Partnerships, Not Products, Will Define How Consultancies and Native AI Companies Share Value in Agentic AI Era

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Services regain strategic importance

Services are cool again. Native AI companies are embedding services offerings around their products and thinking about services as part of their long-term strategy — well, not too long-term, as how long can a strategy remain the same?


Suppose native AI companies want to deliver services. How will they compete, or perhaps even partner, with traditional IT services companies and management consultancies, which are pursuing their own AI opportunities? These assertions and questions came up repeatedly this summer, and as 2025 winds down, we are starting to see some outcomes and answers.


TBR attended several tech conferences and analyst events in recent months, and AI was the inescapable topic at each one. In particular, KPMG’s Technology and Innovation Symposium in Deer Valley, Utah, stands out, in part because of the sheer breadth of opportunities discussed, use cases highlighted, and future hopes and fears laid out in stark detail.


In our latest blog series, TBR on AI in 2025, we intend to connect those ideas with research and analysis conducted by TBR over the last few years to highlight implications for the companies we cover across the technology ecosystem. Previous topics about the agentic AI age have included human resources management and expectations for enterprise IT architecture. Future posts will discuss who gets first and consistent access to limited resources like energy.

Watch now: The Good, the Bad and the GenAI Opportunity in Cloud Ecosystems

AI-native platforms disrupt the people-dependent services model, forcing incumbents to rethink partnerships

Native AI companies folding services offerings into their products and platforms follow in the large footsteps of the cloud and ERP vendors that persistently maintain professional services offerings to complement their cloud infrastructure and software money-making machines. As these giants have learned, success in services is harder than it looks and requires, at minimum, people and permission. Services are inherently a people business, and delivering consistently, with quality and at scale demands teams of people and staff to support them. No clients buys services without first being certain the provider can deliver (i.e., permission). Cloud and software companies have always had the latter locked up. Who knows SAP better than SAP professionals, and who can deliver Azure better than Microsoft? But the people part continually presents a stumbling block, or at least a check on scale and growth.


Why does all this framing around the cloud and software giants matter to native AI companies with their products and platforms? It doesn’t. However, it’s the world in which IT services companies and consultancies have been operating. Partnering with native AI companies looking to expand their services offerings will not be the same as fending off Microsoft Professional Services or SAP Services. At TBR, we constantly examine the largest IT services companies and consultancies, studying how they operate, partner, go to market and generate revenue. All of these aspects are changing rapidly in the agentic AI age, allowing us to bring that research to this developing space.

What roles will traditional IT services companies and management consultancies play, particularly as AI companies’ products permeate enterprise clients?

TBR sees three, not mutually exclusive, roles for traditional IT services companies and management consultancies: venturer, concierge and rival.


Most IT services companies and consultancies lack an impressive track record of investing early in startups, often acting more like traditional service providers than venture capital firms. At traditional IT services companies, the quarterly earnings clock, management and oversight layers, and competing offerings typically keep startup incubator programs relatively small (and too frequently underfunded).

Why does the people challenge not apply to native AI companies in the ways we have seen with traditional cloud and ERP players?

Solving the people problem is, inherently, part of AI, no matter how often one hears the “human in the loop” chorus. Services offerings folded into native AI companies’ products start optimized for minimal human touch. IT support through a chatbot? Built-in. FinOps solutions? Native.

Yes, services at scale has always demanded people and support staff, but that imperative is fading fast and will not apply to native AI companies.


Consulting firms have not fared much better, given the need for consensus around strategy and investment. Notably, some of the Big Four firms have become more adept at making small investments, capturing enough of a stake to influence without owning. KPMG, in particular, has developed a consistently funded, well-managed startup support program that has seen success in the last couple of years.

Consultancies’ change management expertise positions them as vital partners to emerging AI vendors

Investing can be the gateway into a concierge-like relationship between these giant companies and firms and the relatively small native AI companies. Most commonly, these relationships focus on making introductions to enterprise clients, providing strategic counsel, offering financial and tax advice, and mentoring leaders. TBR sees a critical new opportunity, expressed clearly at the KPMG Technology and Innovation Symposium: change management.


Speakers at that event and professionals across the AI and consulting space in subsequent discussions noted that most native AI companies have not worked out the potentially massive change management requirements and implications of adopting their products. Many IT services companies and all management consultancies excel at change management and could be well positioned to provide clients consulting services entwined with a native AI companies’ product, provided all parties understand both the complementary offerings and the commercial models. And these elements echo TBR’s ecosystem research, which has repeatedly shown that leading companies invest in understanding their alliance partners’ offerings and sales structures.


Just like supporting startup programs, many traditional IT services companies and consultancies have struggled to adequately put themselves in their alliance partners’ shoes. And when those partners are startups or immature native AI companies, that struggle will be harder in the absence of leadership, strategic direction and sustained investment. But that’s the potential downside. The upside is that consultancies are perfectly positioned to be change management specialists, helping their largest clients adopt the best new AI.

“When I think of knowledge, there are two pieces. One is, what are the insights to build the product? That’s where our people come in, because we have practitioners who are working with clients on this product, using the right insights to build the right product. That’s Part 1. And second is, do your salespeople know those product features to help go sell? And I think the second part, I definitely see opportunity. There’s a little bit of upscaling and change management required for a lot of these sales folks across the board on how to sell these modern version of their software in the agentic world.”
– AI professional speaking to TBR about knowledge management, sales and AI

 

And then, there is always the possibility of rivalry

Every traditional IT services company and management consultancy that TBR covers, including Tier 2 companies (enterprise systems integrators, in TBR’s terms), has its own products, and most have platforms. Although not all vendors sell products as stand-alone offerings, they all have accelerators and AI-enabled solutions — quite a few acquired over the last couple of years.


At a technology level, these solutions and AI native companies’ products may compete or complement, but at a business-model level, what matters are the attached services. The traditional IT services companies and consultancies have relied on client intimacy, scale and industry knowledge to stay sticky with their clients, holding off challengers. AI becomes intimate more quickly than traditional ERP. Industry-specific AI is coming fast, so traditional companies and firms will need to rely on scale, for now, to keep native AI companies’ services offerings at bay, at least with enterprise clients. As many traditional companies and firms seek new markets among smaller clients, focusing on investing and partnering will become the only path to sustained success. Or simply acquire, of course.

What comes next for traditional IT services companies and consultancies?

History keeps rhyming. Native AI companies understand that the services business model often clashes with product and platform strategies. As a result, their investment in services will rise and fall with client demands, leadership changes, market opportunities, and the successful moves of the smartest traditional IT services companies and consultancies.

TBR Launches Cloud Voice of the Partner

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Technology Business Research, Inc., is pleased to announce the launch of our Cloud Voice of the Partner report.
Major cloud platforms Amazon Web Services, Microsoft Azure and Google Cloud are positioning themselves at the center of the new cloud-centric ecosystem. With billions of dollars and significant growth at stake, it is critical for hyperscalers, ISVs and systems integrators/managed service providers to improve the effectiveness of their alliance strategies.

The Cloud Voice of the Partner report deep dives into the current state of the ecosystem from each participant’s perspective and highlights winning strategies for capitalizing on the growing opportunity. Analysis is segmented by cloud vendors, ISVs and SIs/MSPs. Covered vendors include Accenture, Alibaba, Amazon Web Services, Boston Consulting Group, Capgemini, Cognizant, Databricks, Deloitte, DXC Technology, EY, Google, HCLTech, IBM, Infosys, KPMG, McKinsey & Co., Microsoft, Oracle, PwC, Red Hat, Salesforce, SAP, ServiceNow, Slalom, Snowflake, Tata Consultancy Services and Wipro.

The first publication of Cloud Voice of the Partner is now available on TBR Insight Center™. If you believe you have access to the full research via your employer’s enterprise license or would like to learn how to access the full research, click here.

Cloud Voice of the Partner Excerpt

The emerging GenAI opportunity is exacerbating the challenges of partnering within the hyperscaler ecosystem

There is still company in alliances

  • The value of multiparty alliances is clearly resonating within the ecosystem and with end customers. The prospect of more deals, higher close rates and greater go-to-market efficiency is compelling for all categories of IT providers. And for customers with services, hyperscaler and ISV solutions, the track record of successful implementations is of tremendous value.
  • Despite that clear value for all stakeholders, managing multiparty alliances remains challenging, and most vendors struggle to consistently take this type of joint go-to-market approach. This episodic approach to multiparty alliances will likely persist.

The ecosystem is busily preparing for the GenAI opportunity

  • For the majority of IT providers, the generative AI (GenAI) opportunity has yet to be realized. Hyperscalers, through alliances with model leaders like OpenAI, have already seen some tangible growth benefit of GenAI, though the bulk of the opportunity still lies ahead following large investments.
  • Professional services providers and ISVs are busily adding GenAI capabilities and widely marketing skills in the area but are still struggling to fully capitalize on the opportunity in a meaningful way.
  • The stifling hype around and slow monetization of GenAI are not stifling investment, however, and 57% of respondents believe AI and GenAI will be the leading area of growth over the next three years. Hyperscalers view partnering with vendors that offer service capabilities and GenAI solutions as the best way to capture that opportunity.

Commercial models are in flux, but monetization remains the most important ecosystem metric

  • Across all partner types in the ecosystem, measurements of opportunity, deals and revenue are the most important partnerships metrics. The current AI-related state of flux as well as shifts in customer investment patterns are stressing the traditional commercial models.
  • With a limited number of GenAI best practices, end customers are leery of engagements with uncertain ROI, leading to a desire for outcomes-based pricing models or hyperscaler funding of services delivered by systems integrators (SIs).
  • Even hyperscalers and ISVs are struggling with commercial models that properly align the monetization of core underlying offerings with new AI and GenAI technologies. In this environment, flexibility is the most critical aspect of pricing strategies as best practices emerge.

Partners remain hopeful about GenAI opportunity but still need to build trust through monetization

Key takeaways

1) Although GenAI is dominating partnership activities, the technology’s actual revenue contribution remains insignificant.

“Executives are obsessed with GenAI. If you are not articulating how GenAI will be used in a project, you’re not even in the conversation. It has become the elephant in the room with customers, partners and hyperscalers alike.” — Senior Partner Sales Leader, Hyperscaler

“I haven’t seen it [GenAI] significantly shift anything. I think we’ve probably added more partners just because the OpenAI and Perplexities of the world are becoming more relevant, but none of the old legacy systems are going away, because there’s still you know, 99.9% of companies have a lot of legacy systems in place that are going to continue to need support and maintenance and migration in the future.” — Director, Client Partner, SI

2) Cloud providers have done a better job of reducing competition with partners, but it still happens, particularly at the field level.

“Hyperscalers officially say they don’t compete with partners, but at the field level you still see bad behavior. A [AWS] ProServe team or a sales rep can undercut a partner, and when it happens it creates a lot of noise and frustration.” — [Hyperscaler] GTM Lead, GSI

3) Everyone believes trust and transparency are key to a successful alliance, but cloud vendors place a lot of weight on more tangible factors, like pricing and coinvestment.

“Gone are the days where partners were able to just get by on a handshake relationship. These companies are really investing in resources and teams to ensure that their sellers are being the most effective and working with the right partners that are making an impact on the business.” — SI Partner Program Lead, ISV


4) When three parties are involved, win rate and deal size potential increase, but orchestrating these relationships remains a challenge, largely from a sales perspective.

“A triparty-type opportunity pulled off successfully, it’s typically a much larger deal size, but because of the complexity of operating between those several entities, you don’t see as many of them happening unless it’s something that’s been, you know, very plug-and-play, you know, easy licensing.” — Partner Development Manager, ISV

Trust built on delivery, alignment and collaboration enables vendors and partners to present unified strategies and win complex enterprise deals

Drivers of a successful alliance

  • Across all vendors types, trust was deemed the most important attribute of a successful alliance. That said, compared to ISVs and SIs, cloud vendors value measurable drivers such as willingness to coinvest, alignment of sales incentives, and pricing model flexibility.
  • Services vendors are leading the charge with outcome-based pricing, though these vendors are far from delivering this model at scale. Although we expect some SaaS vendors will continue exploring outcome-based pricing, at least as part of a hybrid model, the infrastructure vendors are not equipped to implement a similar strategy, suggesting a potential challenge ecosystem participants will need to navigate.

“They [SI partners] have experience doing successful implementations. They’ve got a bench of certified individuals who are ready to go and have the most up-to-date enablement on the product itself to ensure successful delivery. They’re actively looking at where has joint work happened, who are some of those joint customers? What does their regular consumption as well as contract value actually look like? And so those are some of a few data points that help round out that story to go and develop trust.” — SI Partner Program Lead, ISV

Graph: Drivers of a Successful Alliance (Source: TBR 1H25)

Graph: Drivers of a Successful Alliance (Source: TBR 1H25)

In Fast-evolving AI Markets, Platform Alignment Determines Who Keeps the Customer

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Platform strategy emerges as critical differentiator in agentic AI era

Every company claims to be a data company, every tech company wants to be an AI company, and every smart company has become a platform company. For companies in the technology ecosystem, including consultancies like McKinsey & Co. and EY, OEMs like Dell Technologies (Dell) and Hitachi, connectivity specialists like Nokia and Ericsson, and IT services generalists like Accenture and IBM, finding the right platform partner might be the most strategic decision they need to make before 2026.

The speed of technology change and challenges adapting to business model changes drive the importance of platforms. The rapid developments in AI, most recently with agentic solutions, illustrate how being a platform company enables vendors to maintain a solutioning role even as technology evolves, and new participants become critical in the eyes of customers. Agentic solutions have also created myriad ways for companies to sustain their business models, acting as a conduit between end customers and the changing vendor landscape.

Ecosystem partnerships evolve as agnosticism fades, fast followers rise and multiparty alliances drive wins

Partnering across the technology ecosystem will be different going forward in three specific ways that will make early-2020s alliances seem as wildly dated as the metaverse. First, agnosticism will enter its death throes. Companies, starting with consulting firms, can no longer simply let the customer decide which components of the tech stack will come from which companies. Having no preference no longer implies flexibility to work with anyone and everyone because every company now works with anyone and everyone. Having no preference now implies having no technology innovation or, critically, no commercial relationship to bring to bear on the client’s business and technology needs. Clients have grown accustomed to tech agnosticism and moved beyond it. They want to know which companies have special relationships that bring innovative ideas, the latest solutions and the best commercial terms. Agnosticism is not special at all.


Second, 2026 will be the year when fast followers start following in earnest. TBR has seen industry-leading consultancies, IT services companies, hyperscalers and software vendors align their go-to-market motions, sales and leadership teams, and even cross-training and knowledge management initiatives to build a pipeline and accelerate revenue for all parties. According to data and insights from TBR’s Ecosystem Research, the most profitable and fastest-growing companies have been leveraging their alliances in new and profoundly meaningful ways.

For example, in TBR’s September 2024 Adobe and Salesforce Ecosystem Report, we estimate that IBM, currently among the top four largest by revenue, will have the third-fastest Salesforce-related revenue growth from 2023 to 2028 as “the two will integrate IBM watsonx platform and IBM Granite series models with the Salesforce Einstein 1 Platform. IBM Consulting is creating industry-specific prompt templates and copilot actions for Salesforce applications.” TBR anticipates more companies across the technology ecosystem will emulate the leading companies’ alliance strategies in 2026. Success begets imitation.


Third, transparency wrought by AI adoption will compel companies across the technology ecosystem to partner differently. As agnosticism fades and strategies converged around the best practices, differentiation will come from companies’ abilities to engage in multiparty alliances. In March, we highlighted Informatica’s realization that although going to market with one partner led to a win rate of 47%, compared to only 17% when going alone, going to market with more than one partner led to an 83% win rate. A couple of months later, Salesforce acquired Informatica. Without question, Salesforce understands the lessons Informatica learned about the power of multiparty alliances.

Reflecting this understanding, Salesforce has quickly embraced a multiparty ethos, prioritizing third-party integrations within its broader AI platform strategy. Its Agentforce Partner Network already includes Amazon Web Services (AWS), IBM, Workday, Google Cloud, and dozens of SI and ISV partners building reusable agent actions and templates into a shared agent ecosystem rather than separate point products. At the same time, Salesforce has expanded its alliance with Google so that customers can build agents using Gemini models while running core Salesforce workloads on Google Cloud infrastructure, although that capability is not yet generally available. On the services side, partners will lead deployment, governance, and change management. For instance, Cognizant recently announced an expanded partnership to help clients deploy, scale, and govern enterprise agents within the Salesforce ecosystem.

Like services vendors, Salesforce is moving away from an agnostic approach in favor of prioritization. Partner performance KPIs are closely scrutinized, and ecosystem participants showing the most momentum in scaling Agentforce adoption are receiving the bulk of co-marketing and co-innovation resources. From Salesforce’s perspective, it matters little whether a service vendor is an upstart; in fact, the company has seen several emerging partners rise quickly through the ecosystem ranks due to their adaptability and speed amid Salesforce’s growing focus on data and AI. Regardless of which vendors carry the Agentforce torch, Salesforce is making clear distinctions and taking note. By becoming more targeted, the company is better positioned to build structured multiparty alliance frameworks with selected vendors—an approach that will help Salesforce achieve the win rates cited by Informatica.

Next year, companies across the technology ecosystem will begin doing the same, slowly. We are under no illusions that multiparty alliances will become easier beginning Jan. 1, 2026. Commercial arrangements, sales incentives, and selecting the right mix in a multiparty alliance will still require investment, leadership and time. The fastest-growing companies in the technology ecosystem will commit to making their multiparty alliance strategy work, and we expect they will start to see those win rates above 80%.


In this changed environment, TBR will be more closely tracking the companies and firms most likely to be disruptive in the near term. Our current Ecosystem Research stream covers 15-plus global systems integrators’ and consultancies’ relationships with nine cloud companies and software vendors. Of those companies, we expect 2026 to be particularly exciting, perhaps even momentous or strategic, for Deloitte, Salesforce, Dell and IBM. In TBR’s quarterly coverage of those four companies, subscribers will learn about strategies, trends and performances that reflect how ecosystem participants expect the agentic AI age to shape the market.

The Federal Government Shutdown: What It Means for Leading Federal System Integrators

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How are federal systems integrators interpreting the latest federal shutdown?

Federal fiscal year 2026 (FFY26) began on Oct. 1, 2025, with the federal government shuttering most operations after lawmakers failed to reach a budget agreement. The current stoppage is the fourth full shutdown since 1995. The last federal closure was the 35-day partial shutdown that occurred from December 2018 to January 2019, during President Trump’s first term. TBR believes federal systems integrators (FSIs) are intensely concerned that the current shutdown will be as long and disruptive as the 2018-2019 shutdown.

Some integrators have noted that while the short-term impact of the shutdown will create a significant growth headwind in the current fiscal year, they expect to see opportunities to backfill shutdown-related sales gaps by mid-FFY26. Again, the duration of the shutdown will be critical, but federal IT vendors are hoping that Congress passes a continuing resolution to end the current shutdown.

Contractors are preparing for the worst-case scenario: Program funding is interrupted altogether for an unknown duration, which seems more likely this time around. The federal IT community is looking to prior shutdowns for strategies to navigate the latest shutdown, such as heavily scrutinizing their current order book for the most vulnerable engagements while prioritizing preservation efforts on strategic, mission-critical work. From what TBR is hearing, the final quarter of FFY25 was more irregular than usual in sweeps and budget flush, which does not bode well for the federal IT community.

Fortunately for contractors, the shutdown comes at the beginning of the federal fiscal year, when prior-year funding remains available, enabling them to continue working on some programs, at least until the funding runs dry. In contrast, the 2018-2019 shutdown began with fewer budget dollars available, exacerbating the fiscal disruption on contractors’ P&Ls and order books.

How are FSIs navigating the shutdown?

FSIs got a head start of sorts earlier in the year with their responses to the disruption caused by the Trump administration’s Department of Government Efficiency (DOGE), and we are seeing contractors take many of the same actions.

Vendors are again drawing closer to procurement staff in the agencies, and contractors’ development teams are pushing to launch recent contract wins, renewals and expansions as soon as possible, while accelerating the adjudication of programs in the business development pipeline. However, the furlough of so many contracting professionals across the federal government since DOGE’s cost-rationalization efforts began in January will make communications with agency acquisition counterparts more difficult than ever. TBR has heard that procurement staff in some agencies has declined by two-thirds compared to their size during the Biden administration. Many other agency contracting professionals retired early or accepted resignation offers.

TBR also expects the shutdown will impact vendor balance sheets in the third calendar quarter (FF4Q25) in terms of more aggressive collections and a sequential decline in days sales outstanding. FSIs will also have limited latitude with efforts to preserve profitability, as they had already implemented tighter expense controls in response to DOGE’s aggressive contract reviews and cost-cutting actions, leaving little room to further optimize operations and contract execution.

HR managers are struggling to manage myriad shutdown-related challenges affecting contractors’ workforces. HR teams are being forced to develop plans for employees most likely to be affected by the shutdown, in terms of temporary furloughs and reassignments, while having honest discussions about the potential for shutdown-driven furloughs becoming permanent. Vendors are reaching out to their strategic and solutions partners, which should expect leading integrators to help them navigate the shutdown.

Vendors are evaluating new cost-cutting measures to offset the inevitable erosion of margins that accompany any shutdown, which will include doubling down on efficiencies in operations and contract delivery, and, unfortunately, layoffs. Many federal IT professionals at FSIs have defected to commercial IT companies in the wake of DOGE’s impact on the federal technology sector, and attrition among IT workers could spike at FSIs, especially with a prolonged shutdown, creating additional challenges for FSI HR teams.

Project teams are preparing for the challenges of restarting programs stopped by the shutdown and expect, in some cases, it could take up to a week for paused programs to return to full operating tempo. Resuming project work will be complicated by layoffs across agencies and in the FSIs themselves.

We will hear from the federal IT community at the end of October — when the next earnings season begins — regarding how the FSIs expect the shutdown to impact current fiscal-year performance. The full impact will be visible with the 4Q25 earnings season.

What are the biggest risks facing FSIs amid the shutdown?

TBR believes federal IT vendors will suffer multiple margin headwinds, disrupted cash flows from operations, invoicing challenges, and delays in receivables collections in 4Q25 that may linger into 1Q26. We expect most FSIs will be forced to reduce growth, margin, earnings per share and cash flow guidance for FY25 or FY26.

TBR has observed IT professionals at both federal agencies and federal IT vendors departing the sector altogether since the changeover in administration, and we believe the brain drain of IT workers with significant experience and institutional knowledge of federal agency missions and IT infrastructures could continue during the shutdown. Once these people are gone, particularly professionals with advanced technological degrees and/or training in digital technologies like AI, cybersecurity and quantum computing, they will be very difficult to lure back, let alone replace.

Recent contract wins may be at risk of cancellation, and delivery timelines on programs that endure will be significantly delayed as contractors struggle to maintain communications with agency procurement staff. In some cases, program continuity will be disrupted as project teams will not have access to shuttered government facilities.

Contractors who fail to fully and proactively document every shutdown-related expense or disruption may not be able to recover those expenses when the government resumes operations. TBR has heard that once the federal government reopens, contractors may have only 30 days to submit reimbursement claims.

Which contractors are best positioned to minimize shutdown-related disruptions?

FSIs that operate as subsidiaries of larger global IT services firms or consultancies — for example, Accenture Federal Services (AFS), CGI Federal, Deloitte Federal and IBM Consulting’s federal operations — have been moving resources from federal projects to other public sector or commercial programs to retain highly experienced workers. However, transitioning them back to federal work after the shutdown may be met with consternation from workers reluctant to return to the turbulent federal market.

Leidos has more globally diverse operations than many other leading FSIs, with operations in Europe, the Middle East and Australia, as well as in commercial healthcare IT, and has the option of offering to reassign federal IT workers to projects in these markets to prevent them from leaving altogether.

The largest FSIs, particularly those with the flexibility afforded by strong profitability (e.g., Leidos, CACI and Booz Allen Hamilton [BAH]), will use the shutdown as an opportunity to cross-train large swaths of their workforce on AI, cloud, cybersecurity, data science and other emerging technologies, versus implementing layoffs, again to avoid losing skilled IT professionals. The leading FSI with strategic awards funded by prior-year appropriations (e.g., Leidos, BAH, CACI and SAIC) may also be able to avoid work stoppages or interruptions on ongoing, big-ticket engagements.

FSIs with significant volumes of work on programs considered “essential,” such as national security and national defense work, most notably CACI but also Leidos, BAH and SAIC, may avoid the same shutdown-related pitfalls as vendors without a large presence in the Department of Defense, Intelligence Community or national security agencies in the civilian sector.

Federal IT vendors with large cash reserves will be able to tap into their fiscal war chests to defray at least some of the financial impact of the shutdown on profitability, but this will mean deferring any funding for M&A, joint ventures or internal implementation of AI to streamline operations. Conversely, TBR expects small to midsize federal contractors, particularly those focused exclusively on the federal space, will suffer the most severe consequences of the shutdown.

Access TBR’s federal IT data and analysis with a free trial of TBR Insight Center.

Managing Strategic Alliances & Ecosystem Partnerships: A Case Study in Data-driven Strategy and Enablement

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Gone are the days of declared vendor agnosticism — enter the super-group go-to-market alliance

TBR market analysis shows that over 83% of enterprise technology spend is captured by multivendor partnerships and strategic alliances. Central to these super-group alliances are global systems integrators (GSIs), and key to GSIs’ execution are their practices dedicated to enabling alliance partners’ technologies.

In the below TBR Insights Live session, TBR’s Principal Analyst Patrick Heffernan and Senior Vice President of Sales & Marketing Dan Demers share how TBR’s proprietary data is supporting executives’ go-to-market alliances and ecosystem partnerships. TBR tracks the Amazon Web Services, Google Cloud Platform, Microsoft Azure, SAP, Oracle, ServiceNow, Workday, Salesforce and Adobe practices of the top 20-plus GSIs globally. Our proprietary data includes trailing 12-month revenue, headcount and credentialing details. Tier 1 companies are using this information for competitive intelligence, while savvy Tier 2 firms are using it to gain mindshare and capture revenue.

In the above session on how clients leverage TBR’s ecosystem and alliance research, you’ll learn:

  • How IT outsourcing and applications outsourcing revenue trends can guide partner selection
  • How trends in headcount and credentialing can signal hidden strengths or weaknesses in peers’ strategies
  • How objective data can guide goals in staffing, marketing budgets, engineering talent and certifications

Excerpt from “Managing Strategic Alliances & Ecosystem Partnerships: A Case Study in Data-driven Strategy and Enablement”

TBR’s Foundational Research: From Competitive Intelligence to Ecosystem Intelligence (Source: TBR)

 

Use Cases for TBR Analysis and Data in Alliances (Source: TBR)

 

This TBR Insights Live session is available on demand on our YouTube channel. Visit this link to download the presentation’s slide deck.

If you’d like to further explore the data mentioned in this TBR Insights Live session, sign up for a free trial of TBR Insight Center™ today.

TBR Insights Live sessions are held typically on Thursdays at 1 p.m. ET and include a 15-minute Q&A session following the main presentation. Previous sessions can be viewed anytime on TBR’s Webinar Portal.

 

 

Modernization First: Mongo’s Enduring Pursuit of the AI Opportunity

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Now is a good time to be in the database business

Since relational databases first entered the market in the 1970s, a lot has changed. Even as non-relational systems entered the scene to help overcome the scale constraints of their SQL-based counterparts, technological revolutions — from the internet to cloud computing — have ultimately revealed the database’s true purpose: a transactional system for storing data. But generative AI (GenAI) is changing markets in ways we never expected, and databases are gaining renewed importance and being seen as more of a strategic piece of the AI strategy. That is because large language models (LLMs), while revolutionary in their ability to generate content and provide reasoning, do not have the underlying memory and ability to execute against the private data that agents thrive on. Customers are now realizing that if they want to bring the next wave of AI applications — AI agents — into production, they need to consider the role of the database.

If it is any indication of the opportunity, almost every cloud vendor now wants to be a data company. Even established data companies are moving down to the OLTP layer, recognizing that while AI models may train well alongside analytics systems (OLAP), AI agents, which thrive on enterprise context, are best built closest to where that enterprise data resides. But even as new analytics vendors vie for a piece of the TBR-estimated $71 billion cloud operational database market, the few companies that have been in the business for more than five decades, including MongoDB, will have some clear advantages when addressing customers’ needs around AI, especially as customers are averse to using net-new databases for vector search.

In today’s market, capabilities like vector and retrieval-augmented generation (RAG) have become a prerequisite for any modern, AI-ready database. While MongoDB is an early supporter of delivering vector search natively in-database, specifically with MongoDB Atlas, it is the company’s 50 years of experience with document architecture and JSON — which powers LLMs and agentic protocols like MCP (Model Context Protocol) — that make MongoDB well suited to address the AI opportunity once customers overcome legacy application modernization hurdles.

Modernization precedes AI

Although the fact that 30% of Atlas’ annual recurring revenue (ARR) comes from customers using at least one feature, such as vector search, suggests early momentum, AI is not yet having a significant impact on MongoDB’s growth. The question then becomes, when will this opportunity manifest? While AI startups and digital natives are ready to adopt scalable databases, most enterprises with bigger budgets are not prepared to make the shift. The reality is that these enterprises, although eager to leverage GenAI and agentic AI, have accumulated decades of technical debt and are often ill-prepared to handle the AI innovations the market is introducing.

Enterprises need a way to justify not only the cost of upgrading but also re-architecting their core infrastructure software to take advantage of AI. This is where the innovation announced at the September MongoDB.local NYC event was focused. In some ways, a lot of MongoDB’s AI work has already been accomplished by default due to its JSON-native architecture and early support for vector search in the cloud. The company is now focusing on preparing customers to take advantage of AI.

“At some point Oracle lost the competitive advantage because it was everything for everybody, and as a result of that, the code base become very voluminous and bloated, and that simply wasn’t scalable and agile enough to go around and develop [on] … Oracle Database is still the best on-premises relational system. But when people talk about specifically for GenAI, most likely the GenAI decision makers are not going to be the old-world relational database experts.”

— Managing Director (Firmwide) and Chief Data Architect, Financial Services

Announcing AMP

MongoDB believes that legacy relational databases are ill-equipped to support the needs of AI. However, this is not a unique point of view, as TBR has heard the same thing during its discussions with enterprise decision makers. Of course, Oracle and other vendors will make it very attractive for customers to upgrade to their AI databases, saving them the cost and risk of migrating to a different data model such as MongoDB’s, while often offering steep discounts.

Even so, there will always be a market for customers that want to reduce their dependency on the likes of Oracle and Microsoft SQL Server, not just for scalability reasons as they consider AI but also to reduce the high degree of lock-in these vendors create. That is where Application Modernization Platform (AMP) comes into play. While MongoDB announced new feature updates you would expect at a database conference, such as support for in-query encryption with MongoDB v. 8.2, AMP was the company’s big strategic announcement at MongoDB.local NYC.

At its core, AMP brings together the various tools, processes and best practices MongoDB has used over the years to help customers transition from a relational schema to a document schema. As described to participants at the event, AMP follows “three T’s,” Tools, Techniques and Talent. To be clear, MongoDB has always believed these three attributes are key to a successful migration, and AMP provides the company with an opportunity for some fresh messaging. That said, unlike some of MongoDB’s existing capabilities, such as Modernization Factory and Relational Migrator, AMP leverages GenAI and LLMs to actually convert code, making the platform more than a rebranding or repackaging of existing capabilities, which could help MongoDB expand beyond its core developer audience and engage in more senior-level conversations.

Tools: GenAI for code conversion

Although we have not yet seen significant traction for code conversion as a GenAI use case, customers seem willing to experiment in the hope of lowering the cost and time of migration. This is particularly true as AI puts pressure on businesses to move faster, which is challenging for enterprises that are still spending a significant portion of their IT budgets on maintaining legacy systems. There is no shortage of GenAI-powered code conversion tools in the market to help solve migration challenges, from Microsoft’s GitHub Copilot to Amazon Q Developer: Transform, and AMP acts in a similar capacity, relying on LLMs to analyze the existing code base, test the code and convert it to newer frameworks.

It is too soon to tell if AMP will drive revenue growth for MongoDB, but early use cases are promising. For example, we heard from Geneva-based asset management firm Lombard Odier, which already uses MongoDB as its core database engine and is now using AMP to modernize and migrate 250 applications. We also see an opportunity for MongoDB to leverage its existing hyperscaler partners, which collectively support MongoDB Atlas across 120 cloud regions, to integrate the platform with one of these partners’ existing code conversion capabilities.

Techniques & Talent and the role of services partners

Any GenAI tool on its own is unlikely to solve the modernization problem, and the other attributes of AMP — Techniques and Talent — are where MongoDB adds value. As previously mentioned, MongoDB has been in the business of transitioning customers away from the relational model for decades, and techniques like the App Transformation Framework (ATF) and MongoDB’s engineers, who are equipped to help with validation and deployment, are where MongoDB can add its unique perspective and leverage its code conversion to win these workloads.

That said, we believe MongoDB will still need to consider the role of its services partners, which can provide MongoDB with the enterprise access it needs as it begins to look upmarket and branch outside its core developer audience. For many data ISVs, gaining the attention of global systems integrators (GSIs) is not always easy, as GSIs go where the revenue opportunities are, such as large-scale IaaS migrations or upper-stack areas like analytics that they can build around. With GenAI, however, we see GSIs paying more attention to the data infrastructure layer.

For instance, when we surveyed alliance decision makers at professional services companies, 55% of respondents said data strategy & management represented the most opportunity for partner-led growth over the next two years, up from 42% a year ago. Moreover, GSIs are refocusing on managed services, and a technology partner that can create an enabling layer for cheap and fast migration — leading to customers spending less money on implementation services and more on managed services —  represents an emerging opportunity within the ecosystem. In many ways, this shift is being driven by GenAI’s disruption of the traditional services model, but many of MongoDB’s SI partners that were represented at MongoDB.local NYC, including Accenture, Infosys and Capgemini, have also demonstrated success using code conversion tools from other vendors to hasten migrations for clients and scale their own modernization practices.


The same opportunity exists for MongoDB and SIs, provided the company puts the right partner model in place around AMP. At the end of the day, we believe that while MongoDB will be successful in using AMP to transform applications stemming from the data tier, GSIs will play a critical role in extending AMP to the application layer, optimizing that application and, most importantly, tying it to a business outcome. This will be especially true if MongoDB further integrates with its hyperscaler partners, which are perhaps best equipped to bring the GSI to the table and act as an orchestrator of a MongoDB-GSI relationship.

The opportunity is there; success will boil down to execution

Agentic AI has customers and ecosystem participants reconsidering the role of the database. MongoDB is well positioned to capitalize on the AI opportunity, partly due to its JSON-native document database, as well as its early support for native vector search in the cloud. But for customers using legacy systems, there is still much work to be done in preparing for AI, and this is where MongoDB’s investment priorities lie. GenAI provides opportunities to accelerate migrations in ways that customers, which have been running their legacy databases for decades, have never experienced. AMP, which uses LLMs to evaluate and convert code to modern frameworks, combined with MongoDB’s extensive experience in helping customers transition away from the relational model, could be a compelling way to help customers shed their legacy tech debt. Strategically, this could serve as a stepping stone for MongoDB to become a more relevant piece of the enterprise AI strategy. With this opportunity, much of MongoDB’s near-term success will hinge on execution, including a willingness to proactively collaborate with partners in new ways.

Human Capital Management in the Age of (Agentic) AI

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How all of HR will be upended by agentic AI

Let’s start by raising a glass to the human resources personnel who have stuck it out since January 2020, through a pandemic; quiet quitting; slashing layoffs, especially in the tech sector; and generative AI (GenAI) eliminating jobs. The hope now is for stability, for at least a few years, at which time digital full-time employees could shift from curiosity to commonplace.

TBR attended several tech conferences and analyst events in recent months, and AI was the inescapable topic at each one. In particular, KPMG’s Technology and Innovation Symposium in Deer Valley, Utah, stands out, in part because of the sheer breadth of opportunities discussed, use cases highlighted, and future hopes and fears laid out in stark detail. In our latest blog series, TBR on AI in 2025, we intend to connect those ideas with research and analysis conducted by TBR over the last few years to highlight implications for the companies we cover across the technology ecosystem. Topics will include how to talk to AI agents, who gets first and consistent access to limited resources like energy, and expectations for enterprise IT architecture in the agentic AI age.

In this blog, we analyze how human resources personnel and processes — really all of HR — will be upended by agentic AI.

Watch now: The Good, the Bad and the GenAI Opportunity in Cloud Ecosystems

Digital employees challenge HR models, creating opportunities for consultancies but hurdles for enterprises

Humans will get in the way of transformation in the agentic AI age. Full stop. As with all new, emerging technologies, resistance to change, inability to fully leverage new technologies and pure inertia will delay or prevent enterprises from transforming (or “reinventing”) with the assistance of AI-enabled solutions and tools. In particular, we expect:

  • The vast majority of HR teams are not prepared to hire, manage, evaluate and fire digital employees. Digital full-time employees (FTEs) necessitate a reevaluation of HR, from processes and IT architectures to required skill sets. Consultancies and platform providers see opportunities, but HR professionals see change, change and more change.
  • Everyone in the AI space talks about the “human in the loop,” but are humans ready for what that will mean in terms of day-to-day tasks and overall job performance (and satisfaction) as well as the constant change possible with the loop? It remains to be seen if a human in the loop will always result in a slow rollout of agentic AI.
  • Consultancies enjoy advising on generational gaps in the workforce and offering retraining for employees being run over by emerging technologies, but AI, in particular agentic AI, may challenge even the most adept consultants and their abilities to support HR teams.

So, who benefits from agentic AI?

Consultancies, IT services companies, and the software companies selling platforms will see their markets expand while facing tremendous competitive pressure and decreased opportunities to differentiate. As TBR has repeatedly shown through its research and analysis, companies in the tech space that partner well, partner differently, and effectively leverage multiple partner arrangements will outperform peers. Transformation in the agentic AI age will prove this out again.

Fundamentally HR management remains a back-office function that IT services companies and consultancies can use to drive managed services engagements. And TBR’s research shows that managed services can lead to additional consulting opportunities, particularly when managed services providers (whether a traditional IT services company or consultancy) partners smartly with technology companies, leveraging the data and insights generated through back-office platforms to uncover issues and opportunities.


But what about employee experience? We all remember the first half of 2020, when every company extolled their employees’ virtues and invested in keeping their talent healthy and happy. That wore off, and now the remaining employees — and the HR professionals supporting them — must contend with agentic AI digital employees as well as rising confusion and spiking costs, for some companies, around H1-B visas. Whether hiring people or robots to manage other robots, what will remain most critical is recruiting and retaining the best HR people possible, because neither the problems nor the humans are going away.

Cloud Go-to-market Benchmark

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TBR Spotlight Reports represent an excerpt of TBR’s full subscription research. Full reports and the complete data sets that underpin benchmarks, market forecasts and ecosystem reports are available as part of TBR’s subscription service. Click here to receive all new Spotlight Reports in your inbox.

 

Cloud leaders seeking to expand reach and limit internal resource investments prefer indirect sales channels

Revenue mix is quickly shifting toward indirect revenue

Across nearly all covered vendors, indirect revenue as a percentage of total revenue is growing faster than any other go-to-market metric. Vendors like Microsoft and Google Cloud even have public targets for 100% partner-attach on every cloud transaction, turning the ecosystem into the primary growth engine rather than a complementary route. At the same time, Amazon Web Services (AWS), Salesforce and ServiceNow let field sellers meet their quotas with partner-sourced or partner-transacted deals, a change that removes the historical tension between direct and channel teams. These developments underscore a consensus among cloud leaders that scale, reach and solution depth increasingly reside outside the walls of the vendor’s own organization.

Several forces explain the shift. First, customers prefer bundled outcomes over raw technology, especially as generative AI (GenAI) and industry-specific regulations complicate deployments. Partners supply integration skills, compliance assurances and managed services that vendors cannot replicate economically with their own staff. Second, marketplace private offers and cloud credits have shortened buying cycles, decreased legal overhead and preserved list-price integrity, making indirect transactions faster and less price-erosive than many direct pursuits. Third, partner economics are attractive to finance chiefs. Paying a rebate equivalent to 3% to 5% of incremental consumption costs far less than hiring, onboarding and retaining another account executive whose compensation may run well above $250,000 annually.

Graph: Cloud Revenue Growth vs Cloud S&M Expense

Graph: Cloud Revenue Growth vs Cloud S&M Expense (Source: TBR)

 

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Cloud leaders overwhelmingly shift their go-to-market efforts toward partner-led engagements, investing in rebates and milestone-based incentives to ensure alignment

Different programs implement different models for partner incentives

Cloud vendors now place global and regional IT services firms at the beginning of the deal cycle. These partners work with the customer and vendor account executive early to custom-build solution architectures, rather than waiting for the contract to close. Most programs use tier-based rebates that increase as the partner’s booked revenue crosses fixed thresholds. Cash arrives only after the workload reaches production, shifting delivery risk to the services team while allowing the vendor to keep its own headcount focused on product engineering and cost discipline. The result is shorter sales cycles and larger contract values because implementation details are ironed out early and customer confidence rises.

Each vendor fine-tunes the model differently. Microsoft and ServiceNow both offer joint credit in their field compensation plans, giving partners a transparent path to quota completion and naturally channeling complex transformations toward their clouds. AWS, Oracle and Salesforce take a margin-based route. They use escalating rebates and fee concessions that convert a slice of gross margin into customer-acquisition funding without changing payroll. Google Cloud and SAP occupy the middle ground. Both grant joint credit only after partners pass rigorous capability tests that cover industry focus, workload expertise, and verified proficiency in GenAI delivery. The tighter filter aims to make sure incentives land on engagements that reduce ramp time rather than pad service hours.

The efficiency upside is real, yet it comes with downside risks. Overly strict entry criteria limit geographic reach. Loose standards inflate payouts faster than revenue. To take a balanced approach, most vendors feed live consumption data and customer satisfaction scores into quarterly scorecards that can raise or lower joint credit automatically. A partner that speeds adoption keeps 100% credit toward their quota on future deals. A partner that misses milestones reverts to standard terms until performance improves. Vendors that refine these feedback loops and respond quickly to the data are likely to preserve a cost advantage as AI workloads expand. Maintaining that edge will depend on constant calibration of incentives, clear communication with partners, and disciplined use of real-time data.

Vendor spotlight excerpt

Amazon Web Services’ go-to-market strategy in review

Graph: Amazon Web Services' Go-to-market Strategy Overview

Graph: Amazon Web Services’ Go-to-market Strategy Overview (Source: TBR)

 

Amazon Web Services’ go-to-market metrics

Graph: Amazon Web Services' Go-to-market Metrics

Graph: Amazon Web Services’ Go-to-market Metrics (Source: TBR)

PwC Positions AI, Industry Depth and Microsoft Partnership as Catalysts for Asia Pacific Momentum

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Singapore event highlights PwC’s regional momentum, client impact and focus on AI-driven transformation

In early August PwC hosted clients, analysts and technology partners in Singapore for an in-person update on the firm’s activities in the Asia Pacific region, with a focus on PwC’s partnership with Microsoft. Among the PwC leaders who spoke were Charles Loh, partner, Singapore Consulting leader, Microsoft Alliance leader, and Digital, Cloud, Data Practice leader, PwC South East Asia Consulting; Winston Nesfield, partner, Insurance and Wealth leader, and AI Advisory leader, PwC South East Asia Consulting; Tracey Kennair (TK), partner and Asia Pacific Microsoft Alliance leader, PwC Australia; Richard Chong, managing director, Microsoft Alliance driver, PwC South East Asia Consulting; Terence Gomes, partner, Cybersecurity, PwC India; and Louise Co, senior manager, Digital Transformation, PwC South East Asia Consulting.

The event included client testimonials, partners’ technology demonstrations, and presentations by the PwC partners listed above, touching on macroeconomic issues, technology (especially AI), and expectations for the firm’s growth in the region. TBR noted that the client stories were exceptionally compelling, in part due to the clients all emphasizing what PwC did for them, the specific value the firm brought and the business problems they solved. Overall, the event included both a large number of sessions spanning a range of business challenges, technologies and PwC engagements, and plenty of time for questions for the PwC partners and their clients. TBR also noted a general optimistic outlook around the current macroeconomic environment in the region, prospects for growth, and PwC’s clients’ embrace of business model reinvention. As TK said, “Businesses need to reinvent, and that includes PwC!”

Investments in acceleration centers and Microsoft alliance strengthen PwC’s APAC digital transformation play

In his opening comments, Loh said digital transformation is accelerating, rather than slowing down, in the Asia Pacific region, despite challenges around geopolitics, supply chains and overall uncertainty in the global macro economy. PwC expects more consulting growth, and the firm has been investing in acceleration centers in Indonesia, Thailand and the Philippines. In Loh’s view, enterprises in the region need to engage in business model reinvention (BMR) and PwC’s AI-enabled approach to BMR will help companies survive and thrive. In TBR’s view, PwC’s bullish outlook on the APAC region’s demand for consulting reflects sentiments across a wide range of businesses and countries, as evident through PwC’s peers’ investments in the region and TBR’s ongoing discussions with regional enterprises and their consultancies. Further, AI-enabled BMR neatly marries two dominant market trends: incorporating AI into everything and refocusing on growth, not simply cost-cutting.

Extending Loh’s comments around digital transformation, PwC’s TK said that the firm’s clients increasingly expect technology to provide faster returns on investments, leading some clients to narrow the scope of their consulting engagements and take smaller steps toward digital transformation. TK added that PwC’s combination of strategy consulting, business innovation experience and AI-enabled solutions creates a compelling story in the boardroom, particularly when Microsoft accompanies the firm as a technology partner. In TK’s words, PwC and Microsoft are “better together” when they cosell and coinvent. With more than 7,000 PwC professionals in the region trained and certified on Microsoft’s technologies, TK said PwC has a credible and strengthening market position. In addition, TK noted PwC is doubling down on industry expertise, particularly around financial services, consumer markets and manufacturing, the public sector, and healthcare, and has a compelling “customer zero” story around change and risk management. Building on TK’s comments, Kevin Wo, Microsoft ASEAN Chief Partner Officer, added that PwC brings capabilities to move from strategy to execution and an ability to rethink business models, leading to “remarkable momentum” with PwC in the region. He noted that PwC is “investing in their own infrastructure” and helping “customers looking for tangible outcomes.”

Kevin Wo further explained his company’s deepening relationship with PwC by describing a shared commitment to execute on a joint go-to-market plan, including holding CXO roundtables, cobuilding assets and accelerators, and meeting with clients together and early in the engagement and digital transformation process. PwC’s uniqueness, according to Wo, came through the firm’s ability to help every organization become a frontier organization by leveraging AI to reinvest in the customer experience, reshape business processes, bring AI into everything, and bend the curve on innovation. He also praised PwC’s “big investment” in Microsoft and the resonance he sees from PwC’s customer zero use case. On agentic AI, Wo said Microsoft looked to PwC to lead with Agentic-led innovations to reimagine business models and accelerate enterprises’ AI transformation from proof of concepts to full scale organization adoption and customer impact. A final comment from Wo struck TBR as a not-so-subtle warning to Microsoft’s other consulting partners (who were obviously not in the room): being a distribution channel for Microsoft’s products will earn flat or falling revenues going forward, a prediction that echoes TBR’s ecosystem analysis.

TK wrapped up the day’s opening session on a more positive note, observing that while enterprises in the region have been struggling to translate AI into real outcomes, consulting partners (read: PwC) can deliver on strategy, governance and change management, making AI’s impact and business model reinvention within an organization more tangible. TK added that as enterprises increasingly seek a partner that can accelerate AI-enabled transformations, business line leaders, not CIOs, have taken the lead. She noted that PwC has “fantastic deep relationships” with the firm’s clients, well beyond the C-Suite, and can help business line leaders articulate the business advantages of AI-enabled solutions.

Client case studies underscore PwC’s Microsoft partnership

PwC clients featured prominently at the Singapore event, with participants from multiple industries and all sharing details about their engagements with both PwC and Microsoft. A financial services client noted that the PwC-Microsoft team beat out a Deloitte-Amazon Web Services team, in part because the winning team brought “heavy customization” to ensure they met every client need. Critically, PwC and Microsoft also built “four use cases and four apps” (in 12 weeks) together with the client, accelerating adoption and avoiding solutions ill-suited for the client’s IT environment and business processes. Notably, PwC directly spoke to clients’ professionals’ fears about losing their jobs to AI agents, getting ahead of disruptions and jump-starting change management. In TBR’s view, taking on fears head-on accelerates the journey to a trusted partner relationship.

A senior executive with an Indian conglomerate explained that his company selected PwC for a Microsoft Sentinel cybersecurity engagement because of PwC’s close alliance with Microsoft and applicable use cases, especially around IT and OT cybersecurity convergence, as well as the firm’s reliability, strategy consulting capabilities and scale. Notably, the Indian conglomerate is building cybersecurity experience centers specifically for OT environments utilizing PwC consulting and Microsoft solutions. In TBR’s view, this use case, especially given the client’s high profile in India and the growing relevance of IT and OT convergence, could be a marquee showcase for the firm’s capabilities. Critical to the success story is the client’s positive view of PwC’s alliance with Microsoft: every consultancy partners with Microsoft, but this client believes PwC has something unique, which makes for a compelling story for other potential clients.


In sharp contrast to the Indian conglomerate, at least in scale and client size, the CTO of an Australian civil engineering and heavy metals reseller described PwC’s AI-enabled supply chain solution, which brought mispricing mistakes down from an average of 35% to 0% across all contracts. It gets better. The AI-enabled solution allowed the company to apply its “Tier 1 service to Tier 2 clients” and “gave back two days of work a week for a four-person team.” Notably, the company did not reduce its headcount but instead shifted professionals’ time and responsibilities to doing more for customers. When asked what made PwC’s efforts special, beyond the clearly remarkable outcomes, the CTO said PwC delivered on time and on budget because of “lots of prework,” setting realistic goals, and staying within the scope. He added, “PwC enforced discipline.” For TBR, the success speaks for itself, but the more surprising aspect of this use case was the client’s size: at 300 people, this engineering firm is far smaller than the typical PwC client. In TK’s assessment, because of AI, PwC’s “client set is changing!” Indeed.

Conclusion

At a 2017 PwC analyst event in Singapore, TBR noted that PwC let its clients tell their success stories. In 2025 PwC expanded to technology partners, most prominently Microsoft, demonstrating the firm has evolved its alliances strategy to leverage one of the most critical means for gaining and retaining clients: have your technology partners tell your story, let Microsoft explain why PwC is special, abandon being agnostic, and embrace the value that closer relationships bring to every player in the ecosystem, including the client. TBR will be watching closely to see how PwC continues to evolve its alliance strategies and how a growing relationship with Microsoft leads to increased market presence and growth across the region.

GenAI Outcomes or Autonomous AI Architecture: Where Should CIOs Focus? 

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CIOs are stuck on GenAI, but is the future autonomous AI?

TBR attended several tech conferences and analyst events in recent months, and AI was the inescapable topic at each one. In particular, KPMG’s Technology and Innovation Symposium in Deer Valley, Utah, stands out, in part because of the sheer breadth of opportunities discussed, use cases highlighted, and future hopes and fears laid out in stark detail.

In our latest blog series, TBR on AI in 2025, we intend to connect those ideas with research and analysis conducted by TBR over the last few years to highlight implications for the companies we cover across the technology ecosystem. Topics will include how to talk to AI agents, who gets first and consistent access to limited resources like energy, and humans in the age of AI transformation. In this blog, we analyze the prospects for accelerated AI adoption if CIOs continue to focus on the architecture needed for generative AI (GenAI) outcomes rather than planning for the architecture needed for autonomous and deterministic AI.

What good are AI-enabled solutions if an enterprise’s IT environment and architecture can’t handle the data orchestration demands and IT becomes a roadblock to faster, better, clearer insights from AI, rather than the business accelerator expected of IT departments in the AI era? After more than a decade of consultancies and IT services companies helping IT departments become business drivers, will inadequate architecture slow down AI adoption and AI agents at scale?

Listen now: Evolving Complex Ecosystems to Solve Enterprise Transformation

Agentic AI promises enterprise transformation, but messy data, tech fatigue and architecture demands slow ROI

For years, consultancies have been exceptional at making sure CIOs become part of the business, not a cost center. In the most successful cases, IT has been a growth driver. So, agentic AI provides another opening, right? For consulting, yes, but the near-term business impact and midterm return on investment look less likely, in part because of a refrain TBR hears constantly from CIOs and their consulting and IT services providers: The data is a mess. Compounding this reality is that the relentless new technologies all require new budgets and increased spending. Just as CIO and IT departments understand how to shift their IT architecture to take advantage of GenAI, the consulting and IT services message for agentic AI changes to, “That’s not going to be enough.” At what point does fatigue take over and cause “good enough” to replace the fear of missing out (FOMO)?

Further, what will autonomous and deterministic AI architecture look like? Can enterprise IT buyers be certain today that investments in agentic AI will hold up for long enough to see some ROI or even simply be adequate for the next wrinkle — or wave — of change in the AI ecosystem? Will today’s ideas hold up? And new architecture necessitates change management, which no enterprise loves to pay for and many IT services and consulting buyers may be scarred by past experiences with cloud, blockchain, metaverse and GenAI. Are CIOs really going to believe they need to change to be ready for next-generation AI architecture needs?

So many questions, and here are some thoughts about answers (for now):

  • Leaders, the crowd and the lab: At the 8th annual KPMG Tech & Innovation Symposium, one of the speakers proposed that smart organizations will have leaders informed about and committed to AI, a wide range of employees experimenting with AI (“the crowd”), and dedicated employees developing solutions that can scale to meet the organization’s unique needs and fit its tech environment (“the lab”). Consultancies have an opening to help identify and develop internal advocates for agentic AI adoption among all three groups. Think of it as whole-of-enterprise agentic AI change management, reaching beyond the CIO while building constituents for additional investments in AI and the architecture needed to support AI agents.
  • FOMO is a multispeed reality: Consultancies can influence the narrative around AI’s promise and tangible ROI, but they need to account for how differently the various parts of an organization will experience the highs and lows of new technologies. Let’s bring this back to IT architecture: No one outside the IT organization will care, but understanding enterprisewide AI adoption requires changes across the IT stack that can help sustain internal support and turn fear into “You got this.”
  • Structured, regulated and mission-critical IT functions — read: cyber and cloud, with data governance an aspirational goal — can be fertile grounds for early agentic AI wins, particularly when IT architecture depends on proven, flexible and resilient platforms and underlying technologies.

We keep coming back to this, even when we get into weedy areas like consulting around enterprise IT architecture: AI adoption in the enterprise requires change management, and change management requires — demands — leadership from the CIO and the CIO’s boss. Also, plain old vanilla AI (not Amazon Alexa, not even GenAI), can be really valuable. With the advancements in data strategy and structure, there are insights to be drawn and even captured with agentic models to act on. AI doesn’t need to necessarily generate anything to support better decisions. And we all want better decisions.