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.

Amdocs Is Well Positioned to Continue Absorbing Market Share in the Telecom Industry; AI Is a Key Growth Vector

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2025 Amdocs Analyst Summit, Kent, U.K., Sept. 16-18, 2025 — A select group of industry analysts gathered at Port Lympne in Kent, U.K., to hear from executives and business unit leaders on the company’s strategy, portfolio and go-to-market approach, as well as other aspects of its business. Amdocs’ chief marketing officer, chief strategy officer & chief technology officer, along with leaders from the company’s key business units, presented updates on their individual areas of focus. This year’s theme, “Into the Wild,” conveyed that AI will significantly change the telecom industry, and a major portion of the event was dedicated to discussions on AI and how Amdocs is evolving its business with AI.

TBR perspective

AI will forever change the telecom industry, and Amdocs aims to be at the intersection of that change, helping its customers (primarily communication service providers [CSPs]) lean into and reap the benefits of this new technology paradigm. CSPs are still trying to figure out what they want to be in the digital economy — a feat made more difficult by the advent of AI. Some telcos aim to remain utilities, providing connectivity services, while others seek to become techcos; additionally, some aim to be a hybrid of the two. Amdocs offers solutions that can help CSPs in whichever path they choose.

Due to forays into new areas, such as personality agent engineering for brand evolution, customer experience design and agentic AI transformation, Amdocs estimates its Serviceable Available Market (SAM) is $57 billion in 2025, up from $36 billion in 2021.

TBR sees multiple tailwinds blowing in Amdocs’ favor and the company is likely to continue gaining market share in traditional and newer market areas it is targeting. AI will juice this growth as Amdocs seems to be more sophisticated with its AI strategy and offerings compared to its primary competitors (especially as it pertains to the telecom industry). Though Amdocs is making a strong push toward AI transformation, most CSPs will delay adoption due to technical debt and data usability problems, as well as a focus on pending M&A. (Amdocs tends to consolidate and grow market share in CSP M&A events, while weaker, unfocused vendors tend to lose share as synergies are realized.)

This situation may limit the traction Amdocs’ AI initiatives obtain in the short term but will keep the company busy with plenty of business opportunities on the latter issues (i.e., addressing technical debt and data usability challenges) in the meantime. CSPs are investing in AI, but the nature of those investments is more about implementing targeted, quick-hit use cases than large-scale transformation initiatives.

Watch on demand: Telcos Risk Losing the AI Race Without Strategic Shift; $170B at Stake by 2030

Amdocs aims to be a brand consultant and technology enabler for the agentic AI era

AI will have a significant impact on CSPs’ brands. A reassessment and reassertion of brands will be necessary as agentic AI takes hold, and CSPs will need to determine what the agentic version (including the look, feel and personality) of their AI interfaces (e.g., brand avatars) will be. Amdocs has conducted extensive research into this emerging area, dubbed personality agent engineering, and aims to take a consultative approach toward helping CSPs position their brands in the AI era, including planning, design and development of AI avatars as well as aligning their brand messaging with their brand strategy.

Although TBR believes it is very early days for agentic AI branding, Amdocs’ early foray into this emerging area and thought leadership underscore how the company is seeking to move into new and adjacent areas as it expands its offerings, especially around consulting, design and transformation enablement.

Technical debt and data usability impediments continue to bog down CSPs, which will keep Amdocs busy

AI adoption is accelerating rapidly but most CSPs are not prepared to implement it at scale within their organizations. Two key reasons for this are the persistent challenge of dealing with technical debt and the data usability problem. In terms of technical debt, despite having begun cloud migrations more than 15 years ago, the telecom industry is only an estimated 30% complete with this transition, with nearly all of that 30% being in the IT domain.

CSPs have barely made any progress in migrating network domain workloads to the cloud. Lacking the full potential that cloud offers in flexibility and agility hinders CSPs in adopting new architectures and platforms, such as open vRAN and cloud-native networks. Further, most CSPs are still running legacy technologies at large scale that are at least 20 years old, such as xDSL, MPLS, 2G and 3G. If CSPs move this slowly, how will they ever obtain the flexibility and agility that AI requires?

Additionally, CSPs face a universal data usability problem, one that could derail or significantly delay the timeline of their AI transformations. AI is only as good as the data it is trained on, and data is the foundation on which AI is built. The reality is that most enterprises, especially CSPs, lack a unified data lake, limiting their ability to effectively train AI models. Rather, most organizations have data silos and data islands, all with varying levels of governance and oversight.

CSPs especially have a data problem because most are amalgamations of M&A over decades, and with each M&A event, new data layers are added to the fold, but they usually remain largely separate. A nascent crop of data management platform companies, such as Databricks and Snowflake, aim to tackle this issue head-on for enterprises, but TBR’s analysis suggests CSPs continue to underestimate the time and cost of data management transformation.

Amdocs plays a unique role in the ecosystem as the company is a change agent for consolidation and digital transformation on the software systems side (particularly for OSS and BSS). This uniquely positions the company to help CSPs consolidate and build aspects of this data management framework via Amdocs’ AI and Data Platform. So, even though CSPs may be delayed in adopting AI at scale, Amdocs will still be extremely busy helping them become AI-ready.

Business outcomes are the future, but the path there is uncertain

Amdocs executives and the analyst community represented at the event broadly agreed that business outcomes will become the primary monetization model in the AI economy. However, there was also broad agreement that what this looks like from a commercial model structure perspective remains a big unknown. Labor-related tasks have historically been monetized on a time-and-materials or cost-plus basis, and software has transitioned to a subscription-based, consumption-based or “as a Service” model. Selling outcomes will be very different (e.g., how to price, how to measure value, how to assess and manage risk, how to structure terms and conditions of a contract), and this model will likely significantly impact labor-oriented businesses, such as global systems integrators. The selling of business outcomes also requires changes to procurement and sales organizations.

TBR expects commercial models to evolve first to a hybrid of outcomes and more traditional models, with traditional companies primarily taking this more risk-mitigated approach. Meanwhile, TBR expects a new crop of disruptive companies that are more heavily geared toward outcome-centric models to enter the picture. This type of market disruption was witnessed with the SaaS trend, and the march toward selling outcomes will likely follow a similar trend.

Product (technology)-led services go-to-market approach is more conducive to outcome-based commercial reality

Amdocs firmly believes its product-led services approach is unique and better suited to aligning with market changes being brought about by AI and the shift toward outcome-based commercial models. Specifically, leading with products embedded with AI capabilities eases the impact of disruption on the labor side of the business model. This contrasts with C&SI firms, which are services-led and services-centric in their go-to-market and pricing models. Services-led companies likely face a much greater impact from disruption than companies that lead with products but also provide services related to those products.

Like all companies, Amdocs will still face fundamental changes to its organizational structure, workforce and delivery models from these market trends (i.e., AI and commercial model evolution), but TBR believes the magnitude and associated risks of that impact will be relatively less for Amdocs compared to traditional C&SI services firms.

Accountability-based model will become more desirable as the telecom industry navigates deeper into uncharted waters

Amdocs prides itself on its “never give up” mindset and approach to work. This accountability-based model is culturally embedded within the organization and is a key differentiator and selling point for the company when positioning itself for new business. Additionally, Amdocs’ expertise in dealing with mission-critical systems (e.g., carrier-grade networks and supporting operational and business systems) makes it an ideal partner at a time when companies are facing monumental disruption and persistent change. TBR expects Amdocs will increase its share in the telecom market and find new opportunities in other verticals, especially those that are also in mission-critical sectors, such as financial services.

TBR notes that Amdocs’ positioning differs from that of most other vendors and C&SI firms, which may have a lower tolerance for risk and risk sharing and are more apt to disengage or deleverage situations when significant problems arise. History is full of examples where services providers missed the mark with customer transformations and had to pivot midproject, and customers are cognizant of the risk of these types of situations potentially occurring.

Conclusion

Amdocs may be getting too far ahead of its customers in terms of AI, as CSPs are not ready to embrace and adopt the AI world Amdocs envisions. However, a plethora of CSP needs in the areas of system consolidation from M&A, as well as technical debt and data usability, will keep Amdocs busy with a steady flow of business opportunities for years to come. Amdocs is well positioned at the intersection of the three aforementioned trends in the telecom industry.

Additionally, thanks to its unique product-led services business model, the company is also well placed to thrive amid the impending disruption that will result from a shift to outcome-based commercial models and the impact of agentic AI on the professional services industry.

TBR Launches Cloud Go-to-market Benchmark

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Technology Business Research, Inc., is pleased to announce the launch of our Cloud Go-to-market Benchmark. Go-to-market strategies are constantly evolving, and as cloud vendors adapt to shifting buyer behavior, expanding ecosystems, and the demands of emerging technologies like AI, they are also updating their sales motions and spending. TBR’s new Cloud Go-to-market Benchmark quantifies these shifts at the financial level, tracking how vendors allocate sales and marketing (S&M) spending across direct and indirect channels.

As numbers alone do not tell the full story, this report layers in strategic context, examining how partner investments align with each vendor’s priorities and how these priorities are changing in the AI era. Whether it is the balance of internal versus partner-led sales or the evolving structures of ecosystem support, the Cloud Go-to-market Benchmark offers data-backed insight into where the cloud leaders are going and what that means for the broader competitive landscape.

Clients gain access to sales expense data split by direct and indirect sales motions, workforce headcount split by direct and indirect sales teams, and year-to-year growth comparisons for seven of the biggest cloud leaders covered by TBR. Additionally, you’ll find the qualitative backdrop behind these numbers, offering the necessary context for external stakeholders to navigate the changing landscape.

Cloud Go-to-market Benchmark is now available on TBR Insight Center™. Click here to preview the first publication.

Cloud Go-to-market Benchmark Excerpt

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

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.

Graph: Total Cloud Revenue Growth vs Cloud S&M Expense Growth, 1Q25

Graph: Total Cloud Revenue Growth vs Cloud S&M Expense Growth, 1Q25 (Source: TBR)

 

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.

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

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.

Cloud Go-to-market Benchmark Excerpt

Cloud Go-to-market Benchmark Excerpt (Source: TBR)

 

Vendor spotlights in the initial edition of this report include hyperscalers Amazon Web Services, Google Cloud, Microsoft and Oracle and SaaS providers Salesforce, SAP and ServiceNow

Cloud Go-to-market Benchmark Vendor Spotlight: Microsoft

Cloud Go-to-market Benchmark Vendor Spotlight Excerpt (Source: TBR)

Ericsson’s Biggest Customers and Partners (Operators) Are Holding it Back

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2025 Ericsson Industry Analyst Event, Boston, Sept. 11, 2025 — A select group of industry analysts gathered at Convene in Boston to hear from Ericsson leaders, partners and customers about the company’s Enterprise business unit’s strategies, capabilities and opportunities in domains such as private cellular networks (PCNs) and network APIs, with AI and 5G monetization serving as themes that ran across the various executive presentations.

TBR perspective

Ericsson struck a cautiously optimistic tone at its annual industry analyst event, which focused on the Enterprise segment. The company acknowledged struggles and highlighted learnings and adaptations, especially pertaining to Vonage and the new Aduna joint venture for network APIs, which will set the stage for better outcomes moving forward. Ericsson is uniquely positioned to capitalize on the still-nascent PCN opportunity that is developing globally, but the vendor’s go-to-market encumbrances continue to constrain its growth prospects.

Specifically, the range and nature of Ericsson’s partnerships with the broader PCN ecosystem remain relatively limited compared to other players in the domain, especially frontrunner Nokia. Ericsson’s go-to-market channel beyond CSPs for enterprise growth areas remains limited relative to competitors such as Nokia, particularly in PCN. Nokia decided several years ago to reduce its reliance on CSPs and made a concerted effort to sell its PCN solutions directly to enterprises and through a robust roster of channel partners, including global systems integrators (GSIs), niche systems integrators (SIs), VARs and communication service providers (CSPs).

Ericsson has little control over one of its biggest challenges: CSPs are difficult to deal with, hesitant to work together for competitive reasons, and move slowly. Compounding this, CSPs are Ericsson’s largest customer cohort and partner channel, and TBR estimates more than 97% of Ericsson’s total company revenue through direct and indirect means stemmed from CSPs in 2024. These are key reasons why TBR expects Ericsson’s enterprise revenue will lag its potential in areas such as PCN, network APIs and communication applications.

Impact and Opportunities

FWA has significantly more room to run

Ericsson estimates that approximately 25% of all mobile broadband traffic globally is fixed wireless access (FWA) now and that 18% of premises globally will utilize FWA within five years, representing unprecedented growth considering FWA only began to take off in 2020. These statistics align with what TBR has been saying for several years: The market opportunity for FWA is much larger and more vibrant than the industry originally thought.

For example, TBR estimates FWA is technologically and economically feasible to support up to 50% of residential premises in the U.S. This opportunity is helped by the rapid time to deployment and strong value proposition the technology provides end users, especially when compared to other broadband access mediums like fiber-to-the-premises (FTTP), which is laborious and expensive to build out and has higher service costs for the end user. Business premises can also be strong candidates for adopting FWA. Ericsson is arguably the largest beneficiary of the FWA movement on a global basis in terms of revenue generated by selling FWA enablement products and services to CSPs (infrastructure only, not including customer premises equipment [CPE]).

Awareness gap in market slows adoption

On several occasions at the event, Ericsson leaders mentioned that there is an “awareness gap” in terms of the efficacy and outcomes that 5G-enabled technologies are achieving, especially as it pertains to PCN for businesses and the public sector. For example, Newmont, a Tier 1 mining company that adopted a private 5G network from Ericsson to remotely control its dump trucks at the mine, has realized a tenfold increase in coverage and a significant corresponding boost in productivity.

Such outcomes are compelling to enterprises looking to transform their operations to drive more revenue growth and or reduce costs. Though word is gradually getting out that early adopter enterprises are achieving strong results from new technology solutions, more needs to be done. Greater emphasis on partnerships with companies that have the ears of senior management at enterprises, most notably global systems integrators (GSIs), are a key way for Ericsson to promote the benefits of these solutions.

Differentiation, both in technology and marketing, needs to be addressed

Ericsson needs to do more to differentiate its technology solutions, and this includes ensuring that differentiation is well messaged to the market. Though some consolidation has occurred in the PCN, network API and communications applications domains, there remains significant competition and fragmentation, with many vendors playing in the market that are not well differentiated. This lack of differentiation is likely another reason Ericsson is struggling to achieve outsized growth in these nascent market areas. TBR did not hear a compelling narrative as to how Ericsson is differentiating itself in terms of technology and partnerships in these key, high-growth market areas. It remains to be seen if Ericsson’s NetCloud AI-powered cloud management and orchestration solution for PCN becomes a key differentiator once it is scaled up to large deployments of the Ericsson Private 5G solution.

Private cellular networks channel remains underdeveloped

Ericsson’s PCN revenue is growing at a relatively strong double-digit rate, but the size of the business is less than 60% the size of frontrunner (outside of China) Nokia’s PCN revenue, according to TBR’s Private Cellular Networks Vendor Benchmark. This differential in revenues is primarily due to Ericsson’s underdeveloped go-to-market approach and channel relative to Nokia. This is an issue TBR has identified and written about for the past few years, but Ericsson seems to have made minimal progress.

Ericsson is partnering extensively on PCN, but according to TBR’s research, most of that activity is driven by CSPs — even though enterprises and the public sector primarily work with GSIs, niche SIs, VARs and government contractors on digital transformation-related initiatives. Ericsson is also partnering with non-CSPs, including a new agreement with NTT DATA that reflects the kind of deeper, broader partnerships TBR believes Ericsson should pursue. Surface-level partnerships are essentially reseller arrangements, whereas some vendors have robust engagements with key GSIs and other types of partners. Nokia’s relationship with EY and Kyndryl are two such examples.

Connected laptops is a niche market, not a mass market opportunity

Ericsson has jumped on the bandwagon of 5G-connected PCs, and representatives from T-Mobile and HP Inc. spoke at the event about why this is a unique market opportunity. Though 5G-connected PCs sounds like a great feature that end users will utilize, TBR believes the additional cost required to embed the 5G modem into the PC, plus the subscription fees that would need to be paid to the service provider, broadly limits the scope of who would actually find enough value in the product and corresponding service to actually pay for it, especially when most people have smartphones and those smartphones have mobile hotspot tethering, which essentially makes the computer a cellular-connected device at no additional cost.

To be sure, 5G-connected PCs offer a unique capability that the mass market would value, but once extra cost is involved, only a small fraction of that market is likely to pay for the experience. Some enterprises and select types of SMBs (e.g., construction firms) and small office/home office (SOHO) workers (e.g., real estate agents and other road warrior workers) would be unique candidates for 5G-connected PCs, but this would be more of a niche market than a mass market opportunity. As such, TBR suggests network and PC vendors reassess their addressable market projections to be more aligned with observed user behavior and price-for-value considerations.

Conclusion

Ericsson has competitive technology, but its overreliance on CSPs to purchase that technology and/or scale it into end markets remains a weakness that will continue to hamper the company’s ability to participate more significantly in key growth domains, such as PCN. On the network API and communications application side, progress is being made and some scale is occurring, but Ericsson and its CSP partners are up against relatively fast-moving, well-resourced and more specialized entities, most notably hyperscalers and other digital-native players. Addressing the telecom industry’s weaknesses and shortcomings in these market areas will require more investment in channel development and more robust strategic partnerships with entities such as government contractors, GSIs and niche, domain-specific SIs.


Ericsson’s dependence on slow-moving CSPs is a risky proposition, especially when it comes to driving growth in key areas, including PCN, network APIs and communication applications. Ericsson should take a page out of Nokia’s playbook for aligning its opportunity areas with non-CSP players to accelerate growth. Specifically, Ericsson should take a closer look at how Nokia structured its PCN business, especially its channel ecosystem, to reduce its reliance on CSPs. This has proved to be the most optimal way to participate in opportunities arising in the enterprise end markets Ericsson is targeting.

HCLTech’s Expanding KYC Journey: From Technology Provider to Trusted Compliance Partner

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HCLTech’s expanded capabilities, new geographies and deeper client impact prove that a successful use case can be just the beginning

Use cases in the IT services space can bring technology to life. Everyone loves a good story. And following up on a successful use case to see what happened two years on doesn’t happen often enough. In fall 2023, TBR discussed with HCLTech the details of a know-your-customer (KYC) solution HCLTech developed for a European bank, and TBR highlighted the possibilities this solution unlocked. Almost two years later, TBR heard “the rest of the story” and gained additional insights into HCLTech’s growing portfolio of KYC solutions.

In a meeting with TBR, Subrahmanyam Umashankar (Uma) and Gourav Dilip Sontakke from HCLTech’s Financial Crimes Prevention practice discussed the recent updates on the engagement with the European bank to implement the KYC solution, which we originally discussed in 2023. The solution has been used in more than 40 countries and has evolved from accelerating and strengthening periodic reviews to powering autonomous KYC journeys, such as onboarding and event-driven reviews.

As anticipated, success in the KYC program with the European bank springboarded HCLTech into KYC opportunities with multiple other European banks, as well as an Australian bank. Uma and Gourav noted that HCLTech shifted from squad-based pricing to outcome-based service models and developed deeper engagement with client teams to better understand their specific processes and pain points. Leveraging the power of their AI-intrinsic design, HCLTech implemented a centralized digital KYC policy, purpose-built KYC workflows and customer-tailored notifications, with additional capabilities such as digital verification, intelligent document processing and a customer self-service portal on the road map. In short, success.

By evolving its KYC offerings across platforms and clients, HCLTech shifted from tech implementer to outcomes-driven partner

As HCLTech extended the KYC offering with additional banks, the company adjusted to different core platforms (such as Pega and Fenego) and shifted from traditional resource-based pricing to outcome-based service models, showcasing its confidence in delivering tangible results. HCLTech’s approach emphasized not only technological implementation but also a holistic reimagining of KYC processes, avoiding simply “lifting and shifting” existing inefficient systems. True to HCLTech’s DNA, Gourav noted that HCLTech brought core engineering capabilities to bear, allowing the company to be both flexible and innovative with clients, particularly when addressing average handling time, the most common metric (and pain point) in KYC.

A few additional points, from TBR’s perspective:

  • Uma commented that implementing multiple KYC solutions on different platforms has accelerated HCLTech’s skills on those platforms and extended the company’s domain expertise: “It has helped us strengthen our practice team in financial crime compliance.” Having clients essentially fund HCLTech’s training and delivery experience benefits HCLTech in multiple ways.
  • TBR previously noted that HCLTech worked alongside Big Four firms as they advised banks on financial crimes and other risk issues, with HCLTech profitably (and smartly) staying in its own swim lane, a strategy not every IT services company executes successfully. Uma confirmed that as HCLTech brought the KYC solution to additional clients, the company continued to work alongside Big Four firms, although with an important shift over the last couple of years. Banking clients, recognizing the success of the KYC solution with the European bank, now seek HCLTech’s “outside-in” view of broader KYC, financial crimes and compliance, offered in tandem with a Big Four firm’s perspective.
  • Multiple times during the discussion, Uma and Gourav delved into the intricacies of measuring success around KYC, from the perspective of the bank and its clients. In contrast, two years ago, HCLTech’s story mostly centered on the technology and how the company could quickly, securely and effectively implement a technology solution. Success, in part, has shifted the goals for HCLTech from delivering technology to delivering outcomes.

If HCLTech continues successfully expanding its KYC clients and extends further into consulting around financial crimes and compliance, the company will likely begin attracting more attention from both technology product companies looking for aggressive and growing alliance partners and India-centric peers already well-established in the financial services space. In TBR’s estimations*, HCLTech’s financial services revenue represents about 20% of the company’s overall IT services revenue, a share that has remained relatively constant over the last three years.

Peers such as Infosys (28%), Tata Consultancy Services (31%) and Wipro (32%) earn a considerably larger share of their revenues in that industry, arguably exposing them to greater risks if and when financial services revenue growth slows. Competitive threats aside, TBR believes HCLTech’s investment and subsequent success in the KYC space, as well as partnering — or at least collaborating — with Big Four firms, bode well for the company’s overall performance. As financial crime evolves, HCLTech’s AI-intrinsic design and autonomous KYC capabilities position it to lead in a space where banks and regulators are never going away. It is just a question of who stops them, serves them and answers to them.

 

*TBR uses its own taxonomy to estimate revenues of 17 IT services companies across seven industries. Start your TBR Insight Center™ free trial today to access this client-only proprietary data.