ANNOTATION: This question gets more granular. It looks at the crosswinds driving the broader commercial patterns explored in the last three questions.

In Question 6, you took a wide-angle view of market direction.

In Question 7, you considered GenAI’s aggregate effects on overall demand and spend.

In Question 8, you reflected on how those dynamics are showing up inside your own organization.

Now, Question 9 drills into the specific opportunities and threats that together shape those outcomes—the component forces behind your organization’s commercial trajectory.

Again, we start from the premise that no one truly knows how this will play out. These are impressionistic judgments, not factual assertions. The exercise isn’t about forecasting accuracy; it’s about surfacing your working theories—the perceptions and assumptions guiding your real-world decisions.

Every organization is making choices in a fog of partial evidence. Yet decisions about pricing, staffing, investment, and positioning are being made nonetheless. This question helps make those underlying assumptions explicit. What are you treating as headwinds, and what things feel like tailwinds? Where do you sense new potential value, and where do you feel pressure or erosion?

We expect your answers to blend partial data, client anecdotes, internal observations, and instinct. That’s precisely what we’re trying to capture: the felt reality of operating under uncertainty.

Before diving into the individual items, it’s important to note that these factors don’t move independently. Some reinforce each other; others conflict. You can have multiple, even contradictory forces operating at once.

For example, more hours paid for and higher rates both assume the continuing dominance of the billable-hour model—yet returns to scale or new fee structures may assume its erosion. Similarly, an organization might simultaneously gain from more hours billed in certain practices while achieving higher realizations or greater leverage in others where GenAI enables outcome-based or fixed-fee models.

This question intentionally separates these items to make your reasoning visible—which theories you implicitly hold about how GenAI reshapes commercial mechanics. You might believe the market will remain fundamentally driven by billable hours, with GenAI simply accelerating volume and velocity. Or you might expect a structural shift toward more scalable, tech-leveraged models that decouple revenue from time. Both are viable perspectives, and many respondents will hold a mix of them.

The opportunities and threats are meant to be mirrors—each opportunity has a corresponding risk viewed from the opposite direction. The same dynamics that create openings for some providers can generate pressure for others, depending on market position, client mix, and timing.

We ask you to identify the primary commercial opportunity and the primary commercial threat for each of the three time periods covered in the question. By “primary” we mean the opportunity or threat that, in your judgment, has had (or will have) the most material influence on GenAI’s commercial impact for your organization during that specific period.

Your primary opportunity and primary threat may change across time periods, or they may remain consistent. You may find that one opportunity has been most significant to date, another will be most important over the next 12 months, and a different one will take precedence over a longer 36-month horizon. Alternatively, the same opportunity or threat may lead throughout. All of these patterns are plausible. The aim is not prediction with precision, but a clear view of how you believe these forces will sequence and evolve as GenAI capabilities and market dynamics develop.

After selecting one primary opportunity and one primary threat for each period, you are asked to indicate the extent to which the remaining opportunities and threats have affected—or are expected to affect—your organization. This two-step structure helps distinguish the forces that sit at the center of your commercial experience from those that exert more modest but still meaningful influence.

Opportunities

Increased Outsourcing. Superior external GenAI capabilities may create conditions in which clients choose to shift more work to external providers. This is not simply a matter of “more hours paid for.” It reflects the possibility that providers with demonstrably stronger GenAI-enabled workflows, quality assurance, throughput, or cost-efficiency become comparatively more attractive as execution partners. Importantly, increased outsourcing can occur even in environments where total demand remains flat or even declines—particularly if clients perceive that external capabilities deliver meaningfully better outcomes or materially reduce internal strain. For these reasons, increased outsourcing is treated as a distinct pathway rather than a derivative of changes in total hours.

More Hours Paid For. GenAI could increase total paid legal work not only by accelerating business activity but by creating new legal complexities. Regulations governing AI, data use, and automation are proliferating globally, and every enterprise will need counsel on compliance, contracting, and governance. In this view, GenAI doesn’t eliminate work; it generates new categories of it. The persistence of the billable-hour model supports this logic: As legal complexity expands, time spent advising and negotiating around GenAI expands with it.

Higher Rates. True expertise becomes more valuable in a world where baseline knowledge becomes universally accessible. Further, if GenAI enables faster, higher-quality, or more sophisticated outputs, some providers may command premium pricing for that enhanced capability. The assumption here is that clients are willing to pay for demonstrable value and that GenAI strengthens—not weakens—the case for premium expertise. This can coexist with, or even depend upon, the continuation of time-based pricing: Clients pay higher rates for the true experts that AI cannot replace or more for “enhanced hours” where automation underlies the work.

Returns to Scale/More Lucrative Fee Structures. This view holds that GenAI allows revenue to grow faster than headcount—creating efficiency gains that improve margins or enable non-hourly, value-based pricing. It assumes work can be modularized, standardized, or productized at scale. This can complement or contrast with the previous models: A provider might still bill hours in some segments while building alternative-fee or subscription models elsewhere. In short, hours may shrink (in raw numbers or as a share of revenue-generating activity), but profitability may rise.

Higher Realizations. Automation and improved workflow discipline reduce rework, write-offs, and unbillable time. Even if rates or volumes remain stable, collections improve because work is cleaner and more defensible. This view assumes operational impact before structural transformation—GenAI as an efficiency layer that tightens margins without upending pricing.

New Practice Areas/Matter Types. GenAI generates entirely new domains of legal work—AI governance frameworks, model-risk management, data-use compliance, IP around algorithmic outputs, and cross-border data transfer regimes. These areas are likely to expand significantly as legislation and enforcement increase. In this model, new demand offsets any commoditization of existing work.

Differentiation from Peers. Organizations that operationalize GenAI credibly can use it as a visible market signal—winning more mandates or attracting new client segments. Even if measurable ROI is uncertain, perceived capability drives commercial advantage. Differentiation becomes both a competitive and reputational asset.

Entry into New Segment(s). Automation can lower delivery costs, making it viable to compete in markets or client tiers that were previously uneconomic. Conversely, GenAI might allow expansion upward—eroding brand advantage and creating opportunities for challengers who work differently. Either direction broadens the addressable market.

Clients More Willing to Allocate Work to You. As GenAI reframes efficiency and innovation narratives, clients may reevaluate incumbents. Providers seen as ahead of the curve can capture reallocated spend from competitors perceived as lagging. This theory depends on client perception and their willingness to act on it.

Accelerated Upskilling. GenAI could compress training curves. Professionals become capable of higher-level work sooner, enabling leaner staffing and deeper leverage. Over time, this can enhance margins and scalability while improving talent retention through more engaging work.

Threats

Insourcing. Internal GenAI capabilities may enable clients to retain more work in-house. This is not merely the inverse of outsourcing, nor is it reducible to “fewer hours paid for.” Insourcing can rise even in scenarios where external spend also increases if overall demand expands or if internal capacity gains are unevenly distributed across work types. What matters here is the directional shift in client behavior: the degree to which GenAI tools improve internal productivity, confidence, and economics enough for clients to keep work that might previously have gone to external counsel. Because insourcing operates through its own mechanisms—organizational capabilities, budget decisions, adoption maturity—it warrants separate treatment as a distinct dynamic rather than a subset of pricing or volume changes.

Fewer Hours Paid For. This model assumes the billable hour erodes as automation replaces time (does the work) or displaces time (clients pay for outputs, not effort).

Lower Rates. Clients push to capture perceived productivity gains, arguing that GenAI-enabled efficiency should translate into lower average rates. This reflects a value-capture inversion—even where outputs improve, price expectations fall. The assumption is that clients view GenAI as a cost-saving tool.

No Returns to Scale/Less Lucrative Fee Structures. Efficiency gains fail to improve margins because clients capture the resulting savings or pricing pressure outweighs productivity improvements.

Lower Realizations. Perceived efficiency leads clients to heighten their scrutiny and demand more discounts, write-downs, and capped fees.

New Entrants into Your Segment(s). Just as GenAI may allow your organization to expand into new client tiers or adjacent markets, it also enables others to move into your space. If GenAI lowers barriers to scale, standardizes core capabilities, and reduces dependence on deep institutional infrastructure, then both traditional and nontraditional competitors—including ALSPs, consultancies, technology companies, and even clients themselves—can credibly compete for work that once required your footprint, brand, or legacy infrastructure.

Differentiation by Peers. Early adopters of GenAI who achieve visible, credible wins—whether through client-facing innovation, measurable efficiency, or strong public narrative—can reset market expectations for everyone else. Even modest pilot successes, if communicated effectively, can change the baseline for what clients believe is possible or expected. This can lead to expectation inflation across the market: Clients begin benchmarking every provider against the best stories they’ve heard, even if those examples are isolated, exaggerated, or unrepeatable. For organizations still in earlier phases of adoption, this dynamic can be destabilizing.

Homogenization. As tools standardize, capabilities converge. Differentiation declines, commoditization rises, and client procurement becomes more price-driven.

New Provider Types. AI-native platforms and service intermediaries emerge between clients and traditional providers, capturing workflow and data visibility. This creates disintermediation risk, particularly for routine or repeatable work.

Clients More Willing to Move Work from You. Incumbency advantages weaken as clients gain confidence experimenting with GenAI-enabled alternatives. Relationship stickiness declines, switching costs drop, and client bargaining leverage increases. Providers must continuously justify value amid shifting expectations.

Clients Demand/Capture GenAI-Enabled Savings. Clients explicitly seek to translate efficiency claims into lower bills or rebased budgets. Even when the efficiency gains are theoretical, the narrative itself becomes leverage in negotiations.

Deskilling. Automation reduces opportunities for early-career professionals to learn through manual work. This can threaten long-term capability development and succession planning, creating future talent risk even as short-term efficiency rises.

Taken together, the foregoing opportunities and threats form the crosscurrents of GenAI’s commercial impact. They are not forecasts; they are vectors of perception—the pressures and potentials shaping strategic behavior across the ecosystem. We expect your assessment to be impressionistic. The market is still forming, and even conflicting responses are valuable. The objective is to map how the industry is thinking and feeling its way forward.

These are not predictions—they’re signals of the assumptions, anxieties, and ambitions driving real decisions under extreme uncertainty.

The optional commentary box is there for you to identify additional opportunities or threats that you believe belong on this list but aren’t reflected in the structured response options. We know the landscape is evolving faster than any fixed framework can capture, and new crosswinds are emerging all the time—technological, regulatory, cultural, and competitive. Highlighting those unlisted dynamics helps the broader project stay current and complete. If you wish, you may elaborate on your structured responses—to clarify or contextualize your selections.