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Building the Tercera 30: Where AI Helped and Hindered

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At Tercera, we spend considerable time advising business leaders on how AI is impacting the way work is delivered. So we are always looking for ways to take our own medicine and responsibly weave AI into our own processes.

When it came time to develop this year’s annual Tercera 30 report, we knew this was the perfect opportunity to leverage AI in a purposeful way. Over the last four years, we’ve built up a significant amount of data on software vendor ecosystems. This, along with our deep connections with software channel partners, bankers, analysts, and service providers in these ecosystems, gives us a lot to work with. However, we are still a small team without a dedicated research arm. 

We started by looking at our existing processes and where we could leverage the foundational enterprise AI tools we already use (e.g., ChatGPT, Claude) and the AI capabilities embedded within our existing productivity tools (e.g., Google, Box, HubSpot, and Microsoft). These processes include: data collection, data validation, report development, and marketing and promotions.

From there, we looked at how we could use fit-for-purpose AI platforms and agents to do things that would’ve been difficult or impossible to do without giant budgets, or an army of analysts. This included interviewing or doing deep research on smaller, less established software vendors where we may not yet have direct relationships, or getting direct insights from senior enterprise IT leaders to understand how they’re buying, building or adopting AI platforms. 

The two tools we ended up leaning on the most were ChatGPT Deep Research and Bridgetown Research’s agentic research platform. 

ChatGPT: The Swiss Army Knife for Research 

ChatGPT proved most helpful at filling in basic data in our repository of company information. In batches, we were able to create tables including information such as website, company description, public/private, etc. formatted in a way we could easily import into our existing data set. Of course, there were more than one instances of hallucination or incorrect information, so it still required a fair amount of double-checking data against past data we had collected and other sources. This should require less training in the future based on this year’s usage. We found consistent value in ChatGPT’s Deep Research function to develop a base-level understanding of corners of the market that we were unsure about. 

Bridgetown Research: Agents to Go Wider, Deeper, Faster

We were first introduced to Bridgetown Research through one of our Advisors, and were immediately impressed by their vision to transform how primary and secondary data are gathered and analyzed, achieving scale, speed, and cost efficiencies that were previously difficult or nearly impossible. At its core, the Bridgetown platform uses autonomous AI agents to conduct expert interviews, collect data, and run custom analyses. It’s still early days, and we only scratched the surface of what Bridgetown’s platform can do, but we were impressed enough that we are considering incorporating it deeper into our AI-stack for value creation advisory work. 

Lessons Learned for Using AI in Research

When it comes to incorporating AI deeper into our processes, we learned first-hand just how important it is to experiment. Some of the tools and ways we leveraged AI in this process exceeded expectations. At times, it was more trouble than it was worth. 

It reinforced to us that AI can be extremely powerful as part of the research process. However, its impact is best felt when applied not generally, but purposefully. 

Here are 5 other lessons we learned along the way.

1. ChatGPT is a utility player, but not the best tool for everything

General-purpose GPTs are table stakes for the modern worker. Like Word or Excel, they are a base tool you should reach for by default to draft, summarize, run first-pass research, and structure messy information. In our process, ChatGPT sped up recurring tasks like populating company metadata into import-ready tables and spinning up quick briefs so we could engage experts faster. But just as a growing company eventually moves from tracking pipeline in a spreadsheet to implementing a CRM, you’ll eventually outgrow what a generalist GPT can do on its own. 

The real transformational change in our research process came from pairing a generalist GPT with a purpose-built tool.

Bridgetown Research’s platform expanded our reach by using agents to coordinate expert and buyer interviews and generating targeted insights we could not reliably get from a generalist model alone. Its “Bridget” assistant answered research questions more effectively because it was tuned to market-research workflows and grounded in the data we supplied. 

As scope and complexity increase, you need tools that are designed for the specific job.The model you choose matters: different GPTs handle retrieval, structure, and citation differently, and task–tool fit drives quality. 

2. There is still a role for traditional research tools

AI helped us move faster and cover more ground, but it did not replace the basics. We still leaned on the traditional research and analysis tools. For example, we commissioned a third-party research firm to survey 321 current services leaders to gauge how teams were actually implementing AI and thinking of partnership strategies. 

We also built a straightforward scoring model to evaluate ISVs on service potential and growth. The weightings and KPIs came from proven ways of consolidating and analyzing data, which kept the results anchored in reality rather than a black box. Then came the hands-on work that turns data into insight. We used Excel to clean, weight, and combine variables, and to turn the results into a clear 2×2 grid that made the tradeoffs easy to see. 

Just as important, we talked to people. Interviews and live conversations with services leaders, ISV channel leaders, and analysts gave us context, helped validate surprising findings, and turned the numbers into practical guidance. AI gave us speed and reach. Traditional research tools and real conversations made the output trustworthy and useful. 

3. Challenge the output at each step 

Reviewing any AI output is essential, but it’s just as important to understand the inputs and sources from which the AI tools are pulling. In more than a few instances, we found data was wrong because it was pulled from a source that was wrong and not reliable. A prime example of this was a higher-than-expected rate of misidentifying whether or not companies were publicly traded. Double-checking outputs like this is a given for anyone using AI to produce deliverables. And while this may seem like a no-brainer, Deloitte’s recent blunder shows how easy it is to skip this step in the name of efficiency and speed. 

Another problem we encountered was a feedback loop involving our own data from the report. As we began to query in more specific areas, multiple different AI research platforms began to feed us back data from our past Tercera 30 reports. While upon first glance this was incredibly self-validating, the danger is exactly that. We ran the risk of blindly validating ourselves with stale data and commentary without challenging our narrative based on other resources and data from the past year. Luckily, we were able to identify this and use our previous thought leadership as contextual input, while ensuring we incorporated third-party resources. GPTs and agents are designed to interact with data that already exists and consolidate. Even then, the outputs are not consistently reliable (although accuracy continues to increase). 

4. Agents are powerful, but they can’t replicate a human experience

While agents can do many things, the power of a human relationship remains one thing that they can’t replace. We found great value in using the Bridgetown platform agents to conduct interviews with key market stakeholders, but we also found tremendous value in calling upon our professional relationships to validate (and debate) the selections we made for this list. 

Not only do we know where these individuals stand and what might be influencing their opinions, but through these conversations, we garnered insights that likely would not be shared with an agent that people don’t have a personal relationship with.

Exposure to expert and on-the-ground opinions is not only crucial to our process but the secret sauce to the Tercera 30.

While anyone with enough time and resources can replicate the base process that we have built, it’s tough to replicate relationships and discernment.

In a similar vein, it is dubious to expect agents and GPTs to automatically reflect the decades of institutional knowledge and context that we bring to the analysis and scrutiny of the data we gather for the Tercera 30. Unlike relationships, this can be trained into models over time, but it requires the foresight and structure to do so. Even then, these models will need to be updated by the rapid changes and learnings that we gain over time. 

5. AI cannot run the whole project (yet)

AI gave us speed and reach, but it couldn’t (and we didn’t expect it to) fill all of the gaps across an end-to-end project like this. At this point, AI wasn’t able to design the right models for our goals, drive the sequencing of work across teams, or keep all the moving pieces on track. We still needed people to frame the problem, pick the methods, set quality thresholds, and decide when to pivot. Beyond basic, repetitive tasks, AI still isn’t reliable at carrying that context through the full lifecycle.

The creative side is another limit. While AI can draft copy, suggest layouts, and generate visuals, the final deliverables still required human taste and judgment to tune the voice, ideate and contextualize data visuals, and make choices that fit our brand and audience. The tools are getting better every day, and we will keep experimenting, but they are not ready to own creative direction or produce truly differentiated deliverables by themselves. AI is a strong contributor. It is not the project owner.

The Future

AI tools are moving fast. What was unreliable this cycle could be ready by our next round of research. We will keep testing and adopting the best options for our work, and we will fold standout platforms into the ever-growing list of companies we consider for the Tercera 30.

Blurred lines: software and services converge

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How AI is redrawing the map for tech services firms

One of Tercera’s long-standing mantras for tech services leaders has been to pick a lane – services or software. You can’t be both. 

But in the AI era, that mantra no longer holds true. 

AI is not just disrupting how services are delivered, it’s collapsing the traditional boundaries between what a services firm is and what a software company does. 

AI has begun to take over tasks that once sat squarely in the domain of professional services: pulling and analyzing data, creating reports and nurture flows, developing and testing applications, migrating legacy code to modern tech stacks…you name it. More and more of those capabilities are being embedded directly into software workflows. Salesforce’s AgentForce, SAP’s Joule, and ServiceNow’s AI Now platform are great examples of this at play.

At the same time, forward-thinking services firms are starting to leverage AI to evolve their own offerings. They’re using co-pilots to move faster, and building their own agents to automate full processes that could once only be done by humans. And a growing number of firms are building their own software – not accelerators and reference architectures, but full platforms.

The rise of Services-as-Software

In our latest research report, A New Services Playbook for the AI Era, we explore the rise of a new category called services-as-software. Analyst group HFS, which coined this term, defines this category as AI-first offerings that automate traditionally people-based services, delivered through repeatable, software-like experiences, sold in a subscription or consumption model. 

And we’re seeing this model everywhere. 

Publicis Sapient’s AI-powered Slingshot platform promises to automate everything from prototyping to coding to maintenance. Accenture’s AI Refinery promises to help companies deploy agentic AI safely. Infosys markets a collection of solutions, including hundreds of industry-specific agents, under the Infosys Topaz brand.

It’s not just the large global firms though. Dozens of services-as-software startups are coming onto the scene to automate segments of the services space. Companies like Bridgetown Research, Auctor AI, Echelon AI and Rocket (just to name a few) are automating tasks that used to be purely human, and customers and software partners are starting to take notice.

And in between, are thousands of mid-size services firms. These firms don’t have the luxury of building from scratch, or the big R&D budgets to invest in developing sophisticated platforms from scratch. They are feeling the pressure, but also aren’t sitting still. The smart firms are getting creative with their investments. They are co-developing alongside customers and partners, extending platforms and diving into vertical use cases. They’re embedding tools and third-party services-as-software within delivery processes, and redirecting funds from stagnant areas or raising capital) to build out IP. They’re consultants – they adapt! 

This blurring of lines is forcing service leaders to take a hard look at their future direction. Are they growing a services firm or building a software company? Or increasingly common, how do they successfully do both when the DNA of services and software companies is very different?

This blurring of lines is forcing service leaders to take a hard look at their future direction. Are they growing a services firm or building a software company? Or increasingly common, how do they successfully do both when the DNA of services and software companies is very different? This decision has significant repercussions on many other downstream decisions – from how budgets are allocated, to go-to-market strategies, to the types of employees that are hired, to how they engage with their partners.

When Software Players Move Deeper Into Services

There’s no question the partner playbook is evolving. We’re seeing more software and AI companies leaning more heavily on their captive professional services teams. Not to replace partners, but to drive deeper consumption of their AI capabilities and maintain proximity to the customer where models are being trained and tuned. Their ‘land and expand’ mindset now includes not just license growth, but usage, outcomes and training. All of which require more human intervention.

This trend isn’t new, but it is accelerating. Some vendors like Palantir and IBM have had a blended services and software model for years. Palantir, which started as a services company and gradually became more productized, has been winning deals for years with its white glove Forward Deployed Engineers who work alongside customers to ensure the software performs and evolves. IBM has been focused on software and services for decades. Its global consulting business generates nearly a third of IBM’s total revenue. 

Some vendors, like OpenAI, are newer to the consulting game. OpenAI’s move into consulting felt like the shot heard round LinkedIn with many people suggesting this was the end of consulting. Yet OpenAI has been clear that (for now) their consulting arm will only tackle engagements that begin at $10 million. That leaves a lot of real-estate for other partners to play.

In our Tercera 30 research, one of the metrics we consider (for public companies that report it) is the percentage of revenue delivered through internal services, and how that is trending over time. We look at this metric because internal services arms can make it more difficult for services partners in the ecosystem to compete. 

Typically, software firms try to keep services to a relatively small percentage of revenue. Partly because margins are lower on services and partly because it keeps the company focused squarely on building the core product. However, as services margins increase and software companies focus on consumption, we wouldn’t be surprised to see these numbers will tick up. 

That in no way spells doom for external services partners. Services partners will still be invaluable to growth, and may become even more important as software firms look to drive value and consumption of their offerings. However, the partner playbook will change. Something that we tackle in our new research report.

The future belongs to the bold

This new world brings uncertainty, yes. But it also brings tailwinds for those that choose to ride them. According to our latest survey of 320+ tech services leaders:

  • 90% believe that their partners’ investments in AI will create new business opportunities for their firms
  • 95% say they’re already getting AI-related support from partners
  • 47% tell us that a significant portion or nearly all of their revenue is coming from AI work

In other words, most firm leaders see the trend not as a threat, but as an opportunity. An opportunity to build differentiated solutions, not commoditized services. To create more value for customers and the partners that they work with. And to capture a larger share of that value as well.

We’ve said before that AI changes the game in services. It resets the scoreboard. We believe the most successful companies in the AI era will see themselves as hybrid organizations, firms that blend automation with human expertise, and product thinking with an advisory mindset. Those that focus on long-term customer value, not just successful implementations and projects. Those whose growth isn’t completely tied to how fast they can hire. 

This next era of technology demands bold moves and shaking up beliefs that have held for decades. We’ve had to shift our own thinking because of this. So what long held mantras are you re-evaluating?

Can traditional services compete with AI-native firms?

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We’ve all seen the headlines: AI leaves systems integrators behind! Services as Software comes for consultants’ jobs!

There is no doubt that AI is fundamentally altering the tech services landscape, however, we do not believe that AI will be the extinction event for services that so many predict. 

For decades services firms have had to reinvent to stay ahead of their clients. It happened when mainframes shifted to PCs, when the Internet took hold, and across all three waves of cloud computing. 

The partners at Tercera have built and invested across a number of different eras, and in every one of them, we’ve seen companies adapt and reinvent. I think back on Microsoft and Oracle’s struggles in the early days of the cloud, and how so many (including me) dismissed them as dinosaurs who would never be able to quit their cash cow businesses. Today, these two companies are leading in the AI era, and their market caps show it.

That said, for every one company that adapted, five didn’t. Just look at Peoplesoft and Siebel in software. Or EDS as the cautionary tale of a services firm that completely missed the move to cloud and new sourcing models.  

New eras reset the scoreboard, and the tech services game is about to get heated. 

It’s why we developed The New Services Playbook for the AI Era – a research-based, practical guide to how services are evolving and what to do about it. The playbook includes data and insights gleaned from a survey of 300+ services leaders, along with interviews with enterprise buyers, software vendors, traditional services leaders and founders building in the AI era. 

The Rise of AI-Native Firms and Services-as-Software

It’s clear that AI is putting pressure on services businesses, and in the next five years, we believe growth for traditional labor-based services will slow or even decline while the market learns what AI can and can’t do. However, AI in the long run should lead to more service opportunities, not less. 

Every process, business decision and tech investment will need to be rethought through an AI lens. AI will solve one set of problems faster and more cost effectively than before, but it will exacerbate and create entirely new challenges that will need to be resolved. 

Who solves those problems, and how those services are delivered, is changing though.

AI-native firms – companies built from the ground up, or completely rewired, to put AI at the center of their operations and offerings – are changing the narrative. AI-native firms don’t just use AI, they’re rethinking entire value chains through the lens of AI so they can move faster, deliver at dramatically reduced costs, and tackle problems that were once out of reach. It’s these AI-native firms, not AI, that are the existential threat for legacy service providers, and they are coming for their customers, employees, partners and investors.  

It’s these AI-native firms, not AI, that are the existential threat for legacy service providers, and they are coming for their customers, employees, partners and investors.  

Many AI-natives are building their offerings in a new Services-as-Software model – an AI service layer that automates traditional services, and delivers them through a repeatable, software-like experience and subscription model. Analyst firm HFS, which initially dubbed the term, believes this could be a $1.5 trillion in the next 10 years, and expects this new category to take share from both software and traditional services. This won’t be too dissimilar from what happened with on-premise software when SaaS and cloud computing took hold.

Why are AI-Natives Winning?

AI-native firms are getting attention from customers and investors for a number of reasons:

Accelerated Delivery, Higher Margins

AI-native firms can deliver results far faster, at higher margins by automating tasks that once required large teams and manual processes. Similar to the cloud shift, we believe speed and cost will be early drivers of AI adoption and will put near-term pressure on what firms can charge for their services. However, innovation is the real game changer and clients will pay for that.

Ability to Charge Based on Output, Not Input

AI lets services firms shift from charging for inputs (hours, headcount) to charging for outputs (results, adoption, performance). As low-value busy work gets automated, a partner can spend more time delivering value further up the stack. Charging for value delivered, not how long or how many people it took to solve a problem, has been the holy grail for services firms. However, the use of AI is now forcing it. 

Talent Leverage

Rather than scaling through armies of junior consultants, AI-native firms can use AI and agents to handle repeatable tasks. Junior talent will still be needed, but there may be fewer roles. Senior talent can focus less on recruiting and on-boarding, and more on solving client problems and discovering new revenue streams. In theory, these firms are more resilient to attrition and bring in much higher revenue per employee, up to 30-40% higher in some cases.

Market differentiation

With AI central to their brand, offerings and culture, AI-native firms are getting attention from large customers who recognize that navigating the AI era may require a different type of services partner. Customers who are fed up with wasting millions on failed, multi-million dollar, multi-year transformation efforts and want partners who look and operate differently.

Traditional Services Firms Fight Back

All this said, tech services leaders see what’s coming. They aren’t sitting on the sidelines.

This summer we surveyed more than 300+ tech services leaders to understand how they were incorporating AI into their operations and future strategies. Nearly 100% of the respondents said their firm was using AI – 85% said AI was a top 3 priority, and nearly 30% said it was their number one priority. 

The biggest area of investment for services firms today is in service delivery execution. This is an area where the AI-native firms are leaning in hard to automate, and traditional firms know this is their revenue engine.

The majority of respondents were also increasing their use of alternative pricing models, with 45% of respondents already experimenting with outcome or value-based pricing.

Existing firms aren’t sitting still. They also possess several hard-won, wide-moat strengths that they can capitalize on to compete with AI-native entrants. One strength is their deep experience with complex enterprise environments. Newer entrants are bound to underestimate the messiness of large organizations and the complex realities of integration and legacy systems. 

Existing firms aren’t sitting still, and they possess several hard-won, wide-moat strengths that they can capitalize on to compete with AI-native entrants.

Additionally, strong customer relationships and trust, built over years of delivering services, remain a major advantage. Clients are people, and people look for trusted partners who have been in the trenches with them, who can provide cover when things get tough, and act as a “throat to choke” if outcomes fall short. While many legacy firms are still coming up to speed on AI, these firms deeply understand issues like data privacy, governance and regulations, security and change management. All of which are hugely important in the AI era.

Traditional firms that can combine their existing expertise, relationships and human judgement with the speed and capabilities of AI and agents, may find they have a leg up on AI-native firms. Especially if those new, tech-focused firms ignore the first principles of consulting in their rush to automate. 

The Game Changes, But We’re in Early Innings

We’re still early in the game. Many companies are building products to automate parts of the service delivery lifecycle, but true AI-native firms that can navigate complex enterprise environments and deliver fully trusted solutions using AI agents still remain relatively rare. For now. 

Today, only 6% our survey respondents said they would characterize themselves as an AI-native firm. Six months from now, we expect that number to increase dramatically, especially with the amount of capital flowing into this space. Every day we hear from engineers from tech titans or senior leaders from brand name consulting firms leaving to form AI-native firms, with the goal of chipping away at the trillion-dollar IT services industry.

But it takes time for true market transformations. If you want inspiration for how this could play out, take a look at the electric vehicle (EV) market. The first mass-marketed EV from GM was launched in 1996. Nearly 30 years later, the EV market is booming but it’s still only a quarter of the market. Not everyone is ready for an EV yet – the infrastructure is still evolving in some markets, the technology is still advancing, and behavior change takes time.

It’s why hybrid vehicles are so popular. They combine the familiarity and convenience of gasoline with the next-generation benefits of renewable power. Hybrid service models may see a similar path, combining the advantages of established ways of working and new technologies. 

However, AI is evolving faster than anything we’ve seen in the past. If these AI-native firms and leaders can deliver on their promises, and can scale fast without needing to find, hire and onboard thousands of consultants every year, they’ll take share fast – from both traditional software and traditional services firms.

How To Stay on the Winning Side

The best way services firms can stay on the winning side is to know that the game is changing, and that the playbooks that worked five years ago won’t apply in the next five.  It’s why we wrote the New Services Playbook for the AI Era. 

Here are a few pieces of advice pulled from the playbook to help you navigate this new world we’re in. Because it’s not the first time services firms have had to reinvent themselves, and it won’t be the last. 

1. Know thyself

Reconfirm your firm’s value and identity, and define how that can evolve to redefine your competitive moat.

2. Move up the stack

If your value proposition is becoming commoditized, determine how AI can enable you to transition to a differentiated, higher value service.

3. Double down on outcomes and client service

Capitalize on your competitive advantages – experience and expertise – and on delivering the value that your clients seek.

4. Be client zero

To be an AI consultant, your team and leadership, especially the CEO, must be power users. Avoid the risk of falling back on legacy ways of working while the market surges ahead.

5. Tap into collective intelligence

Deploy your firm’s collective expertise inside to redefine partner relationships and how you co-create with them and customers. Consider the potential of agent-to-agent collaboration.

6. Remember it’s still a people business

Double-down on your relationships. Both your employees and your most important customers as you navigate this transition. For centuries, people have been hiring, buying from and working with people. That isn’t going to change. At least not anytime soon. 

First principles of building a great services firm

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Building a great services firm isn’t easy, but it isn’t rocket science either. It really comes down to three simple things: customers, team, and financials. 

I like to think of these as dials that you can turn up or down based on the conditions of the moment. Rarely are these dials perfectly balanced. During the Great Resignation or in the war for AI talent, the “team” dial may need to get spun up to hire and retain the best people. In a downturn, like the Great Recession, the “financials” dial may need greater attention because customers and capital aren’t as plentiful. And, when competition heats up or buyer behaviors fundamentally shift, the “customers” dial may need the most attention. 

Leadership is the art of turning the right dial at the right moment without breaking the system. 

So how do leaders know when and how to tune those dials? I wish there was an easy answer. 

A lot of it comes down to experience and intuition, which is why investors tend to gravitate toward repeat founders or executives who’ve been in the seat before. They’ve already made the rookie mistakes. They’ve seen how cranking one dial in a business can unexpectedly throw the system off balance, or create consequences that can be difficult to unravel.

But that doesn’t mean first-timers can’t get it right. Many do. And often, they do it by looking at situations based on first principles. First principles thinking forces you to strip away complexity, assumptions and circumstances until you’re left with the foundational truths of what makes a great business. And what makes yours a great business. 

First principles thinking forces you to strip away complexity, assumptions and circumstances until you’re left with the foundational truths of what makes a great business. And what makes yours a great business. 

After 30+ years in tech services, I’ve seen a lot of different leaders and firms, and the great ones all focus on certain first principles. Every firm may have a slightly different view of their first principles, but here are six principles that have guided me and many other successful services leaders over the years.

#1 – Customer-First

Great firms are almost always obsessively customer-focused. Everything starts and ends with the customer in mind. Even when the customer dial has to get turned down (say to rebuild the team or improve profitability), the best firms still think outside-in, not inside-out. They don’t let themselves fall into a “guild” mindset, where they define themselves by what their team is great at, or what their partners are pushing that quarter, or what their experts want to build. They start with the customer’s problems and focus on solving them. They don’t force fit a solution or sell something they know won’t work for short term gain, because they know trust, referrals and customer expansion are the lifeblood of a services firm. 

#2 – Deep Expertise

At the end of the day, customers hire a service partner to solve a problem they can’t solve on their own. They look for proof a firm has solved their problem before, and can confidently do it again. Without real depth, whether it’s in a technology, an industry or a functional area, you’ll never build the credibility needed to become indispensable. It’s why we push the importance of focus so hard in our conversations. The best firms know they can’t be all things to all people, which is why they double down on a specific domain and specific types of customers. It’s why they build unique IP and strong thought leadership programs – to make their expertise visible and establish trust.

#3 – Personalized Service at Scale

Deep expertise is only part of the equation. To be a trusted partner, services firms must apply that expertise to the specific needs of a customer (their industry, size, culture, tech stack, current processes and desired future state). However, firms that sell everything bespoke will struggle in sales and marketing. Margins will suffer because the work isn’t predictable or efficient. The best firms figure out how to package and deliver personalized services at scale. They institutionalize what consultants learn across customer engagements, codify offerings, and productize portions of their work so it can be delivered repeatedly without losing the personal touch. Increasingly, AI is making this far easier:  summarizing interactions, surfacing insights in the right context, and freeing humans to focus on relationships and go deeper.

#4 – A Talent Engine

In software companies, the asset is the code. In services firms, it’s the people. In the AI era, those people will be more and more supported by code, however, people want to work with people. They want partners who know their business, who can work through tough issues, and shoulder the risk alongside them. The best firms understand this and don’t treat talent like a commodity. They also don’t just hire for what a person can do today, they invest in what they could become tomorrow, creating systems for learning, mentorship and mobility. As automation takes over repeatable tasks and customers expect more strategic thinking from their partners, the bar for talent will only rise, and the half-life of skills will shrink. Firms that rely solely on outside hiring to keep up will always be playing catch-up and paying a premium. The ones who grow their own talent? They’ll build a bench the market can’t easily replicate.

#5 – Outcome-Aligned Incentives

In the best-run services firms, goals aren’t vague or siloed. They’re clearly defined, communicated, and grounded in measurable outcomes that matter to business and customer success. A delivery lead should know that success isn’t just hitting a utilization target, it’s about delivering results with high customer satisfaction. A seller should know that it’s not about closing the biggest deal, it’s about closing the right deal that the firm can deliver well and grow over time. Finance should know that success isn’t just about tracking and containing costs, it’s about partnering with the business to make smart decisions that will lead to growth. Build compensation plans and total rewards plans based on the outcomes and behaviors you want to see across the business. Build systems of accountability and recognition programs that reinforce those behaviors, and as a leader, model what you want to see.

#6 – Continuous Improvement

Most services firms are born to solve specific customer problems, but the best firms don’t stop there. They make it their mission to find smarter, faster, cheaper ways to solve those problems, or to continuously move up the value chain to solve bigger and more strategic problems. They’re constantly thinking about how to automate steps, templatize repeatable pieces, or use AI to reduce friction. High-performing firms constantly ask: Are our teams operating at their best? Are we investing in the right places? Are we improving margins without burning out people? Continuous improvement isn’t about chasing perfection, it’s about developing the muscle to learn, adapt, and get better every quarter. 

Of these first principles, it’s hard to say what the most important ones are as they’re all pretty important. However, I can tell you what the two hardest have been for me – personalizing services at scale and aligning outcomes with incentives. But get these right, and hold on tight!

Using AI to Support First Principles

Everyone today is talking about how AI is disrupting professional services, and that is absolutely the case. It’s rewriting the way firms market, sell, deliver and compete. However, the firms that will win the next decade won’t be the ones shouting about AI, they’ll be the ones quietly aiming AI at supporting these first principles. 

They’ll use AI to understand customers better, predict their needs and respond faster. They’ll use it to capture, elevate and embed institutional knowledge across teams so they can bring deeper expertise to customers and deliver more personalized services at scale. They’ll use AI to hire better, tailor learning paths and create talent moats. They’ll use AI to align and measure outcomes, and most importantly, to continuously improve every facet of their business.

Every industry goes through eras of disruption. Navigating this one will require leaders to think hard about their first principles and then tune the business for the situation. Don’t expect to get it right 100% of the time, but if you stay true to what makes a great services firm – and what makes your firm great – you’ll be in the right vicinity.

If this resonates, you’ll like our upcoming eBook on the rise of the AI-native services firm. It goes deep into how to operationalize these principles with today’s tech and tomorrow’s teams.

When everyone has AI, learning is your moat

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Everyone is talking about how AI will change the way we work, how businesses operate and which jobs will survive and thrive in an AI Era. Nowhere is that more apparent than in IT services. 

Today, professional services firms seem to be focusing in three areas: What services can we provide around AI that our customers will value? How are we meeting our partners’ needs and expectations with their AI solutions? How can we use AI to do what we do better, faster, cheaper?

I’d like to offer up a fourth focus area that needs to be addressed by any professional services company that wants to stay ahead of the AI curve: how will you differentiate based on how your teams learn? 

Technology certifications used to be differentiators, now they are table stakes. The basic functional and industry knowledge required to do certain jobs is now available at scale through GenAI. AI isn’t just changing what your employees need to know or what tools they use; it is transforming the fundamental nature of how your teams will learn, develop, and demonstrate expertise in a way that is unique to your company. 

The Problem: Everyone’s Busy Training on AI, But Not Strategic About It

Your people need to know how to use AI, in all its various forms. If you’re slightly ahead of the curve, you’ve done things like introduced training classes and held “AI Innovation Days” and AI hackathons.  A recent Tercera survey of IT services leaders indicates that almost every single firm is making some internal investment in AI capabilities – from creating formal adoption programs to buying new AI tools (see below).

Everyone’s busy with AI. But busy isn’t the same as effective. The question isn’t whether your people can use AI tools; the challenge will be teaching your people to learn differently when AI handles the knowledge work. The real questions are: “Are any of these things helping me win because my people can do things that others can’t? Are they helping my teams think and learn better (not just faster)?”

Why Learning Differently Matters

To understand why this matters, I need to show you how learning actually works – and bear with me while I show you how big a nerd I am.

I am a HUGE Star Trek fan. I’ve always been fascinated by how the crew uses the computer (AI at its finest) for information, analysis and to perform mundane-to-battle-ready tasks. At the same time, the crew are incredibly educated and constantly rely on their intuition and ability to connect unrelated dots to form hypotheses and solve that week’s dilemma.

This works because – even in space – this is how people actually learn:

  • Factual and procedural knowledge – the mental shortcuts you can draw on without having to look things up
  • Conceptual knowledge – what enables your teams to understand business use cases and shape solutions through various mental models
  • Metacognition – understanding your own thought process and using that understanding to deepen learning

Your approach to AI right now is probably dealing with facts and procedures. There are incredible benefits to presenting information in the right context, enhancing workflows, and removing mundane work. But as valuable as that is, it is ultimately about productivity and will become ubiquitous.

Focus on teaching your employees HOW to learn with AI to make your people your competitive edge.

Your task is to realize the metacognitive shift required when knowledge acquisition is transformed and develop learning strategies that leverage AI to build unreplicable expertise. In non-learning-geek language? Focus on teaching your employees HOW to learn with AI to make your people your competitive edge.

Three Ways to Make Learning Your Differentiator

  1. Develop proprietary perspectives, not just AI skills. What are the viewpoints, frameworks, or success stories that define what is unique about your organization? This isn’t about teaching AI as a tool—it’s about teaching AI as a thinking partner. Use it to pressure-test your perspectives, to explore scenarios your competitors haven’t considered, to uncover blind spots in your approach.
  2. Get the productivity gains, then get serious about the learning gains. Push hard to get to AI as coach, tutor, mentor, and devil’s advocate. This shift from AI-as-efficiency-tool to AI-as-learning-amplifier is where the real competitive advantage lies. Stop talking about “growth mindset” and start expecting your people to use AI to become better thinkers, not just faster workers.
  3. Flip the hiring script—creativity and relationships are your new technical skills. Talent retention in professional services just got harder because when AI is doing the repetitive work, it will shine a spotlight on those that truly add value. Those are the employees your competitors will want the most and the ones you need to keep. One of your most valuable retention tools  is a new learning strategy. Step away from competency models that prioritize technical depth and start building teams around the ability to build sticky relationships, communicate complex ideas, and innovate with AI as a collaborator.

The Collective Intelligence Advantage

Everything I’ve said up to this point has been about the individual. But here’s the counterintuitive reality: As AI makes individual expertise more accessible, collective intelligence becomes more valuable. The firms winning tomorrow won’t just have smart people, they’ll have people who make each other smarter.

When everyone has access to the same foundational knowledge through AI, the competitive advantage shifts to how well teams can combine their unique perspectives, challenge each other’s assumptions, and co-create solutions that no individual (or AI) could produce alone.

When everyone has access to the same foundational knowledge through AI, the competitive advantage shifts to how well teams can combine their unique perspectives, challenge each other’s assumptions, and co-create solutions that no individual (or AI) could produce alone.

Think about it: your clients aren’t just buying your technical capability anymore. They’re buying your ability to see connections across disciplines, to bring together diverse viewpoints, and to synthesize complex challenges into actionable strategies. That synthesis happens in the spaces between people, not within individual minds.

While your competitors are training individuals to use AI better, you’re building networks that think together. That’s not just a capability. It’s a moat.

 The Choice Ahead

We’re witnessing an organizational identity crisis. Companies are frantically reorganizing around AI. We’re creating Chief Work Officers, resurrecting Chief Learning Officer roles, launching endless training programs. But in all this scrambling to teach people how to use AI, we’re missing the deeper question: What happens when everyone has the same AI capabilities?

The firms that survive won’t be the ones with the best AI training programs. They’ll be the ones who figured out how to make their people’s thinking irreplaceable.

If AI democratizes knowledge, what becomes the new scarcity? The answer is wisdom work—the kind of thinking that emerges when humans and AI collaborate not just to process information, but to create genuinely new insights.

Knowledge work is becoming wisdom work. It’s kind of exciting to see people come front and center in all the conversation about how AI is transforming work. I’d love to hear from you about what you are doing to differentiate your professional services firm and what your teams are learning.

Cat Lang is a Tercera Advisor and has helped build skilled customer and partner workforces in the Cloud world before it was called Cloud. She has led groundbreaking training, certification, and customer success initiatives at global Cloud leaders including Salesforce, Google, and ServiceNow.

Is shifting to enterprise sales the right move? The answer might surprise you

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Boutique solutions providers often ask me whether transitioning from small or mid-size clients to large enterprise accounts is the key to unlocking step-change revenue growth. The short answer? It depends.

On one hand, enterprise clients come with deeper pockets, greater discretionary spending, and theoretically less risk when it comes to cash collections. It’s also far easier to scale a services organization on the back of large accounts and bigger deals. The rewards can be substantial, especially if the cost of sales remains relatively stable while deal sizes increase.

On the other hand, the road to enterprise sales isn’t always paved with gold. Higher barriers to entry, complex decision-making hierarchies, longer sales cycles, stiffer competition, and stringent certification requirements can make the transition far more demanding than it first appears.

So, is the shift worth it? Let’s break it down.

The Allure of Enterprise Clients

During my early days consulting in Chicago, I was tasked with leading and scaling a small engagement at a Tier-1 telecom provider. At the same time, my close colleague was assigned to a similarly scoped project at a boutique bank. Our Friday night “war room” happy hours quickly became a study in contrasts. She’d regale me with stories of sleek offices, vibrant teams, afternoon wine carts, and a decision-making process refreshingly free of red tape. Meanwhile, I was surrounded by high, beige cubicle walls that smelled faintly of decades old carpet, serenaded daily by the gentle snoring of my cube neighbor, a year shy of retirement. Bureaucratic bottlenecks were more common than afternoon wine breaks.

Fast forward a few years, the irony wasn’t lost on us. That less-than-sexy telecom turned out to be a powerhouse of sustainable growth. This growth fueled our consulting firm’s momentum, generated rich career opportunities, and ultimately helped boost our valuation when the time came to exit. And yes, my friend eventually joined one of my projects at that very same telecom… proving yet again that substance often outlasts sparkle.

For boutique solutions providers, the prospect of landing enterprise clients can feel like a game-changing move. The potential benefits are hard to ignore. These include larger budgets, multi-year contracts, and a steady stream of revenue that provides stability and scalability. Unlike mid-market clients, enterprises often have discretionary funds that enable investment in innovation and strategic initiatives rather than just cost-driven decisions.

Beyond financial advantages, enterprise deals also offer a chance to establish credibility, positioning your firm as an industry leader in the market and with strategic partners. Breaking into this space can unlock repeat business, referrals, and broader visibility in your sector, setting the foundation for sustainable long-term growth.

However, for all its promise, this shift isn’t always a guaranteed golden ticket.

The Hidden Costs and Challenges

What many providers underestimate is the cost and complexity of penetrating the enterprise market. The road isn’t just about selling bigger, it’s about selling differently. Enterprise buyers operate within rigid procurement processes, often requiring multiple layers of approval, risk assessments, and compliance verifications before a deal can move forward.

What many providers underestimate is the cost and complexity of penetrating the enterprise market. The road isn’t just about selling bigger, it’s about selling differently.

Sales cycles stretch longer, sometimes taking months or even years before contracts are signed. Decision-making within large organizations can be bureaucratic, requiring alignment across multiple stakeholders, each with competing priorities and perspectives.

I once led a fast-growing systems integration business with eyes firmly set on a marquee client. We followed the playbook to the letter and hired a well-connected “insider” consultant on retainer, only to watch months roll by with little to show beyond a hefty invoice padded with lavish travel and expense reports.

After a year of patient pursuit, anchored by Account Based Marketing and countless industry expert roundtables, we finally earned our big break: an invitation to join their preferred supplier program. But even this hard-won milestone came with its own maze of challenges. The onboarding process dragged through six more months of dense legal paperwork, spawning a fresh set of workstreams: expanded insurance coverage, newly required certifications such as PMP, ITIL, ISO27001, and more boxes to check than we ever anticipated.

It wasn’t until nearly two years after and a revolving door of sales executives that we started seeing actual proposals. And even then, every opportunity was a battlefield, competing against deeply entrenched incumbents with home-court advantage. We eventually made it to the promised land of active sales and successful delivery. But the victory came at a cost: tight margins at every turn, squeezed by compromises made to gain entry.

Beyond navigating complex decision-making hierarchies, solution providers may need to address significant compliance requirements to take down an enterprise deal, especially deals above $5 million or in highly regulated industries.

Beyond navigating complex decision-making hierarchies, solution providers may need to address significant compliance requirements to take down an enterprise deal, especially deals above $5 million or in highly regulated industries.

Many enterprises won’t even consider vendors unless they meet industry-standard certifications, such as ISO 27001 (for information security), SOC 2 (for SaaS providers), or GDPR/HIPAA compliance (for regulated industries). These certifications require time, financial investment and operational readiness, making them a heavy lift for smaller firms.

Security protocols are another major hurdle. Enterprises demand robust data encryption, controlled access policies, incident response plans, and proactive monitoring to mitigate risks. Without these safeguards in place, boutique providers may struggle to gain entry into enterprise ecosystems, even if they have an exceptional product.

Firms can spend months or even years preparing their infrastructure, security frameworks, and compliance documentation before they can effectively compete in enterprise sales. The investment in the end will likely be worth it — opening up multi-million dollar opportunities and reducing risk for the firm in a time when security threats are only increasing. However, it’s not a small undertaking.

One of the toughest moments in my early career was losing a Fortune 500 client, a blow that still stings. The loss stemmed from employee fraud, a breach that could have been avoided with basic security measures like multi-factor authentication. Ironically, when the client began strengthening their protocols, our direct stakeholder argued for us to be grandfathered in, hoping to sidestep increased costs tied to new vendor requirements. That decision proved costly.

The ex-employee, turned fugitive, had executed a sophisticated scheme, siphoning exactly $1,792 through a cash app. Small theft, big consequences. We didn’t just lose a $10 million annual account. We lost trust. And no recovery effort could fully restore what that breach shattered.

How to Evaluate If the Shift Is Right for You

Before diving into the enterprise space, business leaders need to ask themselves critical questions:

  • Is our offering truly built for enterprise scale? Does your product or service align with the operational complexity and expectations of enterprise clients?
  • Do we have the patience for longer sales cycles? Can your business sustain itself while waiting months for deals to close?
  • Can we meet enterprise requirements? Are your sales, delivery and back-office teams prepared to navigate procurement hurdles, compliance mandates, certification processes, and keep up with best-in-class delivery?
  • Are we willing to invest in enterprise-level relationships? Winning enterprise accounts often means higher-touch engagement, strategic consulting, and ongoing support. Do you have the capacity and capabilities to capture and maintain these relationships?

Enterprise sales can be incredibly rewarding and is increasingly required when you hit a certain plateaus of growth, but only if your business is structured to capitalize on the opportunity effectively. Otherwise, chasing large clients without the right foundation can lead to wasted resources and stagnation instead of growth.

Enterprise sales can be incredibly rewarding and is increasingly required when you hit a certain plateaus of growth, but only if your business is structured to capitalize on the opportunity effectively.

Winning Sales Strategies to Break Into The Enterprise

For those ready to take the leap, navigating this shift requires a strategic approach. Some key tactics include:

  • Build enterprise credibility: Invest in certifications, case studies, and industry partnerships that signal legitimacy.
  • Understand the buying process: Map out procurement steps, approval chains, and key decision-makers to avoid unnecessary bottlenecks.
  • Shorten sales cycles: Develop pilot programs, smaller initial engagements, or bundled offerings to gain traction faster and stick around longer.
  • Strengthen relationship-based selling: Enterprises buy from trusted partners, not vendors. Focus on consultative selling rather than transactional approaches. Make sure you have the right team in sales and service delivery to be that trusted partner.
  • Prepare for post-sale success: Ensure you have the resources to support enterprise customers’ long-term: scalability, customer success teams, and ongoing engagement.

At the heart of the decision lies a single truth: breaking into enterprise sales isn’t just a shift in client size, it’s a transformation in strategy, process, and execution. Done right, it can be the steppingstone to sustainable growth. Done wrong, it can drain time and resources with little to show for it.

So the real question isn’t just “Is enterprise sales the right move?” it’s “Are you prepared to evolve your business to make it work?”

Cathy Jooste is a Tercera Advisor and business leader with nearly 30 years of experience in consultative and technology-enabled services. She excels at scaling companies, optimizing operations, and driving digital transformation. She is the founder of Nuvidea Solutions, and has held executive roles at CGS, Atento, DXC Technologies, Cognizant, Avanade and Accenture.

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