In consulting, there’s no hiding behind a product on a shelf. Your employees walk into a room, and their expertise is the product. Every client engagement depends on whether the right person has the right skills at the right time.
That’s why it’s worth paying attention to the way consulting firms like ZS think about workforce development. In any business where capability is a competitive advantage, the way your employees develop skills matters for bottom line business performance.
Jennifer Sutherland is the Global Head of Learning Enablement at ZS, a management consulting and technology firm with about 15,000 employees worldwide. Her team has learned a lot about building high-performing L&D functions under real, mounting pressures like flat budgets and rising expectations for workforce performance and growth. She shared what they learned and how they responded at Degreed’s LENS 2026 conference.
Making Skills and Capabilities the Infrastructure of Business
Business stakeholders are demanding results from AI investments and transformation initiatives. Timelines have compressed. Expectations are higher. You can’t use the old model to keep up with change anymore. If you spend five months building a four-hour AI training program, half of the content will already be obsolete when you roll it out.
That kind of learning doesn’t hold up to the needs of today’s workforce.
At ZS, they are fully aware that the old L&D model can’t keep up with the rapid evolution of skills and that making a change has a direct connection to business outcomes. “Being a consulting firm, skills are so important because we need to know how to staff our teams to projects,” Sutherland said.
That simple statement carries a lot of weight. Staffing decisions at a consulting firm are business-critical. The wrong skills on any given project can lead to poorer outcomes for the client, and that means poorer business outcomes for ZS. End of story.
In consulting and professional services organizations, skill data isn’t a nice-to-have for workforce planning; it’s necessary to operational infrastructure.
From complexity to capability
When Sutherland joined ZS, she found that the organization had genuine foundational investment in L&D, from strong platforms to good content. The problem wasn’t a lack of resources. It was friction and system complexity.
The natural response to pressure is to add more. More tools, more content, more dashboards, more processes. The result becomes accumulation: content and data in multiple places, workarounds built on top of workarounds, and a team spending its capacity managing complexity rather than building capability.
Sutherland’s advice for her peers? Ask: “What’s adding complexity to your processes, your systems, your workflows, but isn’t adding any value?”
High-performing L&D organizations, in her experience, treat capability-building as infrastructure. If it’s too complicated, it’s not working.
Three Steps to a Skill-Building Infrastructure
Sutherland outlined a practical model her team has implemented at ZS to effectively integrate learning as a business foundation. There are three essential levers.
1. Focus and Simplify
Most L&D teams can list 30 or 40 things they want to accomplish in a given year. But this list could be the problem, not the solution. For a consulting firm, the question of capability differentiators is key, and Sutherland challenges every L&D leader to be able to answer it with only three capabilities. Not initiatives, not job functions, but capabilities.
“Think through those top three capabilities that you want to focus on,” she said. “If what we’re doing doesn’t support growing these three capabilities internally, we need to rethink how we’re going to approach that.”
The discipline required to identify three differentiating capabilities creates clarity that cascades into faster decision-making. Then, teams can ruthlessly align learning time and money with those three things.
“Simplification is not about lowering our expectations. It’s removing the distraction. It’s clearing the noise,” Sutherland said.
There’s also an element of quality over quantity. Budgets are limited and a big part of simplification is doing more with less. “I expect those high expectations. But I also have to know how to manage that with those resources,” Sutherland explained.
At ZS, simplifying and focusing meant auditing their learning tech stack for overlaps and workflow dependencies, standardizing their intake process, and building a “How to Work with Learning” guide, so stakeholders understand what the L&D team actually does and what a training request requires.
2. Make Skills the Operating System
“Our skills are what people hire us for,” said Sutherland. That means developing those skills as well as (or better than) another company might know its own product—inside and out, no matter the scale. It means making skills a universal, shared language for leaders.
For example, the ZS team is launching an expectation framework that maps every learning course to seven defined expectations (e.g., Technology and Domain Knowledge or ZS Culture and Values). With Degreed, Sutherland’s team can see not only completions, but also what people are searching for, what they’re advancing through, and where they’re stalling. The goal is that, whatever a learner is working on, it connects back to something meaningful and shared across the firm.
For L&D functions trying to earn a seat at the business strategy table with the C-suite, this is the mechanism to get there. Skill data, tied to learning and mapped to business expectations, turns L&D from a service function into a strategic asset.
Not to mention that going skills-based supports other important internal functions and goals. Deloitte found that organizations who do adopt a skills-based approach are 107% more likely to place talent effectively and 98% more likely to retain high-performing employees. When a business adopts a skills infrastructure, it provides a common architecture and flow of resulting benefits across the business.
3. Embed AI in the Learning Workflow
Human-enabled, but AI-powered. Sutherland explained that this is an important distinction to make when making AI and learning part of a broader infrastructure.
The most visible example of this at ZS is “My Virtual Coach,” which is their internal deployment of Degreed Maestro. “You don’t want a system or a tool or technology to give you feedback. You want it to be human enabled,” she said.
The early results have shown employees using Maestro’s interactive coaching capabilities to prepare for difficult conversations, work through conflicting feedback, and take action on the development insights managers provide to direct reports. This functionality helps ensure that human-enabled performance feedback is carried through into active, AI-enabled interactions and practice that can help improve key performance areas. But what makes it even more relevant to ZS is that My Virtual Coach leverages company knowledge, values, vocabulary, and context when it provides feedback. “It wasn’t just general information. It has our terminology. It has our infrastructure,” Sutherland said.
That specificity matters. A generic AI coaching tool can only offer generic advice.
Beyond coaching, Sutherland described how AI has reduced friction in daily operational workflows for her team: tagging courses, managing content versions, and automating email responses from a shared inbox that receives 100+ messages daily.
“AI, as part of our processes, helps reduce some of the friction of some of that stuff. It helps us scale the efficiency within our team, but it doesn’t replace judgment,” she explained.
Working Smarter with Better Design and Learning Data Infrastructure
For consulting firms, effective skill-building infrastructure is table stakes. When employees and their expertise are the product, learning has to be organized, connected, and ultimately oriented to organizational strategy.
That sound foundation means fewer initiatives, better alignment, clearer capability targets, and AI embedded into daily workflows. It leads to smarter resourcing and more focus, organization-wide.
It’s ever more important for organizations and learning functions to be focused, skill-centric, and AI-powered. Human capability is rapidly becoming a hallmark differentiator, and a learning-driven infrastructure could become the next big competitive advantage for innovative businesses.
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