Building Smarter Legal Departments Through Responsible AI Integration – with Leaders from Filevine and Meta

This interview evaluation is sponsored by Filevine and was written, edited, and revealed in alignment with our Emerj sponsored content guidelines. Learn extra about our thought management and content material creation companies on our Emerj Media Services page.

In-house authorized groups face a mounting problem: the size of digital info has outpaced their capability to handle it. Email, Slack, Teams, Zoom transcriptions, CLM programs, and AI note-takers have made authorized work richer in knowledge however poorer in focus. 

Today, attorneys are spending rising time discovering info somewhat than making use of judgment. Recent research from EY Law and Harvard Law School Center on the Legal Profession finds that in-house counsel now spend at the least a fifth of their working hours on repetitive administrative duties somewhat than strategic advisory work, leaving much less capability for high-value evaluation.

Yet hiring, as reported by Thomson Reuters’ 2025 Legal Department Operations Index, shouldn’t be catching up. 79% of surveyed company legislation departments reported elevated matter volumes whereas headcount remained flat or decreased.

When extrapolated throughout enterprises, these challenges are sure to hold appreciable enterprise price. Enterprise authorized departments sit on the intersection of compliance, threat, and innovation — features that rely upon well timed, assured choices. When attorneys are pressured into guide knowledge wrangling or repetitive evaluation cycles, delays cascade throughout product launches, contract approvals, and regulatory responses.

The consequence is not only slower authorized work however slower enterprise execution. As generative and analytical AI instruments mature, authorized leaders should decide how know-how can speed up choices with out undermining accuracy or accountability.

Recently on the ‘AI in Business’ podcast, Emerj Editorial Director Matthew DeMello spoke with two leaders addressing that imbalance from totally different angles: Ryan Anderson, CEO and Founder of Filevine, and Kevin Ahlstrom, Associate General Counsel for Patents at Meta. Both see accountable AI as a structural resolution to the identical drawback — the right way to take away friction so human experience can focus the place it issues most.

Their conversations spotlight two vital approaches for enterprise authorized leaders:

• Unify fragmented authorized knowledge to raise human judgement: Using AI to consolidate knowledge throughout authorized, finance, and product programs, giving decision-makers full, context-rich perception with out slowing operations.
• Automate repetitive authorized duties to unlock strategic capability: Deploying accountable automation to speed up patent evaluation, portfolio alignment, and advisory work, releasing attorneys to deal with strategic outcomes.

Unify Fragmented Legal Data to Elevate Human Judgment

Episode: Overcoming Compliance Challenges in Legal AI Adoption – with Ryan Anderson at Filevine

Expertise: Legal Technology Innovation, Workflow Automation Strategy, SaaS Leadership

Guest: Ryan Anderson, CEO and Founder of Filevine

Brief Recognition: Ryan Anderson is the CEO and Co-founder of Filevine, a authorized work platform serving to legislation corporations and in-house groups handle operations. A former working towards legal professional, Anderson holds a Juris Doctor from the University of Utah’s S.J. Quinney College of Law.

Ryan begins by outlining the size of the issue dealing with fashionable authorized groups, explaining that the inundation of information from messaging platforms, emails, Zoom recordings, AI note-taking paperwork, and extra can really overwhelm them.

The core problem, he continues, is not only entry however construction. Fragmented programs scatter info throughout departments, leaving attorneys to manually reconcile a number of sources. Anderson argues that AI can grow to be the organizing intelligence for this complexity, structuring and labeling knowledge so decision-makers can deal with judgment as a substitute of retrieval.

Anderson envisions a future the place authorized groups work from a unified knowledge surroundings — what he calls a “single pane of glass.” In different phrases, somewhat than toggling amongst ten or fifteen disjointed instruments, attorneys would see a consolidated workspace the place contracts, communications, and monetary knowledge converge. That coherence permits AI to normalize artifacts, hyperlink associated threads, and flag anomalies for evaluation. When attorneys open a matter file, they don’t see a haystack of paperwork, however the few objects that truly require their consideration.

Anderson notes that implementing this shift requires deliberate design. To obtain consistency and transparency, he recommends:

• Centralize context by consolidating knowledge streams from authorized, finance, and product programs into one accessible interface.
• Apply AI to normalize knowledge, mechanically merging or tagging duplicate or redundant information for deletion.
• Establish exception routing guidelines that push solely non-standard or high-risk objects to counsel for evaluation.
• Measure outcomes utilizing resolution latency and rework charges somewhat than the amount of duties accomplished.

He cautions towards seeing AI as an all-knowing substitute, 

“Human judgment is, at the least in our foreseeable lifetimes, irreplaceable. The end line [for AI adoption in patent workflows] is when the sign from the noise will get separated in a extremely constant, extremely dependable approach for the human decision-maker. So once they get the data that they want truly to make a judgment on, they know that not solely is that info correct, however full.”

– Ryan Anderson, CEO and Founder at Filevine

Anderson’s perspective reframes AI as a quality-control layer. Legal departments ought to deal with confidence and traceability as first-class necessities; each AI-generated suggestion should embody its confidence stage and the sources it relied on. He likens this to scientific rigor: repeatable, clear, and accountable.

To put this into observe, he advises that leaders:

• Define evaluation thresholds that decide when automated suggestions can proceed and when human sign-off is required.
• Maintain audit logs of each AI interplay, together with prompts, supply materials, and approval choices.
• Institute data-quality checks in order that programs floor fewer, extra related alerts somewhat than extra noise.

When utilized persistently, these ideas yield measurable change. Legal groups spend much less time looking out and reconciling knowledge, and extra time making defensible, insight-driven choices. 

For massive enterprises, Anderson believes this self-discipline — structuring chaos earlier than scaling automation — will separate those that use AI successfully from those that experiment with it. Ultimately, Anderson’s perception is about focus. By utilizing AI to construction cross-channel knowledge, organizations scale back the noise that clouds decision-making and empower authorized professionals to behave sooner, with better context and confidence.

Automate Repetitive Legal Tasks to Unlock Strategic Capacity

Episode: Practical AI for In-House Patent Legal – with Kevin Ahlstrom of Meta

Guest: Kevin Ahlstrom, Associate General Counsel, Patents, Meta

Expertise: Patent Portfolio Management, AI-Enabled Legal Operations, Intellectual Property Strategy

Brief Recognition: Kevin Ahlstrom is Associate General Counsel for Patents at Meta, overseeing patent technique and portfolio growth throughout key know-how areas. Before Meta, he managed international mental property technique at Novartis. He holds a Juris Doctor from Brigham Young University and a Bachelor’s in Electrical Engineering from the University of Utah.

Ahlstrom’s work at Meta echoes Anderson’s philosophy of augmentation over automation, however begins with a special constraint: time. Spending hours attempting to resolve which patents to maintain and which to let go, he argues, will within the very close to time period be simplified by AI.

His first breakthrough got here via a custom-built invention evaluation device. By feeding lengthy, unstructured disclosures into the system, Ahlstrom receives readable summaries that distill important concepts and potential dangers. “I can take very advanced writing and put it into the device,” he explains, “and it’ll spit out one thing easy for me to know. I could make these choices quite a bit sooner on whether or not or not we must always file an invention or not.”

Secondly,  Ahlstrom notes he makes use of AI as a collaborator somewhat than an assistant; a means of dialogue somewhat than delegation: 

“I view it as an amplifier to my mind. I’m placing in shorter prompts, going again and forth, and it’s extra of a dialog. Rather than ‘spit out this whole report for me,’ and then I simply e mail it off, what it could actually do is say, summarize this report into three bullet factors and assist me perceive what it’s saying.”

– Kevin Ahlstrom, Associate General Counsel in Patents at Meta

To make AI collaboration sensible for invention evaluation, Ahlstrom outlines a repeatable method:

• Feed full disclosures and associated prior artwork references into the device for context.
• Request concise summaries that scale back technical complexity to plain language.
• Extract declare deltas and key deviations mechanically into structured tables.
• Route solely exceptions — the disclosures that deviate from firm norms — to attorneys for detailed evaluation.

This structured course of converts evaluation time into resolution time. The AI handles summarization and comparability; the legal professional applies experience. The result’s pace with out compromise.

Beyond invention evaluation, Ahlstrom has prolonged automation into its portfolio technique. He makes use of AI to scan public statements from executives — comparable to Mark Zuckerberg and Andrew Bosworth — and summarize Meta’s acknowledged funding priorities. He then compares these insights to current patent filings and asks the system to suggest changes. Work that when took a number of days now takes lower than an hour.

His method permits authorized to stay strategically aligned with enterprise path. Instead of reacting to filings, the authorized group can proactively steer patent focus towards rising know-how priorities. For Ahlstrom, that’s what makes automation transformative: it modifications not simply workflow however affect.

Still, he insists that pace must not ever eclipse duty. Ahlstrom enforces strict enter governance to forestall delicate knowledge from leaking into AI programs, even internally; earlier than any knowledge is entered, it undergoes guide evaluation.

He additionally stresses the significance of defining acceptable accuracy ranges. If AI is 80% appropriate, leaders should resolve the place that’s enough and the place it’s not. He frames it as a coverage query, not a technical one. To institutionalize this, he recommends:

• Categorizing workflows by threat, figuring out which duties can tolerate minor AI error and which require 100% accuracy.
• Applying confidence thresholds to every class, flagging outcomes under acceptable limits for evaluation.
• Keeping an auditable report of which suggestions have been accepted, modified, or rejected to help transparency.

Looking ahead, Ahlstrom envisions AI as the primary cease for inner enterprise purchasers searching for authorized steerage. Routine inquiries may very well be triaged mechanically, with attorneys specializing in high-value judgment calls. 

But he’s equally aware of how this shift impacts expertise growth. As AI absorbs repetitive duties, youthful attorneys might lose conventional coaching alternatives. His resolution is to redefine junior roles round AI supervision — immediate design, mannequin analysis, and validation of system outputs — so that they develop new abilities whereas preserving authorized reasoning. 

Delivering this coaching with mentorship from extra senior staffers and information trade between the 2 turns potential disruption into institutional renewal.

For Ahlstrom, the strategic worth of automation lies in time reallocation. Each job the system handles expands the human bandwidth accessible for management, innovation, and advisory work. The shift shouldn’t be about working much less, however about working at a better stage.

While Anderson and Ahlstrom method the issue from totally different angles, each level to a shared resolution: automating the repetitive work that consumes authorized groups’ time and consideration. For enterprise leaders, the takeaway from each conversations is evident: automation shouldn’t be about changing attorneys however releasing them to deal with higher-value, strategic judgment.

• Start with high-volume, low-risk workflows the place automation can safely speed up output.
• Define accuracy, confidence, and evaluation thresholds earlier than permitting programs to function autonomously.
• Measure influence by how a lot sooner and extra confidently choices are made, not by mannequin complexity.
• Rebuild junior roles round AI literacy and oversight, making certain human experience stays central to governance.

The differentiator, each argue, isn’t who deploys AI first however who automates finest. Legal departments that use AI to remove guide repetition, construction knowledge, and embed accountability into each output will scale human functionality far sooner than these treating automation as experimentation. By turning time saved into strategic focus, they transfer from reactive authorized help to proactive enterprise management.