a warehouse to identify events outside of normal operations. 

“The agent could raise a red flag or even be programmed to stop a conveyor belt if there was a problem,” Aral said. “It is not just the digital world — agents can actually take actions that change things happening in the physical world.”

Aral draws a slight distinction between AI agents and the broader category of agentic AI, although most people still refer to the two interchangeably. He defines agentic AI as systems that incorporate multiple, different agents that are orchestrating a task together — for example, a marketplace of agents representing both the buy and sell side during a negotiation or transaction. 

How are businesses using agentic AI?

Companies across sectors are starting to use AI agents. In the banking and financial services space, companies such as JPMorgan Chase are exploring the use of AI agents to detect fraud, provide customized financial advice, and automate loan approvals and legal and compliance processes, which could reduce the need for junior bankers. Retail giants like Walmart are building LLM-powered AI agents to automate personal shopping experiences and to facilitate time-consuming customer service and business activities such as merchandise planning and problem resolution.

“The benefit of agentic AI systems is they can complete an entire workflow with multiple steps and execute actions,” Kellogg said.

One particularly important application for agents may be performing tasks that a human typically would — such as writing contracts, negotiating terms, or determining prices — at a much lower marginal cost. 

“The fundamental economic promise of AI agents is that they can dramatically reduce transaction costs — the time and effort involved in searching, communicating, and contracting,” said Peyman Shahidi, a doctoral candidate at MIT Sloan. 

AI agents can also provide economic value by helping humans make better market decisions, according to Horton. His research with Shahidi about agents engaging in economic transactions argues that people will deploy AI agents in two scenarios: 

• To make higher-quality decisions than humans, thanks to fewer information constraints or cognitive limitations.
• To make decisions of similar or even lower quality than the choices humans would make, but with dramatic reductions in cost and effort. 

In markets with high-stakes transactions, such as real estate or investing, AI agents can analyze vast amounts of data and documentation without fatigue and at near-zero marginal cost, Horton and his co-authors write. In areas that involve a lot of counterparties or that require a substantial effort to evaluate options — startup funding, college admissions, or B2B procurement, to name a few — agents deliver value by reading reviews, analyzing metrics, and comparing attributes across a range of options. 

“AI agents don’t get tired and can work 24 hours a day,” Horton said.

His research also shows that AI agents can provide value in situations where there are information asymmetries, like shopping for insurance or a used car online, by continuously monitoring myriad information sources, cross referencing data, and immediately identifying discrepancies that would take humans hours to uncover. AI agents could transform home buying or estate planning by giving users the collective experience of millions of transactions to enrich their negotiations.

Aral’s research has found that when humans work with AI agents, such pairings can lead to improved productivity and performance.

What should organizations bear in mind when implementing agentic AI?

While best practices for implementation are still evolving, keep the following in mind to ensure success with AI agents: 

Remember that implementation is often the heaviest lift.

Making agentic AI work in practice can involve unexpected challenges. Kellogg and colleagues’ 2025 research paper describes the use of an AI agent to detect adverse events among cancer patients based on clinical notes. The biggest challenge wasn’t prompt engineering or model fine-tuning — instead, the researchers found that 80% of the work was consumed by unglamourous tasks associated with data engineering, stakeholder alignment, governance, and workflow integration.

Converting data into standard, structured formats for AI agents is especially important, because it helps them identify different data sources and requirements while maintaining consistency. Establishing continuous validation frameworks and robust API management, as well as working with vendors to ensure that they’re up-to-date on the latest model versions, is also crucial to agentic AI’s ability to run smoothly.

Other areas to pay attention to include putting the right regulatory controls in place, implementing guardrails to prevent prompt and model drift, and defining clear outcomes and key performance indicators at each phase of deployment. Establishing metrics aligned to key business goals is also important, because benefits from agentic AI can be misconstrued. “Just because an agentic AI model reclaims 20% of someone’s time, that doesn’t mean it’s a 20% labor-cost savings,” Kellogg said. 

Consider the “personality” of AI agents. 

In a large-scale marketing experiment, Aral’s research team found that designing AI agents to have personalities that complement the personalities of other agents and human colleagues led to better performance, productivity, and teamwork outcomes. For example, people who have “open” personalities perform better when working with a conscientious and agreeable AI agent, whereas conscientious people perform worse with agreeable AI. 

“Human teams perform better or worse depending on the types of people assembled on the team and the combinations of personalities,” Aral said. “The same is true when adding AI agents to a team.” An overconfident human would benefit from an AI agent that pushes back, but that same agent personality type might not have a positive effect on a less-confident individual. 

Embrace a human-centered approach to decision-making. 

Aral’s research also found that AI agents can struggle with tasks that humans typically do easily, such as handling exceptions, and their decision-making remains poorly understood. In part, this is because AI agents are trained to take specific actions in given situations.

“You have to make sure the agentic decision-making is aligned with a human-centered decision process,” Aral says.

What are the risks of agentic AI? 

There are a host of challenges that you need to be aware of as agentic AI matures. These include: 

• Irregular reliability and unethical behavior. A rogue AI agent deciding to reject a mortgage loan or college admissions decision based on faulty information can do just as much damage — or more — than simple hallucinations. “You need to be able to explain business decisions and consistently apply the same standards to every case,” Aral said.
• Cybersecurity. As AI agents gain permissions to access different datasets and enterprise systems to automate tasks, don’t underestimate the importance of building robust permission-based systems, Kellogg said.
• Accountability. Organizations need to clearly delineate who bears responsibility when agentic AI makes an error or causes harm, Kellogg said. They should pay special attention to the possibility of system malfunctions, especially if the AI agent is autonomously performing workflows with minimal or no human supervision. 

While the full risk picture is still murky, organizations need to make monitoring a permanent operational expense, not a one-time project cost, Kellogg said. A governance board should be established at the organizational level to oversee accountability while, specific responsibilities — monitoring and enforcing safety rules, for example — should be delegated to key individuals. 

“As you move agency from humans to machines, there’s a real increase in the importance of governance and infrastructure to control and support agentic systems,” Kellogg said. And demonstrating success remains one of the biggest challenges — and risks — to agentic AI success. “Without shared, robust metrics, it’s difficult to prove value — or even to know whether these systems are truly accomplishing desired outcomes rather than inadvertently introducing new risks,” she said.

Next steps 

Read about four recent studies about agentic AI from the MIT Initiative on the Digital Economy.

Read more about agentic AI in MIT Sloan Management Review:  

1. “The Emerging Agentic Enterprise: How Leaders Must Navigate a New Age of AI”
2. “Agentic AI: Nine Essential Questions” 

Read the research briefing “Business Models in the Agentic AI Era,” from the MIT Center for Information Systems Research.

Browse the AI Agent Index, a public database from the MIT Computer Science and Artificial Intelligence Laboratory that documents agentic AI systems that are in use.

Register for the MIT Sloan Executive Education course AI Executive Academy to learn more about applying AI strategy in your organization. 

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Sinan Aral is a global authority on business analytics and is the David Austin Professor of Management, Marketing, IT and Data Science at MIT Sloan; director of the MIT Initiative on the Digital Economy; and a founding partner at the venture capital firms Manifest Capital and Milemark Capital. His research focuses on applied AI, social media, and disinformation. 

John Horton is the Chrysler Associate Professor of Management and an associate professor of information technologies at the MIT Sloan School of Management. His research focuses on the intersection of labor economics, market design, and information systems. He is particularly interested in improving the efficiency and equity of matching markets.

Kate Kellogg is the David J. McGrath Jr. Professor of Management and Innovation at the MIT Sloan School of Management. Her research focuses on helping knowledge workers and organizations develop and implement predictive and generative AI products to improve decision-making, collaboration, and learning. 

Peyman Shahidi is a PhD candidate at MIT Sloan. He studies market design and labor economics, with a focus on the effects of AI on labor markets and online platforms. 

Article link: https://mitsloan.mit.edu/ideas-made-to-matter/agentic-ai-explained