Artificial intelligence (AI)-powered meeting tools are being adopted into the workplace at unprecedented speed. Platforms such as Microsoft Teams, Zoom, and Webex now offer features that automatically record, transcribe, and summarize videoconference meetings 鈥� often in real time. It鈥檚 easy to see the appeal. These capabilities promise greater efficiency, searchable records, and reduced administrative effort. 

For legal, HR and compliance functions, however, these same tools raise fundamental questions about data management, privilege, accuracy, and workplace behavior. Without the right governance, they can undermine litigation strategy, erode confidentiality protections, and alter how employees engage in sensitive discussions. 

The pace of adoption compounds these risks. Rollouts are often driven by IT or business units, with legal brought in only after use has begun. That reactive position is especially problematic when meeting content is highly sensitive and discoverable in litigation. What might seem like a harmless transcript of a performance review, workplace investigation, or union strategy can quickly become a piece of evidence. 

The key to safe deployment is to identify where and how AI meeting tools introduce legal exposure and establish considered, practical controls before they become embedded in day-to-day operations. The sections below outline the primary risk areas and safeguards in-house counsel should address.

Key Risk Areas

Permanent Business Records and Retention Challenges
AI-generated transcripts, summaries, and recordings can be deemed official business records under company policy and applicable law. As such, they may be subject to preservation obligations for litigation holds or regulatory investigations, often for years. This can significantly increase storage costs and, more importantly, keep sensitive conversations alive long past when they should have been deleted. Failing to preserve or mismanaging deletion can trigger spoliation claims or regulatory sanctions. 

Privilege and Confidentiality Risk
Recording attorney鈥慶lient conversations, HR deliberations, or internal audits can inadvertently waive privilege protections, particularly if outputs are shared with or stored by a third party. Many AI vendors store data in vendor-controlled infrastructure, and standard contractual terms may not recognize legal privilege or work鈥憄roduct protections. Further, vendors often reserve rights to use client data to train AI models, increasing the risk of exposing confidential strategy, legal advice, and personnel information to unintended audiences.

Accuracy and Reliability Concerns
Automated transcription and summarizing tools lack human judgment and are subject to error. These tools can misidentify speakers, confuse similar-sounding names, omit acronyms or technical terms, or misinterpret back鈥慳nd鈥慺orth exchanges when multiple people speak at once. They may also capture side comments, background discussion, or incomplete thoughts that were never intended to be part of the record or subject to external scrutiny. In disputes, regulators or opposing parties may treat AI-generative records as authoritative over formal meeting minutes, raising credibility questions and making inaccuracies difficult to correct once discovered.

Chilling Effect on Discussions
Disclosure or awareness of active recording and transcription can alter meeting dynamics. Employees may avoid raising issues, sanitize their remarks, or delay escalation of problems for fear of being 鈥渙n the record.鈥� This chilling effect can hinder proactive issue resolution, reduce candor in discussions, and ultimately affect governance.

Data Governance and Vendor Control
Outputs from AI meeting tools are commonly stored and processed by vendors, often in jurisdictions with differing privacy laws. Vendor systems may follow alternative security protocols and encryption standards that do not align with organizational requirements. Without robust contractual provisions, companies may be unable to prevent secondary use, including AI model training, or to control disclosure of sensitive content. Attendance in externally hosted meetings with active AI tools further increases exposure, as content may be recorded, stored, and disseminated outside your governance framework 鈥� and thus, beyond your control.

Practical Considerations and Safeguards

Define Clear Usage Boundaries
Implement clear guidance for when AI meeting tools may be used. Prohibit recording or transcription in meetings involving counsel, HR investigations, internal audits, or sensitive strategic discussions. Consider including guidelines that require advance disclosure to participants before any AI tool is activated, ensuring consent and awareness.

Require Human Review before Circulation 
Develop procedures to disable automatic circulation of raw AI transcripts or summaries. Establish a human review process to verify accuracy, remove informal comments or sensitive language, and ensure alignment with the organization鈥檚 preferred tone. Clearly label reviewed records as 鈥渙fficial鈥� and note where AI-generated outputs are being utilized and that AI outputs are supplementary, not authoritative.

Update Retention and Legal Hold Processes
Integrate AI-generated outputs into existing data retention schedules, legal hold processes, and deletion protocols. Limit access to recordings and transcripts to authorized personnel only. Consider employing encryption and other security measures to protect stored data.

Strengthen Vendor Contractual Safeguards
Conduct due diligence before adopting or expanding AI meeting solutions. Contracts should confirm data ownership, secure deletion upon termination, and require notice of any data breach or disclosure request. Validate that vendor security practices meet relevant legal and regulatory standards. Also, consider banning any secondary use for AI training.

Employee Education and Training 
Awareness is critical to mitigating misuse and risk. Train employees on proper use of AI tools, the legal implications of recorded conversations, and the importance of professionalism in meetings subject to transcription. Encourage escalation of any concerns about unauthorized recordings. Make AI policies easily accessible to employees and update them as AI technologies evolve.

Pilot Before Wide Rollout
Test AI meeting tools in low鈥憆isk environments first, so potential issues can be spotted before the technology is deployed company鈥憌ide. Legal, compliance, privacy, and HR should be part of the evaluation team from the outset.

The expansion of AI meeting tools into daily operations demands active oversight. Compliance and legal should set the framework for how AI-generated content is handled, ensuring accuracy, consistency, retention, and privilege are not compromised. Through clear usage policies, integrated retention processes, strong vendor terms, and regular training, companies can embrace AI capabilities and avoid unnecessary risk. 

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While experts can debate whether we are in an AI bubble that could burst, unlike the boom cycles we have experienced in the past, this time, investors are becoming more selective. AI startup formation will no doubt continue its surge as we move into 2026, but funding will become even more concentrated among those companies that can demonstrate a real product-market fit and a credible plan for legal rights and regulatory compliance.

Below are three trends that we can expect to define the AI sector next year.

A Continued Shift in Investor Focus

The capital that is flowing into AI companies at historic levels is not doing so evenly, and this will continue into next year. The bulk of funding is being funneled to fewer, more mature companies. Savvy investors are no longer looking to simply finance experimentation, and most late-stage funding is going to a smaller number of well-capitalized market leaders, leaving many earlier stage companies operating under structural pressure. 

As we recently discussed at the 2025 TED AI conference, this has led to 鈥渁 tale of two worlds,鈥� with many earlier stage AI startups facing the challenge today of scaling revenue or retaining engineers amid intense talent poaching. So, the earlier stage startups in the AI space can no longer rely simply on their technical potential. They must prove they can trigger and sustain hyper-growth in revenue while retaining key engineering talent in a market where the larger companies can lure away top talent with compensation packages startups cannot match. 

Investors are also prioritizing those companies they feel can best withstand the legal and regulatory scrutiny that is ahead. This means founders must build an infrastructure from the start that can support not only technical growth, but also legal and regulatory durability long term.  That means having the legal rights to the data that your models are training on, and their outputs.  It also means complying with a complex web of interlocking regulatory structures that can be national, supra-national, regional, or even local.  While regulations have jurisdictional borders, AI tools can be accessed anywhere in the world.

A Shakeout Among Horizontal AI Startups

Next year, expect there to be a shakeout among horizontal AI startups that lack vertical specialization or agentic capabilities. Investors want to see companies solving domain-specific problems with proprietary data and actionable outputs. We are moving past the era of generic AI platforms to one of targeted, high-value solutions in regulated or operationally complex sectors. Capital will flow to those AI companies that own a problem, not just a model. The era of undifferentiated AI platforms is coming to an end. 

A Surge in M&A

At the same time as all of this is taking place, the capital markets are evolving in parallel. We have started to see the IPO window cautiously reopen, but public market entry is not likely to be the first source of liquidity for these VC-backed companies. Instead, 2026 will likely bring a rise in strategic mergers and acquisitions (M&A) and secondary transactions ahead of public listings. These 鈥減re-exit鈥� transactions will not only return capital to investors who have made it through a long liquidity cycle, but also help companies to strengthen their balance sheets ahead of a potential IPO.

At a time when capital is plentiful but selective, the next phase of AI expansion is not just about building breakthrough AI tools. Instead, 2026 is going to be about building AI businesses that can withstand legal, technical, and market scrutiny at scale. 

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The Market

The interest and need are here. The authors acknowledge that cloud-based and AI-driven technologies are increasingly automating drug discovery and advancing biomedical research. The global AI market is rapidly expanding, with significant growth projected through 2032, especially in North America. In the pharmaceutical and biotechnology sectors, AI’s market value is expected to rise sharply, and by 2030, AI is predicted to play a role in developing more than half of new drugs.[2] However, key challenges in the development and application of AI in health care are present, including data quality, algorithmic transparency, and ethical concerns, highlighting the urgent need for explainable AI models, robust regulatory frameworks, and equitable implementation to ensure responsible and impactful adoption across global healthcare systems.

Current Application in Health Care and Biotechnology

The authors evaluated a wide spectrum of AI technologies applied within biotechnology, including multimodal AI models that integrate imaging data, electronic health records, and clinical notes; advanced algorithms for drug discovery and development; precision medicine platforms; genomics and proteomics analysis tools; synthetic biology applications; automated diagnostics; and digital biomarkers. Specific subfields highlighted include AI-driven solutions in drug discovery, precision medicine, genomics, bioinformatics, clinical trials, and health care systems. The analysis also considered generative models such as Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) for virtual screening, as well as convolutional neural networks (CNNs) in medical imaging.

Endpoints assessed in Potential were the volume and growth of AI-related publications and patents (across languages and subfields), trends in research activity, the impact of AI on research & development timelines and operational costs, clinical adoption rates (such as FDA-cleared AI/Machine Learning (ML)-enabled imaging devices), and the legal status and distribution of patents by jurisdiction. Additional endpoints are the economic value generated through efficiency improvements, market valuation trends, and the concentration of intellectual property among leading institutions and corporations. The article also examines publication bias, accessibility, and inclusivity within the global research landscape, recommending systematic reviews and bias-aware techniques to ensure balanced assessments.

The Bottom Line 

The research and analysis of Potential demonstrate that artificial intelligence is fundamentally transforming biotechnology, with major impacts on research, diagnostics, and economic value creation. AI鈥檚 integration into medical imaging and diagnostics has accelerated workflows, improved accuracy, and enabled the discovery of novel biomarkers, driving more personalized and effective therapies. The authors also evaluated patent filings as a measure of economic investment and conclude that the rapid growth in AI-related publications and patents signals increased global investment and interest, particularly in subfields like drug discovery, precision medicine, and genomics. 

The promise lies in AI鈥檚 ability to revolutionize biotechnological processes, deliver precision medicine, and expand opportunities for innovation and economic growth. However, the authors also identified pain points: there is significant publication bias favoring positive outcomes, limited access to unpublished and proprietary data, and underreporting of failures. The authors believe that the predominance of English-language publications raises concerns about global accessibility and inclusivity. To address these challenges, the authors recommend systematic reviews of gray literature, inclusion of qualitative insights, and adoption of bias-aware bibliometric techniques to ensure a balanced assessment of AI鈥檚 impact. Overall, while AI offers transformative potential, realizing its full benefits will require robust evidence-gathering, global collaboration, and attention to the limitations inherent in current research and reporting practices.

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