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White Paper – A Practical Guide to Financial Services eDiscovery

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Financial services organizations face unique eDiscovery challenges. Massive data volumes, expanding collaboration platforms, unapproved messaging applications, and evolving privacy and data localization requirements add significant complexity. This white paper shares what we’ve learned working with some of the world’s largest financial institutions, and provides practical direction on how to handle eDiscovery obstacles and increase efficiency. It covers: Regulatory considerations for financial services eDiscovery Differences between litigation and regulatory discovery phases and helpful tips How to best interact with opposing counsel and regulators with regard to eDiscovery And more Download it today to be better prepared when handling eDiscovery for a financial services organization. Download PDF File

​​​Stop Questioning the​​​ Legal​​​ ​​​AI ​​​Model. Question the Foundation​​​​​​

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​​​​Corporate legal teams ​continue to invest heavily in generative AI. Yet many of those investments never move beyond experimentation. In 2024, Gartner predicted that by the end of 2025, at least 30% of generative AI projects would be abandoned after the proof-of-concept stage. The reasons cited were poor data quality, inadequate risk controls, rising costs, and an inability to demonstrate business value. What is notable about that list is that none of those challenges are fundamentally technology problems. The models ​ ​​to advance legal work ​continue to improve at an extraordinary pace. What increasingly determines success is not the technology, but the environment surrounding it. More often than not, organizations struggle not because AI is incapable, but because the foundations required to support it are not yet in place.​ ​ This is becoming one of the defining lessons of enterprise AI adoption​, including for legal teams​​.​ ​​At a practical level, a powerful model can do very little on its own. Its effectiveness depends entirely on the quality of the data, processes, and governance structures that support it. If those foundations are weak, AI does not create clarity. It amplifies existing problems.​​​ ​​The Contracting Data Gap​ This is particularly evident in the contract management space. Recent research from World Commerce & Contracting highlights a distinction that is becoming increasingly important: ​stored is not the same as trusted​. Most organizations have a place to store contracts. Far fewer have contract data they can confidently use to support decisions, manage obligations, or drive operational activity. The findings reveal how significant this challenge remains. More than half of organizations report that each system uses its own data structure, while only 7% operate with a shared reference model across systems. More than half also report having no automated flow of data between contract-related systems. The information exists, but it is fragmented across repositories, applications, and business functions, making it difficult to trust and even harder to use consistently. This is where many AI initiatives encounter difficulties. There is often an assumption that AI can compensate for fragmented processes or poor-quality data. In reality, AI tends to expose those weaknesses. If the underlying records are incomplete, inconsistent, or disconnected, the outputs generated by AI may be faster, but they are unlikely to be more reliable. ​​In that sense, AI is not the starting point. It is the stress test.​​​ ​​Foundation First, Technology Second​ ​​As my article published earlier this year states, “Legal Teams Don’t Need More Technology, They Need Engineering,​​”​​ the ​foundational work comes first. Organizations need structured data, clear ownership, governance, integration across systems, and processes that ensure information remains current and trustworthy. Without those capabilities, even the most advanced models struggle to deliver sustainable value. Once that foundation is established, the challenge shifts from preparing data to adapting AI to the business itself. ​​This is where a new capability is beginning to emerge.​​​ ​​The Rise and Risk of Forward Deployed Engineers​ Andrew Ng recently highlighted the growing importance of Forward Deployed Engineers (FDEs), professionals who work directly with customers to build and refine AI-powered workflows around real business problems. Rather than simply deploying technology, they help organizations translate AI capabilities into operational outcomes. The significance of the FDE role extends beyond technical implementation. As AI becomes more accessible, competitive advantage increasingly comes not from access to a model, but from the ability to integrate that model into the organization’s unique operating environment. The work involves understanding processes, designing workflows, governing data, and continuously refining how humans and technology interact. In many respects, it resembles the evolution we have seen in legal operations and, more recently, legal engineering. At the same time, the emergence of these roles introduces an important strategic consideration. Many FDEs work for software vendors and AI providers. Their objective is naturally to maximize the value of the platform they represent. While this can accelerate implementation, it can also encourage organizations to build processes and workflows around a single technology ecosystem. That may not seem problematic today. However, the AI market remains exceptionally dynamic. Models continue to evolve, new platforms emerge regularly, and the capabilities that differentiate vendors today may look very different in a few years. In that environment, flexibility becomes an asset. Organizations that invest in strong foundations retain the ability to adopt new technologies as they emerge. Organizations that build too closely around a single platform may find that changing direction later becomes more difficult and more expensive. ​​Who Should Build the Foundation?​ This shifts the conversation away from a familiar question. Rather than asking which AI platform to buy, organizations​, and legal teams dealing with high-risk information in particular,​ should first ask who will build the foundation that every AI initiative depends on. For some companies, developing this capability internally will be the right approach. However, doing so requires access to highly sought-after talent and a long-term commitment to maintaining these capabilities as both the business and technology continue to evolve. For others, partnering with external specialists may be more effective. ​​This creates an interesting opportunity for independent partners, particularly Alternative Legal Service Providers (ALSPs). Having spent years helping organizations redesign legal and commercial processes, improve data quality, implement governance frameworks, and operationalize technology, many already possess the capabilities required to support enterprise AI initiatives. Unlike software vendors, they are not tied to a specific platform, allowing organizations to build the necessary foundations while preserving the flexibility to evolve alongside the market.​​​ ​​The Bottom Line​ The technology itself will continue to change. Models will improve. New platforms will emerge. Today’s market leaders may not remain tomorrow’s. What is far less likely to change is the importance of trusted data, connected systems, disciplined governance, and workflows designed to support both human judgment and machine intelligence. Legal teams ​that invest in those capabilities are not simply preparing for today’s generation of AI. They are building an operating model that can adapt as the technology continues to evolve. The question, then, is not which model will ultimately win. It is whether ​​they have​​ built the foundation that allows ​them to benefit from whichever one does. VP, CLM Strategy & Solutions lntegreon About the author Patricia Callejon is a legal technology and consulting leader with over 15 years of experience helping organizations improve how legal work gets done and how technology delivers real value. She has worked across Big Law, legal tech, and consulting, and

AI in Legal Ops: 5 Takeaways from The Blickstein Group 18th Annual Law Department Operations Survey Webinar

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Every year, the Blickstein Group’s Law Department Operations Survey gives legal ops leaders a rare thing: a real benchmark, not just a vendor pitch. This year’s webinar, hosted by Brad Blickstein, explored where legal departments truly stand on AI, compared to where the marketing suggests they should be. If you run legal operations for a large global organization, here’s what’s worth taking back to your team. 1. “Piloting” is the norm — and that’s not a bad thing. The survey found that almost every legal department has done something with AI, but only a small slice describes themselves as fully operational.  Twenty-three percent claim to be “fully operational” but client conversations reveal that true end-to-end integration is rare. The panel’s read: most organizations are still testing, and that’s appropriate given how fast the tooling is changing. The bigger risk isn’t moving too slowly — it’s equating “we turned a feature on” with “we integrated AI into how the department actually works.” Many tools are being “turned on” but are not woven into holistic processes yet. Takeaway: Don’t let a vendor’s activation metrics stand in for genuine adoption. Ask whether AI has improved a workflow, not just whether someone clicked “enable.” 2. Usability now beats security as the top concern — and that’s a sign of progress, not risk-taking. For the first time in the survey’s history, usability edged out security as legal ops’ top AI priority. That might sound alarming, but the panel framed it as a natural sequencing issue, not a lapse in judgment. Usability drives adoption, adoption drives results, results are what we are requiring from AI. If we can’t get users on board and security isn’t going to matter. It only matters if people are actually using the tool. Hand and hand with security is transparency and reliability. The panel discussed the importance of remembering that real generative based AI still doesn’t give you the same answer the same way every time. That means legal professionals need to know what they are asking for and what types of outputs require critical review. Humans are key to AI security. Takeaway: If your AI rollout stalls because a tool is technically secure, but nobody wants to use it, you haven’t solved the problem — you’ve just moved it. Build for usability first, then layer in the governance with an eye toward transparency and a good understanding of use cases. 3. Legal is quietly becoming the AI governance hub — whether it planned to or not One of the more striking data points: roughly a third of legal ops leaders say they now advise on or own AI governance for the entire business, not just the legal department. Historically, legal almost never had that kind of cross-functional reach outside of privacy. This is a natural connection to data governance — since most existing data governance functions already lived inside legal as a function of privacy or data security. AI oversight naturally landed there, too. Acceptable-use policies gave legal an early, visible seat at the governance table, which became a springboard for broader influence. Takeaway: If your legal ops function isn’t yet positioned as a cross-functional AI resource, expect that to change. The organizations ahead of the curve are treating legal’s governance experience as a company-wide asset, not a departmental afterthought. 4. “Agentic AI” needs a much sharper definition before you build around it Nearly every vendor now calls its product “agentic,” which the panel agreed makes survey responses about agentic adoption almost meaningless on their own. A lot of what gets marketed as agentic might just be a robotic workflow and not really a system that makes decisions and pursues a goal. Discrete task tools, strung-together workflows, and true end-to-end agentic systems are three very different things, and most legal departments are still early in that continuum. Plenty of “agentic” claims are just automation with better branding. Takeaway: Before evaluating an “agentic” tool, ask what decision it’s making and on what basis. If it’s just executing predefined steps, you may not need (or want) the added complexity and risk of a true agentic system. 5. The real question isn’t “can AI do this?” — it’s “what are we trying to solve?” Perhaps the most practical thread running through the whole conversation was a look back to the classic IT conversation — someone asks to use a new tool, and the first question back should always be “what problem are you solving for?” That logic applies just as much to choosing between Claude, ChatGPT, or Copilot as it did to any legacy software decision. Takeaway: Resist the urge to start with “which AI tool should we buy?” Start with the pain point, then work backward to the right mix of AI, existing software, and plain old process redesign. Not every problem needs an agent — some just need a well-built workflow. These takeaways are drawn from the panel discussion at the 18th Annual Blickstein Group LDO Survey webinar, featuring Brad Blickstein (Blickstein Group), Diane Homolak (Integreon), Mike Ferrara (FTI Consulting) and Laurie Ehrlich (Icertis). Listen to the 60-minute recorded webinar, “AI Success Stories – How Leading Legal Ops Teams are Operationalizing AI for Value”: Webinar: AI Success Stories – How Leading Legal Ops Teams are Operationalizing AI for Value | Integreon

International Data Law Forum

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Join us March 16 for a free webinar on proving marketing’s impact when margins are tight. Featuring a panel of senior leaders from the financial industry.