{"id":404,"date":"2026-06-05T12:44:00","date_gmt":"2026-06-05T16:44:00","guid":{"rendered":"https:\/\/drugchatter.com\/insights\/?p=404"},"modified":"2026-05-16T13:08:36","modified_gmt":"2026-05-16T17:08:36","slug":"your-sales-reps-are-using-chatgpt-for-drug-information-heres-what-that-costs-you","status":"publish","type":"post","link":"https:\/\/drugchatter.com\/insights\/your-sales-reps-are-using-chatgpt-for-drug-information-heres-what-that-costs-you\/","title":{"rendered":"Your Sales Reps Are Using ChatGPT for Drug Information. Here’s What That Costs You."},"content":{"rendered":"\n
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The medical science liaison had just finished a physician meeting when the rep on the account pulled out his phone. The physician had asked a question about a competitor’s Phase 3 trial results \u2014 something outside the rep’s approved talking points. The rep opened ChatGPT and searched.<\/p>\n\n\n\n

The answer he got was plausible, specific, and wrong on a key efficacy metric. He repeated it to the physician anyway.<\/p>\n\n\n\n

That exchange did not appear in any call report. It did not trigger a compliance flag. The rep did not know the information was inaccurate \u2014 he had no way to know. And the pharmaceutical company he represented had no visibility into what had just happened in that exam room.<\/p>\n\n\n\n

This is the AI problem that pharmaceutical commercial organizations are not talking about loudly enough. While regulatory affairs teams debate AI hallucination risks in patient-facing search and brand teams track share-of-voice across ChatGPT and Gemini, the more immediate compliance exposure is already inside the field force. Sales representatives are using AI tools \u2014 ChatGPT, Gemini, Claude, Perplexity, and increasingly AI-embedded CRM features \u2014 to supplement, shortcut, or replace approved promotional material. The accuracy of those tools for pharmaceutical prescribing information is inconsistent. The regulatory exposure from that inaccuracy is real. And most companies are not monitoring it.<\/p>\n\n\n\n

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Why Sales Reps Are Turning to AI for Prescribing Information<\/h2>\n\n\n\n

The pressure on pharmaceutical sales representatives has not eased. Territory sizes have expanded in most large-cap companies following rounds of restructuring. Physician access has tightened. The window for an in-person interaction \u2014 where it exists at all \u2014 has compressed to minutes.<\/p>\n\n\n\n

In that environment, reps reach for speed. AI chatbots are fast. They synthesize. They answer follow-up questions. They do not require navigating a corporate intranet, logging into a medical information portal, or waiting for an MSL callback. For a rep in a parking lot between calls, ChatGPT is simply more convenient than approved channels.<\/p>\n\n\n\n

The Convenience Gap Between Approved Resources and AI Tools<\/h3>\n\n\n\n

Most pharmaceutical companies provide their field forces with approved resource libraries \u2014 digital detail aids, prescribing information documents, approved medical literature, speaker program materials. These resources are compliant, reviewed, and accurate. They are also, in most organizations, spread across multiple systems, organized by regulatory logic rather than rep workflow, and not optimized for the conversational, rapid-answer format that AI provides.<\/p>\n\n\n\n

When a physician asks a rep a question the rep’s approved materials do not directly answer, the approved path is to submit a medical information request and follow up. That process takes days. ChatGPT answers in seconds. The structural incentive to go off-approved-channel is baked into the system.<\/p>\n\n\n\n

A 2023 survey by ZS Associates found that pharmaceutical field force representatives rated ‘accessing accurate clinical information quickly’ as their top operational challenge. AI tools address that challenge better than most corporate resource systems do \u2014 which is precisely why reps use them.<\/p>\n\n\n\n

Which AI Tools Are Reps Actually Using in the Field?<\/h3>\n\n\n\n

ChatGPT is the most common tool reported in informal surveys and field force focus groups. Gemini is gaining ground among reps whose companies have standardized on Google Workspace. Perplexity, with its citation-linked outputs, appeals to reps who want to appear evidence-based without navigating PubMed directly. Claude is less prevalent in field force usage but appears in medical affairs and MSL functions.<\/p>\n\n\n\n

The more significant development is AI embedded directly into CRM platforms. Veeva’s AI capabilities \u2014 which are actively deployed across dozens of large pharmaceutical companies \u2014 include AI-assisted call preparation, content suggestion, and objection handling. Salesforce’s Einstein layer in Life Sciences Cloud does similar things. These tools are sanctioned by the company. Their outputs are not always verified for clinical accuracy.<\/p>\n\n\n\n

What AI-Embedded CRM Tools Are Telling Reps Before Physician Calls<\/h3>\n\n\n\n

CRM-embedded AI that suggests call content \u2014 ‘based on this physician’s prescribing history, lead with the cardiovascular outcome data’ \u2014 is operating on a different compliance track than a rep personally Googling a competitor drug. The company deployed the tool. The company is responsible for the outputs it generates.<\/p>\n\n\n\n

When those AI-generated call preparation outputs contain inaccurate clinical information \u2014 overstated efficacy claims, omitted safety data, inaccurate competitor comparisons \u2014 the compliance exposure belongs to the pharmaceutical company, not to an individual rep’s personal device usage. Several large-cap companies have discovered this the hard way in internal audits.<\/p>\n\n\n\n

The CRM AI problem is structurally different from the personal device AI problem but equally serious. Both require monitoring. Most companies are addressing neither systematically.<\/p>\n\n\n\n

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The FDA Compliance Exposure When Reps Go Off-Label With AI<\/h2>\n\n\n\n

FDA’s Office of Prescription Drug Promotion regulates pharmaceutical company promotional activity conducted by sales representatives as strictly as it regulates printed promotional materials. The standards that apply to a detail aid apply to what a rep says in a physician’s office. The standards that apply to a journal ad apply to a rep’s verbal efficacy claim.<\/p>\n\n\n\n

How OPDP Standards Apply to AI-Generated Rep Talking Points<\/h3>\n\n\n\n

Under 21 CFR Part 202, prescription drug advertising must present a fair balance of benefit and risk information. Promotional communications by field representatives are subject to the same standard. If a rep delivers an AI-generated talking point that overstates efficacy or omits material safety information, that communication is potentially violative regardless of its source.<\/p>\n\n\n\n

The rep’s use of an AI tool does not transfer regulatory responsibility to the AI platform. The pharmaceutical company is responsible for ensuring its field force communicates within approved promotional bounds. OPDP’s enforcement posture has consistently held companies responsible for the promotional conduct of their representatives, including conduct that company headquarters did not directly orchestrate.<\/p>\n\n\n\n

OPDP Warning Letters That Establish Field Force Liability<\/h3>\n\n\n\n

OPDP has issued warning letters addressing field force promotional violations in contexts that establish relevant precedent for AI-assisted violations.<\/p>\n\n\n\n

A 2019 warning letter to Novartis addressed promotional claims made by sales representatives at a medical conference that went beyond approved labeling for Entresto (sacubitril\/valsartan). The claims were not in any printed material \u2014 they were verbal, made in the course of representative interactions. FDA documented the claims through reports from physicians and agency observers. Novartis was held responsible.<\/p>\n\n\n\n

A 2022 warning letter to Spectrum Pharmaceuticals addressed misleading efficacy claims for Rolvedon (eflapegrastim) in promotional materials that field representatives were using. The violations included superiority claims not supported by head-to-head data \u2014 precisely the type of comparative claim that AI systems generate frequently and inaccurately.<\/p>\n\n\n\n

Neither case involved AI. Both establish that OPDP holds companies responsible for what reps say, regardless of where the information originated.<\/p>\n\n\n\n

When AI-Generated Off-Label Information Becomes Illegal Promotion<\/h3>\n\n\n\n

Off-label promotion by pharmaceutical sales representatives is prohibited. The prohibition covers any affirmative communication, by or on behalf of the manufacturer, promoting off-label use. A rep who uses AI-generated information to respond to a physician’s off-label question \u2014 even framing it as ‘here’s what the literature says’ rather than ‘here’s our approved claim’ \u2014 may be engaging in off-label promotion.<\/p>\n\n\n\n

AI tools are particularly risky for off-label exposure because they do not know, or do not reliably respect, the boundary between on-label and off-label information. A rep who asks ChatGPT ‘what does the evidence say about Drug X for indication Y’ will receive an answer that synthesizes the available literature regardless of whether Y is an approved indication. The model does not flag ‘this indication is not approved.’ It answers the clinical question.<\/p>\n\n\n\n

The Department of Justice has pursued off-label promotion cases aggressively. Pfizer’s $2.3 billion settlement in 2009 \u2014 the largest criminal fine in U.S. history at the time \u2014 was substantially based on off-label promotion by field representatives. Allergan paid $600 million in 2010 for similar conduct. The enforcement history makes clear that the government treats field force off-label communication as seriously as any other promotional violation.<\/p>\n\n\n\n

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What AI Actually Gets Wrong About Your Drug’s Prescribing Information<\/h2>\n\n\n\n

The inaccuracies that AI systems produce about pharmaceutical prescribing information fall into recognizable categories. Understanding those categories helps companies anticipate where their field forces are most likely to be exposed.<\/p>\n\n\n\n

Dosing Errors: The Most Dangerous AI Hallucination Type for Field Forces<\/h3>\n\n\n\n

Dosing information in AI outputs is unreliable in ways that are not always obvious. Models frequently present standard dosing information accurately while mishandling renal or hepatic dose adjustments, pediatric dosing, dose modification guidelines for adverse event management, and special population restrictions.<\/p>\n\n\n\n

Those adjustments are precisely the questions physicians ask when they are trying to use a drug in a complex patient. A rep who provides AI-generated dosing information that omits a renal dose adjustment for a patient with compromised kidney function has potentially contributed to a medication error.<\/p>\n\n\n\n

FDA’s MedWatch adverse event reporting system does not typically capture the information source that preceded a prescribing decision. If a physician makes a dosing error based in part on information provided by a pharmaceutical sales representative \u2014 information the rep sourced from an AI tool \u2014 that causal chain is invisible to pharmacovigilance systems.<\/p>\n\n\n\n

Competitor Drug Comparisons: Where AI Creates the Most Compliance Risk<\/h3>\n\n\n\n

Comparative effectiveness claims are among the most tightly regulated categories of pharmaceutical promotion. OPDP requires that comparative claims be supported by substantial evidence, typically head-to-head clinical trial data, and be presented in a way that accurately reflects the scope and limitations of that evidence.<\/p>\n\n\n\n

AI models routinely generate comparative claims that do not meet that standard. When a rep asks ChatGPT how their drug compares to a competitor, the model synthesizes available literature, observational data, indirect comparisons, and meta-analyses into a summary that often reads as more definitive than the evidence warrants. The output may favor the rep’s drug or the competitor’s drug depending on what the training data contains \u2014 but in either case, the claim is likely not OPDP-compliant.<\/p>\n\n\n\n

The specific problem with head-to-head comparisons is that AI conflates real-world evidence, meta-analyses, and randomized controlled trial data without distinguishing their evidentiary weight. A rep who repeats an AI-generated comparison claim to a physician is making a promotional statement that may have no basis in approved labeling or compliant clinical data.<\/p>\n\n\n\n

Black Box Warning Omissions in AI Drug Summaries<\/h3>\n\n\n\n

Pharmaceutical sales representatives are required to present fair balance \u2014 meaning material safety information must accompany promotional efficacy claims. Black box warnings are the most severe safety designations FDA uses, and their disclosure is non-negotiable in promotional communication.<\/p>\n\n\n\n

Research consistently shows that AI systems omit or attenuate black box warning information. A JAMA study published in 2023 examined ChatGPT responses to drug information queries and found the model failed to spontaneously disclose black box warning information in the majority of cases where disclosure would be clinically indicated.<\/p>\n\n\n\n

A rep who uses AI-generated talking points that do not include black box warning disclosure is presenting an imbalanced communication. The omission is not intentional \u2014 the rep did not know the information was missing \u2014 but OPDP does not require intent to find a violation.<\/p>\n\n\n\n

REMS Programs: What AI Consistently Gets Wrong<\/h3>\n\n\n\n

Risk Evaluation and Mitigation Strategy programs impose specific obligations on prescribers, pharmacies, and patients for drugs with serious safety concerns. REMS requirements are drug-specific, precisely defined, and change over time as FDA updates them. They are among the most technically specific pieces of prescribing information a rep might need to communicate.<\/p>\n\n\n\n

AI systems handle REMS information poorly. Models frequently describe outdated REMS requirements, conflate REMS requirements across different drugs in the same class, or omit REMS requirements entirely. For drugs where REMS compliance is a clinical gating requirement \u2014 isotretinoin’s iPLEDGE program, clozapine’s REMS, thalidomide’s THALOMID REMS \u2014 AI-generated misinformation about REMS requirements is a patient safety issue, not just a compliance issue.<\/p>\n\n\n\n

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How Pharmaceutical Companies Can Monitor What AI Is Telling Their Reps<\/h2>\n\n\n\n

Monitoring AI usage by field forces is technically difficult but not impossible. The challenge is that AI tool usage on personal devices is largely invisible to corporate systems. The opportunity is that the AI outputs themselves \u2014 the answers AI provides to drug-related queries \u2014 are auditable in a way that rep verbal communications are not.<\/p>\n\n\n\n

Auditing AI Outputs for Drug Information Accuracy: A Practical Framework<\/h3>\n\n\n\n

The starting point for any field force AI monitoring program is an audit of what AI systems currently say about your drugs in response to the types of queries reps are likely to run.<\/p>\n\n\n\n

Build a query library that mirrors rep information needs: dosing questions, safety profile questions, efficacy questions by patient type, competitive comparison questions, and REMS requirement questions. Run those queries across the AI platforms your field force is most likely to use \u2014 at minimum, ChatGPT, Gemini, and Perplexity. Capture and analyze the outputs against approved prescribing information.<\/p>\n\n\n\n

The output of that audit tells you three things: where AI is accurate and could be leveraged, where AI is inaccurate and creates exposure, and what the gap looks like between AI-generated answers and your approved promotional materials. That gap analysis drives both compliance training and content strategy.<\/p>\n\n\n\n

Using AI Monitoring Platforms to Track Drug Information Accuracy at Scale<\/h3>\n\n\n\n

Manual auditing is not scalable. A pharmaceutical company with a portfolio of ten drugs across five therapeutic areas cannot manually query every AI platform weekly for every relevant question. Automated AI monitoring platforms address this.<\/p>\n\n\n\n

DrugChatter<\/a> provides automated tracking of how AI systems describe drugs across major LLMs, with efficacy claim extraction and comparison to approved labeling. For pharmaceutical commercial teams, the platform generates reports that flag accuracy deviations, track changes in AI outputs over time as models update, and provide share-of-voice data on how frequently specific drugs appear in AI-generated treatment recommendations.<\/p>\n\n\n\n

The field force compliance application of this monitoring is direct: the accuracy deviation reports that DrugChatter generates identify exactly where AI tools are giving reps wrong information. Those deviations drive targeted training, approved content development, and, where necessary, field force communications about AI limitations.<\/p>\n\n\n\n

What Veeva and Salesforce Life Sciences AI Are (and Are Not) Doing for Compliance<\/h3>\n\n\n\n

The AI embedded in pharmaceutical CRM platforms is not operating without compliance consideration. Veeva’s content management approach is designed to surface approved content \u2014 the AI is supposed to pull from the approved content library, not from the open internet. Salesforce’s Life Sciences Cloud makes similar claims about its AI-generated content suggestions.<\/p>\n\n\n\n

The practical reality is more complicated. CRM AI that suggests call content based on physician data and approved materials can still generate suggestions that are clinically inaccurate, incomplete, or misleading when the approved content library itself is incomplete or outdated. A rep who follows an AI-generated call preparation summary that omits safety updates from the most recent label revision is using an AI tool that does not know the label has changed.<\/p>\n\n\n\n

Pharmaceutical companies cannot outsource clinical accuracy validation to CRM vendors. The regulatory obligation sits with the pharmaceutical company. That means companies need internal processes to validate AI-generated CRM content against current approved labeling \u2014 something few companies have formally built.<\/p>\n\n\n\n

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AI Share-of-Voice in the Field: Which Competitor Drugs Does AI Recommend to Your Reps?<\/h2>\n\n\n\n

When a pharmaceutical sales representative asks an AI tool about treatment options for the disease area their drug addresses, the AI answers from the perspective of a neutral clinical advisor \u2014 not a company employee. That answer may recommend the competitor’s drug first, describe the competitor’s drug as more effective, or present the competitor’s safety profile more favorably than the data warrants.<\/p>\n\n\n\n

How AI Systems Develop Implicit Drug Preferences<\/h3>\n\n\n\n

AI systems do not have preferences in a human sense. They have training data distributions that create statistical tendencies. A drug that appears more frequently in clinical guidelines, medical journal coverage, and physician forum discussions will appear more frequently in AI-generated treatment recommendations \u2014 not because the AI has evaluated the evidence and found it superior, but because the training data overrepresents it.<\/p>\n\n\n\n

This creates a systematic competitive intelligence problem for pharmaceutical commercial teams. The drug that captures AI share-of-voice captures the implicit endorsement of an information source that field forces, physicians, and patients are using with increasing frequency.<\/p>\n\n\n\n

Tracking which competitor drugs receive favorable AI treatment \u2014 and in what clinical contexts \u2014 is now a legitimate function of competitive intelligence programs. DrugChatter<\/a> generates competitive AI share-of-voice data that pharmaceutical teams can use to understand where they are winning and losing in AI-generated treatment recommendations.<\/p>\n\n\n\n

The GLP-1 Competitive Intelligence Problem: What AI Says About Ozempic vs. Zepbound<\/h3>\n\n\n\n

The competition between semaglutide (Ozempic\/Wegovy, Novo Nordisk) and tirzepatide (Mounjaro\/Zepbound, Eli Lilly) is one of the most commercially significant drug competitions in recent pharmaceutical history. Both companies have large field forces. Both field forces operate in a market where AI tools are frequently consulted for competitive intelligence.<\/p>\n\n\n\n

When a Lilly rep asks an AI tool how tirzepatide compares to semaglutide, the answer they receive depends heavily on the model’s training data cutoff and the recency of its knowledge. Before the SURMOUNT-5 head-to-head trial results were published in February 2025, AI models could not accurately describe a direct comparison. After publication, how quickly those results were incorporated into model outputs varied by platform and by query context.<\/p>\n\n\n\n

A Lilly rep using ChatGPT in March 2025 to prepare for a call where the physician was a semaglutide prescriber might receive an AI-generated comparison that did not yet reflect SURMOUNT-5. The rep would go into the call with outdated competitive information \u2014 presented with AI’s characteristic confidence.<\/p>\n\n\n\n

How Field Forces Can Be Inadvertently Trained by Competitor-Favorable AI<\/h3>\n\n\n\n

Repeated exposure to AI-generated content shapes mental models. A rep who checks ChatGPT before physician calls over a period of weeks is not just getting individual answers \u2014 they are being trained by the AI’s implicit characterization of their drug class. If that characterization consistently presents the competitor drug as the clinical default, the rep’s confidence in their own product erodes, and their ability to articulate differentiated value decreases.<\/p>\n\n\n\n

This is a training and coaching problem as much as a compliance problem. Field force managers who do not understand how their reps are using AI cannot address the implicit training that AI usage creates. Performance coaching that focuses solely on approved talking points misses the layer of AI-mediated information that is shaping rep knowledge and confidence.<\/p>\n\n\n\n

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Pharmacovigilance Implications When Field Forces Use AI Drug Information<\/h2>\n\n\n\n

Pharmacovigilance is the ongoing practice of monitoring drug safety post-approval. Pharmaceutical companies have legal obligations under FDA regulations and ICH guidelines to identify, assess, and report adverse events. Field forces are formal nodes in pharmacovigilance networks \u2014 representatives are trained to collect adverse event information from physicians and submit it through company channels.<\/p>\n\n\n\n

How AI Drug Information Could Compromise Adverse Event Collection<\/h3>\n\n\n\n

The pharmacovigilance risk from AI-assisted field forces operates through a specific mechanism: when reps provide AI-generated safety information to physicians, and that information is inaccurate or incomplete, it may influence what the physician considers worth reporting as an adverse event.<\/p>\n\n\n\n

A physician who asks a rep about a specific symptom pattern \u2014 is this something associated with the drug? \u2014 and receives AI-generated information that does not include the relevant adverse event may not report it. The adverse event goes uncollected. The pharmacovigilance signal is lost.<\/p>\n\n\n\n

Pharmaceutical companies are required to ensure that field personnel are equipped to recognize and collect adverse event information accurately. If field training does not address the possibility that AI tools will provide incomplete safety information, the pharmacovigilance system has a gap.<\/p>\n\n\n\n

FDA’s Pharmacovigilance Obligations and Field Force AI Usage<\/h3>\n\n\n\n

FDA’s post-marketing safety reporting regulations (21 CFR Part 314.81 for NDA holders, 21 CFR Part 600.80 for BLA holders) require pharmaceutical companies to report adverse events they become aware of through any source, including field force contacts. The regulations do not specify how companies must train field forces to collect that information. But they do require that the collection system function effectively.<\/p>\n\n\n\n

A company that discovers its field force is systematically receiving incomplete safety information from AI tools \u2014 and whose field force training does not address that risk \u2014 has a pharmacovigilance system with a documented gap. That gap, if identified in an FDA inspection, is a significant finding.<\/p>\n\n\n\n

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What Happens When Physician Trust Is Built on AI-Generated Information<\/h2>\n\n\n\n

Physician relationships with pharmaceutical companies are built over time through consistent, accurate, and useful information exchange. Medical science liaisons and top-performing sales representatives earn credibility by being reliable sources of clinical information. AI tools threaten that credibility at scale when they cause reps to present inaccurate information.<\/p>\n\n\n\n

The Physician Credibility Cost of AI-Sourced Inaccuracies<\/h3>\n\n\n\n

A physician who catches a sales representative in a clinical inaccuracy \u2014 who looks up the actual prescribing information and finds it contradicts what the rep said \u2014 does not typically report the incident to OPDP. They update their mental model of that rep’s reliability. The rep becomes someone whose information needs to be verified, rather than someone who provides value. The relationship degrades.<\/p>\n\n\n\n

That degradation has commercial consequences. Prescribing decisions are influenced by physician confidence in the information they receive. A rep who has lost credibility with a high-prescribing physician has lost commercial access regardless of how many times they visit.<\/p>\n\n\n\n

As AI tool usage by field forces increases, the frequency of inaccuracy-driven credibility events increases. Pharmaceutical companies that do not address AI information accuracy in their field force programs are accepting a slow erosion of physician trust that will not appear in any individual call report but will show up in prescribing data.<\/p>\n\n\n\n

How Physicians Are Using AI to Check Rep Claims in Real Time<\/h3>\n\n\n\n

The dynamic is not one-directional. Physicians are also using AI tools, often in the same meeting where a rep is presenting. A physician who hears a clinical claim from a rep may immediately query ChatGPT or Perplexity to verify it. If the AI produces a different answer \u2014 which may itself be inaccurate, but which the physician treats as an independent verification \u2014 the rep’s claim is challenged in real time.<\/p>\n\n\n\n

This creates a new type of objection that field force training has not yet systematically addressed: the AI-backed objection. ‘ChatGPT says the response rate is X, but you’re telling me it’s Y. Which is right?’ The rep who does not know the answer \u2014 and who cannot immediately access approved clinical references \u2014 is in a difficult position.<\/p>\n\n\n\n

Field forces trained to expect and respond to AI-backed objections are better equipped for the current physician environment. That training requires knowing what AI is saying about your drugs \u2014 which requires monitoring.<\/p>\n\n\n\n

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How to Build a Field Force AI Information Policy That Actually Works<\/h2>\n\n\n\n

Most pharmaceutical company AI policies are written at the corporate level by legal and compliance teams who have limited visibility into how field forces actually work. The result is policies that prohibit things that are already happening and provide no practical guidance for the situations reps actually face.<\/p>\n\n\n\n

What Effective AI Use Policies for Field Forces Must Include<\/h3>\n\n\n\n

An effective field force AI policy addresses four things that most current policies do not:<\/p>\n\n\n\n