Contributors: Ed Nekritz | Bjarne Tellmann
The ideas outlined below draw from Bjarne’s forthcoming book, Law in the Era of AI: Clients, Firms, and the Future of the Legal Industry.
AI and digital transformation are accelerating legal work, compressing timelines, and expanding the range of decisions that require General Counsel involvement. At the same time, risk is converging across legal, reputational, financial, operational, and geopolitical domains. Issues that once arrived sequentially now arrive together and escalate in a single news cycle.
That convergence reshapes both the scope of the GC’s role and the way legal departments must operate. Risk no longer fits into neat categories. Legal risk bleeds into brand risk. Supply chain risk becomes regulatory risk. Financial risk can turn into reputational risk with little warning. Models built on static classification and linear mitigation struggle to keep pace.
As unfamiliar threats arrive faster and cross boundaries, enterprise leadership increasingly expects advice that is holistic, business-oriented, and grounded in context. Legal interpretation remains essential, but it is no longer sufficient on its own.
It also raises the bar for outside counsel as technology reshapes how legal work is delivered.
What does not change is the core of the GC role. As analytics improve and automation expands, judgment, courage, and trusted relationships become more crucial. The GC’s value lies in integrating insights, recommending a course of action, and standing behind it.
Disruption as the New Operating Environment
The expansion of the GC role reflects not just more risk, but a fundamental shift in how risk behaves inside organizations. Risk no longer arrives in discrete, manageable categories. It moves across boundaries, compounds quickly, and often materializes without precedent.
This dynamic is amplified by a higher frequency of high-impact events, persistent macroeconomic volatility, and the reassertion of geopolitics as a business variable. Traditional enterprise risk models, designed around prediction, categorization, and static mitigation, are increasingly strained when unfamiliar threats arrive quickly and interact in unexpected ways.
In response, many organizations are shifting focus from prediction to preparedness. The emphasis moves toward the ability to respond effectively regardless of which risk emerges.
For GCs, that shift carries a clear implication. Enterprise leadership needs advice that integrates legal judgment with business context, risk tradeoffs, and strategic consequences. Legal interpretation remains necessary, but on its own, it is no longer enough.
Dragonfly Lawyering: The New Expectation for the GC Field of Vision
A helpful metaphor for the modern role is “dragonfly lawyering”, which borrows conceptually from Anthea Robert’s notion of “dragonfly thinking” in the risk context (see: Dragonfly Thinking). A dragonfly’s field of view is expansive and multidirectional. The metaphor fits because GC leadership increasingly requires real-time pattern recognition across the enterprise.
That field of vision includes:
- Regulatory signals and enforcement posture across jurisdictions
- Litigation patterns and emerging plaintiff strategies
- Cyber, privacy, and data governance threats that move faster than legislation
- Supply chain vulnerabilities with legal and commercial implications
- Political and geopolitical shifts affecting market access, tariffs, sanctions, and operational continuity
- Reputation dynamics that influence stakeholder trust and enterprise value
AI widens the GC’s field of vision by processing more information at speed and revealing patterns, anomalies, and second-order effects. The obligation to decide, however, remains human. If anything, AI raises the bar: leadership assumes the analysis is done and expects recommendations that are sharper and anchored in enterprise context.
From Traditional Enterprises to AI Factories
Many organizations are moving along a maturity curve from traditional models to AI-native operating models. The stages vary by industry and complexity, but the trajectory remains consistent.
Stage 1: Traditional model
Data is siloed, workflows are largely manual, sharing across functions is limited, and technology is treated as support rather than as an engine of value.
Stage 2: Data-enabled, still traditional
Basic digital integration is emerging, supported by tools such as contract lifecycle management, workflow automation, and structured data collection, but decisions remain primarily human-led.
Stage 3: Embedded AI
Enterprise data begins to pool, enabling AI to augment workflows and provide more adaptive decision support as prediction and automation expand across processes.
Stage 4: AI-native
AI becomes central to operations, with end-to-end digital integration enabling algorithmic optimization across products, services, and internal decisions and materially expanding automation.
Legal departments are moving along the same curve, but with a distinctive constraint: the legal function carries institutional responsibility for judgment, accountability, and principled restraint. The opportunity is significant, and the standard is higher.
The “Gazelle-Elephant” Legal Department
As legal teams evolve, a practical operating model emerges: the gazelle-elephant.
Elephant: a stable, scalable backend
A strong foundation starts with a technology platform that imposes discipline at scale. It standardizes workflows, captures data, and creates learning loops that improve performance over time. In doing so, it enables effective triage, supports self-service and automation where appropriate, and provides visibility into demand, capacity, and outcomes.
Gazelle: an agile, business-embedded front end
On top of the platform, legal value is created by nimble lawyers embedded with business stakeholders, operating with speed, judgment, and commercial fluency. That front end requires generalists who can move quickly, frame options, and align decisions with enterprise priorities.
Legal Operations as the Bridge
An upskilled legal operations capability increasingly determines whether the operating model performs. Legal ops connects technology choices to the department’s purpose, risk posture, and service model. Legal ops also protects the organization from adopting tools that look modern but fail to solve real problems.
The gazelle-elephant model matters because it scales both up and down. A department of one can still triage, standardize, partner intelligently, and deploy targeted tools that remove friction. A global department can do the same with greater complexity and deeper specialization. The principle holds at every size.
Avoid the Bread Machine Problem
A predictable failure mode in legal transformation mirrors a common consumer mistake. A shiny tool looks attractive, so the tool gets purchased. The tool does not match a real workflow pain point, so adoption stalls. The tool becomes shelfware.
Better sequencing, which Bjarne spells out in a law review article he co-authored with Michele DeStefano and Dan Wu (see: Don’t Let the Digital Tail Wag the Transformation Dog: A Digital Transformation Roadmap for Corporate Counsel), prevents that outcome:
- Define purpose: why the legal function exists and what value only the legal function can provide
- Identify bottlenecks: which constraints prevent delivery of that value
- Redesign workflows: triage, routing, self-service, escalation, and outsourcing logic
- Establish operational structure and governance: roles, ownership, metrics, training
- Select technology: only after the preceding steps are clear
The most effective legal departments solve problems rather than chase tools.
Changes for Outside Counsel
AI-enabled legal departments will demand more from law firms and legal service providers. Traditional pyramids built on billable hours and narrow specialization face pressure as clients seek:
- Faster cycle times
- Better data transparency
- More predictable cost structures
- AI-enabled work product that aligns with in-house tools and processes
- Outcome-oriented value, rather than input-cost justification
A parallel is emerging in strategic consulting, where pricing has increasingly shifted toward value and outcomes rather than hours and headcount. Legal services will not move overnight, but the direction is clear: as in-house teams become more AI-enabled, they will expect outside counsel to align around outcomes, integration with in-house systems, and measurable impact.
What Endures: Judgment, Courage, Conviction, Relationships
AI will absorb more routine legal work, but that shift does not diminish the importance of the GC. It increases the importance of GC leadership. As speed, visibility, and analytical capability improve across the enterprise, the differentiator is decision quality in conditions of ambiguity.
In that environment, three capabilities become more valuable. Sound judgment and nuance matter because the hardest decisions cannot be resolved by analysis alone. Courage and conviction matter because enterprise leaders need recommendations, not just well-framed options. Trusted relationships matter because credibility with the CEO, the board, and cross-functional peers determines whether legal advice shapes outcomes or merely accompanies them.
Boards and CEOs increasingly assume the analytics have been done, but the question that follows is harder: “What should we do?” A GC who can answer the question with clarity, business orientation, and principled confidence will define the role in the age of disruption.
The Leadership Imperative for General Counsel
Technology is accelerating legal work, expanding visibility, and compressing reaction time across the enterprise. These shifts demand a corresponding evolution in GC leadership and legal department operating models.
GCs who pair broad, enterprise-wide awareness with operating models that balance scale and agility are best positioned to guide organizations through uncertainty. In an environment of constant disruption, durable legal value continues to come from judgment, credibility, and trust.
Explore BarkerGilmore’s GC AdvantageSM webinars for practical guidance on leadership, operating model evolution, and the capabilities that endure.
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