With AI moving into the core of how businesses operate, the very nature of professional services (PS) is set to change. New roles will emerge, delivery models will evolve, and the expectations placed on services teams will look very different from today.
The future of PS will be about rethinking what human expertise means in an AI-first world, and how services organizations create value in new ways.

The opportunities ahead are enormous, but so are the questions: What kinds of work will AI absorb? What new roles will rise in its place? And how will PS leaders need to reimagine their teams to thrive in this new landscape?

In a recent conversation, Srikrishnan Ganesan, CEO and co-founder of Rocketlane, and Brian Hodges, President and Co-founder of nCloud Integrators, explored all these questions and more.

Brian has decades of experience running large-scale organizations. Before founding nCloud, he led the global professional services team at Informatica Corporation for 20 years, growing the practice to 450 consultants worldwide.

nCloud Integrators is a professional services organization that supports customers with their professional services, customer success, and data integration efforts. Over the last seven years, nCloud has collaborated with more than 600 customers, particularly those aiming to advance their digitization and optimization initiatives by leveraging AI and other cutting-edge tools.

Read on for an insight-packed summary of the conversation.

Expanding the role of professional services with AI

For PS leaders, the real question is: will AI simply make us more efficient, or will we use it to be more ambitious?

Today’s SaaS products typically cover about 80% of a customer’s needs out of the box. It’s the final 20%, the messy, complex, customer-specific work, that requires PS expertise. Historically, that’s where projects stretch out, requiring senior technical talent and adding cost. But AI changes the equation. 

With the latest advances in AI, services teams can now co-create, adapt, and solve these complex problems in real time, turning the last 20% from a bottleneck into a competitive advantage. Imagine expanding your reach from the 20% of customers you actively serve in onboarding cycles to 50% or more of your customer base, including those struggling with product gaps, waiting on roadmap items, or facing unique challenges that aren’t even in development plans. 

AI allows professional services to become the team that truly translates product possibilities into customer outcomes.

"Vibe Coding" and its potential for Professional Services

Vibe coding or using natural language prompts to generate applications, workflows, and automations represents one of the most transformative opportunities for PS teams. Instead of writing complex code for every unique integration or extension, consultants can now move from idea to prototype in minutes, not weeks.

The impact is already visible. For instance, Vanguard is using AI prompting to create internal tools up to 40% faster, while Choice Hotels empowers its non-technical staff, like designers and project managers, to generate code snippets that directly improve guest experience. For PS, this means that even newer consultants or non-developers can now build interactive apps, lightweight calculators, or customer-facing tools without leaning on scarce senior developer capacity.

Vibe coding lets PS teams deliver the “last mile” of value faster, more collaboratively, and in ways that feel tailored to each customer’s needs. Imagine co-building an ROI calculator or onboarding checklist with a client in real time, rather than scoping, coding, and handing off weeks later. That’s the cultural shift: from rigid execution to live, collaborative innovation with customers.

Of course, vibe coding isn’t a silver bullet. Foundational skills still matter; knowledge of APIs, UX design, and problem-solving frameworks remain critical for customer-facing apps. 

The future lies in a hybrid model: empower a broader set of team members across PS, CS, and even sales to create with AI, and have expert “unicorn” resources review, refine, and harden those solutions. 

How the Rocketlane team is experimenting with vibe coding to unlock new possibilities

One of the most exciting developments at Rocketlane right now is how the team is experimenting with vibe coding through the new Custom Apps capability. The idea is simple but powerful: instead of waiting for the product roadmap to deliver every specialized feature, teams can now build lightweight apps that slot directly into Rocketlane.

Here are a few early examples of what the team has built:

• ServiceNow integration: A custom app pulls a customer’s ServiceNow tickets directly into their Rocketlane project, giving teams a unified view without switching between systems.
• Custom dashboards: A CSM with basic API skills built a personalized dashboard showing task and time distribution, data that already existed in Rocketlane, but now presented in the exact view the customer requested.
• A “Resource management queue”: A CSM created a booking-status field in the resource request queue to solve a client-specific need that wasn’t on the product roadmap. 
• Custom client-facing experiences: Using the customer portal, teams have built tailored experiences like embedding e-signature tools or offering a services catalog where customers can request new services directly.

These examples show how PS, CS, and even sales teams can evolve into true bridging teams. A solutions engineer, for instance, could spin up a small custom app mid-sales cycle to demonstrate how the platform addresses a prospect’s unique requirement.

But with this new flexibility comes responsibility. Without structure, organizations risk an unmanageable sprawl of apps. That’s why Rocketlane is introducing a marketplace model, where custom apps can be approved, documented, and shared. This ensures that every solution remains maintainable, reusable, and part of a growing library of customer-ready tools.

How the vibe is shifting in professional services

Professional services has always been about expertise delivered through structured projects and human relationships. That model is being rewritten in real time through key shifts such as the move from:

• Delivery to orchestration
  Traditional PS centered on delivering defined outcomes such as implementations, migrations, and process rollouts. In an AI-driven future, much of the delivery will be automated or accelerated by intelligent systems. The professional’s role shifts toward orchestrating outcomes: aligning AI capabilities with client needs, interpreting system recommendations, and making judgment calls that algorithms cannot.
  
  
• Fixed scope to adaptive engagement
  Engagement models have historically been project-bound, scoped tightly, and delivered over weeks or months. With AI, PS becomes more fluid. Professionals are embedded as ongoing advisors, helping clients adapt as AI models evolve, regulations shift, and new opportunities appear. This means PS work is no longer about “closing” projects but about sustaining value over time.
  
  
• Functional expertise to systems thinking
  In the past, PS teams were built around specific functional or technical expertise. In the future, expertise expands to include model governance, data ethics, AI reliability, and cross-disciplinary fluency. What matters is knowing the system, and understanding how it behaves, how it learns, and how to keep it aligned with business and human goals.
  
  

As we embrace this new era, several key roles are becoming more prominent in the field:

• Process Designers: These individuals are rethinking how services are delivered in an AI-driven world. They're designing new systems that combine automation with the nuanced judgment of human experts. Their goal is to create efficient processes that allow teams to do more with less effort while maintaining high-quality results.
• Agent Builders: With the rise of "agentic" AI, many tools are integrating AI agents. Agent builders are the experts who design, train, and manage these agents to perform tasks, orchestrate workflows, and interact with other systems, ensuring they add real value without creating new complexities.
• Customer Engineers: This role is a direct result of advancements like "vibe coding." Instead of needing a deep technical background (e.g., 10+ years of Python experience), these professionals can use natural language tools to create UI extensions, small integrations, and custom apps. They act as the bridge between customer needs and technical solutions, delivering last-mile value without the traditional development overhead.
• Forward Deployed Engineers (FDE): This term is gaining traction and represents a more proactive, customer-centric approach. These engineers work closely with clients to understand their needs and deploy solutions on-site or in close collaboration with the customer's team. Instead of waiting for the product roadmap to catch up, FDEs can:

• Build beyond the product: They create the necessary integrations, custom tools, and agentic workflows to ensure the customer gets value immediately. For instance, this could involve a RAG (Retrieval-Augmented Generation) implementation to fine-tune an AI model for a customer's specific data, or creating an additional UI to help them better leverage the AI.
• Rapidly prototype and iterate: By working directly in the customer’s environment, FDEs can quickly identify problems, build solutions, and get immediate feedback.
• Inform the product roadmap: The knowledge and patterns they discover while "in the field" are then brought back to the core product teams, ensuring that the company's long-term strategy is directly informed by real-world customer needs.

3 frameworks to guide your AI journey

Adopting AI in services is less about the technology itself and more about how organizations choose to frame the journey. Several models can help structure that thinking, not as rigid playbooks, but as lenses for deciding where to begin and how to move forward.

The EASE model

A useful frame is EASE: Evaluate, Assess, Strategize, Execute.

• Evaluate: Start by mapping today’s reality, i.e., the projects being delivered, the workflows that support them, resource allocation, and points of friction. These friction points might show up as lost margin, wasted time, or recurring frustrations among consultants.
  
  
• Assess: Once that landscape is visible, the next step is prioritization. Which problems matter most? Which problems create the largest losses in value, time, or employee energy? These become the most promising candidates for automation or AI support.
  
  
• Strategize: From there, build a roadmap with clear phases and success metrics. AI adoption should be tied to outcomes, such as what improvement is expected, how it will be measured, and how results will be validated.
  
  
• Execute: Begin with small pilots, track outcomes, and scale what works. Document lessons learned to refine the approach and keep momentum.
  
  

The Brain–Eyes–Hands model

Another way to think about AI adoption is through the metaphor of Brain, Eyes, Hands:

• Brain: Build an organizational brain by consolidating knowledge scattered across teams and making it easily accessible, whether through custom GPTs or emerging purpose-built tools.
  
  
• Eyes: Expand organizational visibility. Leaders and experts need clearer signals, such as where engagements are at risk, which opportunities need attention, and where quality or compliance requires oversight. AI can bring these signals to the surface.
  
  
• Hands: Extend capacity. AI can take on routine or manual work, giving consultants more “hands” to focus on higher-value activities.
  
  

This model emphasizes AI as augmentation by strengthening collective intelligence, awareness, and execution.

The Team-First principle

No model succeeds without the people who deliver the work. Consultants often do not spend their days thinking about AI strategy, so deliberate effort is needed to bring them along. That may include:

• Starting open conversations about changing roles.
• Identifying individuals who are already experimenting with AI tools and can serve as internal champions.
• Anticipating and addressing skepticism or negative sentiment.
• Creating structured opportunities for experimentation, such as workshops or hackathons, where consultants can test ideas, build prototypes, and see for themselves what AI makes possible.
  
  

These frameworks illustrate how AI in services should be approached: with structure, with imagination, and with a commitment to rethinking what services can become. Efficiency will always matter, but ambition is where AI’s true potential lies.

Q&A on navigating AI and professional success challenges

How will AI and “live coding” change professional services pricing? If work that used to take 1,000 hours and cost $150k can now be done in a fraction of the time, should services firms charge less, or hold prices steady and increase margin?

The old pricing model begins to break down once AI accelerates delivery. As efficiency improves, pricing shifts naturally toward outcome-based models. The client isn’t paying for effort; they’re paying for the business result. Faster delivery reduces risk, increases confidence in outcomes, and makes fixed-fee pricing more viable.

Another dynamic is market expansion. When a project priced at $150k becomes accessible at $20–30k, the potential buyer pool grows. Lower costs increase adoption, create new entry points for existing customers, and open up entirely new customer segments. The net effect isn’t just margin gain, but demand creation.

The key is to aim for balance: retain some of the efficiency as margin, but pass most of it to customers to drive scale. 

A recent MIT study suggested only 5% of AI projects succeed. How should PS leaders interpret that?

Success rates depend heavily on framing. Many failures stem from starting with “bring AI into the enterprise”, which is a broad ambition with no clear use case. Without defined friction points, validation steps, or success metrics, adoption stalls.

The more effective approach is methodical scoping to:

1. Define the use case.
2. Identify the point of friction being solved.
3. Quantify the value of resolution.
4. Establish verification methods and success metrics.
   
   

When AI projects are structured this way, the ROI becomes measurable and adoption sustainable. Professional services, in particular, tend to be closer to outcomes than other functions, which makes them better positioned to land in the “successful minority.”

How realistic is it to expect consultants to use AI to build on top of products directly?

Two dimensions come into play here. First, extensibility: every SaaS product spawns a constellation of integrations, workflows, and customer-specific extensions. AI can accelerate how these surrounding tools get built, tested, and deployed.

Second, embedded platforms: some vendors are building environments where extensions can be developed directly within the product itself, reducing friction further. Early experiments use AI to scaffold apps, generate code, and deploy into managed marketplaces with guardrails for data storage, retrieval, and customer visibility.

This is still emerging, but the trajectory is clear: AI will increasingly act as a force multiplier for customization, allowing customers and partners to extend platforms faster, with less reliance on deep engineering capacity.

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