eDiscovery, legal research and legal memo creation - ready to be sent to your counterparty? Get it done in a heartbeat with AI. (Get started for free)
The review and negotiation of complex software as a service (SaaS) agreements has traditionally been an onerous manual process for legal teams. With hundreds of pages of dense legalese to pore over, attorneys must meticulously identify key terms, assess risk exposures, and negotiate favorable provisions. This can eat up significant time and resources. But AI tools are now entering the scene to help streamline contract review for SaaS deals.
Several factors have driven the rise of AI contract review tools in recent years. The volume of SaaS agreements has exploded as companies shift to the cloud. At the same time, contracts have grown increasingly complex, with more negotiable terms that attorneys must analyze. The shortage of legal talent and mounting cost pressures on law departments have created a need for technology to multiply human efforts. AI-based software can ingest contract documents and quickly extract key information for review.
According to Lisa Hartkopf, GC of secure access provider Centrify, "AI platforms enable us to efficiently identify points that require closer scrutiny. This allows outside counsel to focus their time negotiating specific terms versus reviewing an entire document." AI tools can rapidly scan agreements and flag high-risk provisions, like unlimited liability clauses, along with terms that deviate from organizational standards. Some solutions also benchmark proposed terms against industry data to assess competitiveness.
San Francisco-based Atrium LTS combines machine learning algorithms and legal expertise to expedite contract review for enterprises. Their software extracts provision-level insights from documents and highlights negotiating opportunities in seconds. Custom models are trained on a client's historical agreements so the system "learns" their specific risk tolerances and objectives. Atrium GC Matthew Melville notes, "By leveraging AI to handle routine contract review tasks, our lawyers can devote their specialized skills to high-impact work that drives value and innovation for the business."
The nimble on-demand legal firm LawGeex offers an AI application that reviews and approves everyday vendor contracts in under 60 minutes. It accelerates review by automatically surfacing unacceptable terms and suggesting alternative language. LawGeex claims their technology delivers up to 90% time savings versus manual review processes. AI is helping legal teams scale contract review and increase their bandwidth for strategic priorities.
Due diligence is a crucial part of any SaaS agreement negotiation, allowing buyers to validate a vendor's capabilities, security posture, finances, and other critical factors before signing a contract. Traditionally, due diligence entails lengthy questionnaires, calls, document reviews and site visits - a drain on time and resources. AI is transforming this process by rapidly analyzing documents and data to accelerate diligence.
According to Raqi Syed, Co-Founder of legal tech provider ArbiLex, "AI can ingest hundreds of pages of contracts, statements, certifications and audit reports to extract salient points within minutes." Machine learning models can be trained to identify key diligence information like security controls, compliance processes, liability limits and financial health. This frees up lawyers to focus on higher-value assessment of findings versus manual document review.
AI tools also allow buyers to analyze historical diligence documents and identify recurring issues or gaps. Leslie Kretzu, GC at online education platform PathStream, explains, "We built a database of past vendor due diligence records that our AI system continuously learns from. This enables us to quickly pinpoint missing info in new deals and streamline document requests."
Diligence automation is also gaining ground in M&A deals. Companies like Everlaw apply AI to accelerate processing and analysis of acquired entities" documents during due diligence. Technologies like IDEA perform automated data analytics on financial records to speed financial due diligence.
According to Raji Srinivasan, Associate GC at software firm Aha!, "AI diligence tools allow us to scale vendor oversight efficiently. By relying on technology for routine due diligence tasks, we devote more legal resources towards assessing and mitigating cyber risks."
A major challenge in SaaS agreement negotiations is identifying the most critical terms to focus on from lengthy, complex contracts. Key terms often get buried across hundreds of pages of dense legalese. Without a clear handle on negotiating priorities, lawyers risk glossing over pivotal provisions or over-indexing on non-essential terms. This leads to missed opportunities or misdirected efforts.
AI is proving adept at digesting voluminous agreements and pinpointing key terms for negotiation. Algorithms can be trained to scan documents and classify provisions based on importance, risk and other attributes. Machine learning models continuously improve at teasing out negotiating priorities specific to a company"s needs.
Lauryn Agnew, Chief Legal Officer at hospitality SaaS platform Alice, explains how AI helps them hone in on key terms. "Our software ingests the contract, scores provisions using NLP and surfaces the terms that really dictate risk exposure based on our priorities. This allows us to go into negotiations laser-focused on getting the highest-impact terms right."
Israel-based LawGeex offers an AI app that highlights key negotiating terms and explains the associated risks and trade-offs. It uses principles of game theory to model potential negotiation outcomes under different scenarios. LawGeex GC Noory Bechor notes, "Our algorithms help legal teams understand connections between provisions and gain strategic clarity before negotiations."
Kira Systems takes a hands-on approach to identifying key terms. Lawyers train the software by highlighting key words and clauses in sample contracts. Kira then uses this input to score new agreements based on criticality of terms. It emphasizes the importance of human-machine collaboration. According to Kira CEO Noah Waisberg, "AI and lawyers work together to unlock what"s negotiable and material versus standardized or non-essential language."
At Autodesk, GC Pascal Di Fronzo credits AI with helping his team focus negotiations. "Reviewing our cloud services contracts manual used to be like finding needles in a haystack. AI filters reams of language down to the 20% that really requires scrutiny. This leads to better commercial outcomes."
Lawyers spend countless hours manually redlining contract drafts and negotiating back-and-forth before reaching agreement. This process is time-consuming, tedious and prone to overlooking key details in complex documents. AI automation is streamlining contract redlining and drafting to accelerate negotiations.
Contract drafting and review software is automating the creation of redlined versions to reflect proposed changes. Kira Systems offers clause-level tracking of edits between contract drafts. It generates redlines automatically to help both parties visualize revisions. According to Kira CEO Noah Waisberg, "Seeing changes in context, versus jumping between multiple documents, really helps streamline negotiations."
Australian legal tech startup Lawcadia goes beyond basic redlines. It provides an interactive negotiation interface where parties can discuss and resolve changes dynamically. Lawcadia CEO Michael Kemp notes, "We recreate digitally the experience of sitting across the table and negotiating interactively. Our automation allows faster finalization of agreements."
AI is also being leveraged to generate contract drafts and accelerate editing. Companies like LegalSifter and Lexion use natural language generation to quickly create drafts tailored to a deal"s specifics. This provides lawyers a strong starting point versus drafting from scratch.
Leading corporations are collaborating with legal tech firms on solutions to automate drafting and revisions. Abhijit Tannu, GC at food delivery major Zomato, explains "We worked with LawGeex to build an AI tool that instantly converts our templated cloud services contract into a draft tailored to any vendor. We then efficiently negotiate changes from there."
Auto contract generation startup Clausehound relies on libraries of customizable contract templates. Its automation populates templates based on user inputs and allows bulk redlining. Clausehound CEO Rajah Lehal says, "By eliminating redundant manual work, our solution helps legal teams close deals faster."
Advanced contract lifecycle management platforms like Agiloft and Conga Contracts integrate automated redlining and revision tools. Their built-in AI reviews contracts and suggests edits to optimize terms. Alerts notify users of pending changes to accelerate approvals. According to Agiloft CEO Eric Laughlin, "Syncing review, redlining and revision workflows cuts cycle times by over 75% versus manual processes."
As negotiations intensify, predicting potential scenarios and outcomes takes on crucial importance. Lawyers need to strategically model the implications of accepting or rejecting various terms to guide their deal-making approach. But keeping track of permutations across a complex web of interdependent provisions becomes exponentially difficult. This is where AI simulation and analysis makes a big difference.
Sophisticated AI applications can ingest proposed contract terms and data, then run deal outcome simulations to identify optimal negotiation pathways. For example, LawGeex applies principles of game theory and algorithms to assess bargaining dynamics. It models hypothetical scenarios based on varying strategies for material terms. Lawyers can adjust assumptions to see how changes reverberate across other clauses and influence deal value.
Legal tech startup LinkSquares enables lawyers to perform scenarios testing easily. Users can tweak contract variables like payment terms, liability caps, SLAs and cancellation triggers to instantly see how overall risk exposure changes. LinkSquares CEO Jake Sussman explains, "Being able to quantify the impact of concessions and trade-offs is game-changing for negotiations."
At Beckman Coulter, a biomedical firm, GC Laura Brandon cites simulation tools as key to recent wins. "We built a model incorporating 10 years of sales contracts data. Now our AI instantly runs through scenarios showing the revenue impact of adjusting different terms. This allows us to negotiate confidently based on data-driven insights."
Cloud services giant Rackspace partners with big data analytics firm Crunchr to simulate deal outcomes. According to Rackspace GC Holly Windham, "Crunchr"s platform allows us to stress test agreement parameters across various scenarios. Our lawyers gain vital clarity on how decisions in one area affect others."
Morgan Lewis attorney Stephen Wohlgemuth remarks, "Running what-if analyses during negotiations is extremely powerful. AI simulation sheds light on key correlational and causal relationships between terms that humans simply miss given contract complexity."
One emerging application of AI with major potential to transform SaaS negotiations is using algorithms to analyze counterparty behavior. Understanding how prospective vendors historically negotiate specific terms provides invaluable perspective. Their past behavior reveals tendencies, priorities, and sensitivities that lawyers can leverage at the bargaining table. AI unlocks the ability to capitalize on these insights.
Algorithms can ingest sizable document corpuses encompassing years of a counterparty's contracts and interactions. Natural language processing and clustering techniques uncover subtle patterns in how they position and caveat key provisions. As Michael Kemp, CEO of contract lifecycle platform Lawcadia explains, "Our AI looks across counterparties" agreements to map their step-by-step approaches to framing pivotal terms. We equip clients with clear visibility into what trade-offs and phrasing opposing lawyers reliably push for."
With massive datasets beyond human scale, machine learning algorithms discern nuances like changes in how a vendor phrases its liability exclusions over time or consistently links arbitration clauses to other terms. Lawyers gain data-driven intelligence to craft targeted arguments or offer creative trade-offs calibrated to what motivates counterparties.
Australian legal AI startup Canonbury Analytics trained their neural networks on 7 years of AWS agreements with customers. Canonbury COO Karl Chapman says, "Our models identified how AWS has progressively tightened certain indemnification clauses across sectors. This insight helped an insurer client preempt those clauses during their AWS negotiations."
Morgan Lewis Partner Monica Cervantes describes how AI behavioral insights assisted a recent M&A deal. "Analyzing years of the target"s supplier and customer contracts revealed They consistently prioritized broader termination rights. Armed with this intelligence, we negotiated favorable walkaway provisions."
Limiting the AI"s view to counterparties" contract language risks missing the full context. Forward-thinking legal teams are analyzing associated communications and correlating them with contract positions. For example, LawGeex created an interactive map for a client visualizing an opposing lawyer"s responses to key terms over email exchanges and past deals. According to LawGeex GC Noory Bechor, "Linking phrasing to reactions paints a comprehensive motivational profile. Our client strategically tailored arguments using terms that analysis revealed consistently engaged the lawyer."
SaaS agreements present a complex web of risk exposures that legal teams must proactively mitigate. Limitation of liability clauses, data security provisions, service levels, and termination rights all require rigorous analysis to avert financial, operational and compliance perils. But assessing risk interactions across hundreds of pages of legalese confounds even experienced attorneys. This is where AI analytics comes to the rescue.
Algorithms enable rapid risk analysis of contracts at scale by processing thousands of pages in minutes to expose dangers. Applications like LinkSquares automatically tag provisions based on risk categories like privacy, intellectual property and confidentiality. They quantify exposures using severity scores so lawyers instantly see what clauses present the biggest peril. LinkSquares CTO Colin Downes explains, "Our risk analytics visualize contract risk profiles in real-time, enabling mitigation on the fly during negotiations."
Beagle for Contract Analysis goes several steps further by mapping relationships between risk-bearing clauses across a contract corpus. As Beagle GC Leela Srinivasan describes, "Our AI uncovers hidden connections between provisions that compound risks. By making these tangible, we help legal teams address root causes versus just symptoms." For example, Beagle surfaced how expanded service provider termination rights in an SLA amplified risks stemming from limitations of liability in a Master Services Agreement.
Leading legal teams are building contract risk assessment models tailored to their specific tolerance factors. Morgan Lewis Partner Max Van Leyenhorst says, "We helped a banking client train algorithms on historical agreements and risk ratings from their subject matter experts. Now for new deals, the AI accurately flags unacceptable risk areas early in diligence."
AI analytics are also informing risk mitigation by benchmarking contract risk metrics against aggregated market data. For example, SirionLabs benchmarks clients" contracts against anonymized data encompassing millions of agreements. SirionLabs CEO Ajay Agrawal explains, "We determine if provisions are balanced versus peers. Any outliers automatically get flagged for renegotiation."
Platforms like Legal Vision Analytics uncover trends in how prospective vendors tweak liability clauses or service credits over time. Such insights allow preemptively re-balancing terms to mitigate future perils. As Legal Vision GC Lakshmi Challa notes, "Spotting small incremental changes in counterparties" risk allocation equips clients to keep pace."
Morgan Lewis Partner Stephen Wohlgemuth adds, "Not getting blinded by legalese and seeing the forest for the trees is critical for risk mitigation. AI analytics crunch contract data at scale to expose systemic risks and benchmark aggressively versus norms."