The Federal IT Services Reset: Traditional Consulting Evolves to Embrace Mission Outcomes

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The traditional consulting paradigm in the federal sector has been upended in the post-DOGE environment

Federal IT demand will not return to a consulting-heavy transformation cycle. Agencies remain under cost pressure as AI adoption at scale crawls from pilots to production, while spending concentrates on mission-critical modernization, cybersecurity and defense. Amid the recent chaos, is an opportunity emerging for companies offering platforms, automation, managed services and technology programs with measurable operational outcomes?
 
TBR’s Federal IT Services research includes annual profiles of Accenture’s, CGI’s and IBM’s U.S. Federal Services practices. This special report examines current trends, those companies’ strategies and recent performances, and expectations for federal fiscal year 2027 (FFY27).
 
CGI Federal enters the environment described above with the cleanest near-term story because its platforms already map to efficiency, financial management, and fraud, waste and abuse (FWA) reduction. TBR believes Accenture Federal Services (AFS) will have an upside when enterprise AI adoption and defense modernization accelerate. IBM Consulting’s U.S. federal unit has a relevant technical portfolio, but questions remain about whether the company can parlay its technology competencies into sustained contract growth. One certainty and two questions.

Federal IT investment pivots toward operational platforms and mission-ready AI while demand for traditional consulting evaporates

The federal market remains split. Civilian spending is constrained, while defense, intelligence, homeland security and selected mission-critical civilian programs are roaring. Durable opportunities stem from AI-enabled automation, cybersecurity, shared services, fraud reduction, modernization with the Department of Veterans Affairs (VA), secure cloud, and mission systems, with defense growth strongest around missile defense, AI-enabled mission systems, cybersecurity, Intelligence, Surveillance, and Reconnaissance (ISR), secure cloud-to-edge environments and software-defined modernization. That reads like a long list of fantastic growth opportunities for IT services companies.
 
Looking at recent results, CGI Federal returned to growth ahead of AFS and IBM Consulting, supported by more than $1.2 billion in 1H26 contract value and strong demand for its Momentum financial management and Sunflower asset management platforms. Lower dependence on conventional strategy and management consulting, areas targeted by DOGE, gives CGI Federal a degree of structural advantage.
 
Critically, CGI Federal is winning the right work: nearly $500 million of 1H26 awards tied to the Environmental Protection Agency, VA and General Services Administration involved Momentum, creating multiyear opportunities across hosting, licensing, operations, maintenance, upgrades and cloud optimization. CGI Federal’s AI-enabled Fraud, Waste and Abuse platform, which embeds AI in an operational platform rather than selling it as a stand-along consulting promise, also has a positive trajectory as federal AI enters a more intense phase.
 
Although this may be an oversimplification and counterexamples abound, U.S. federal agencies have begun moving beyond generic generative AI pilots and are focusing on hardened, secure, mission-specific AI platforms. As in the commercial enterprise space, data readiness, governance, cybersecurity and integration determine the depth and pace of AI adoption at scale.
 
Among the advisory-focused federal systems integrators, TBR believes AFS stands to benefit the most as this trend evolves. AFS has built the broadest AI-native alliance ecosystem among the three, leveraging relationships with OpenAI, Anthropic, Palantir, Databricks and Quantum Computing Inc. to position across the federal AI lifecycle of experimentation, application design, deployment, managed services and ongoing operations. Unsurprisingly, AFS’ alliances strategy mimics that of its parent company, which has long set the standard for ecosystem strategy.
 
Like Accenture, AFS addresses security, data quality and governance through partner orchestration rather than a proprietary stack. Databricks supports data curation; Anthropic brings secure AI; Palantir supports mission data and applications; OpenAI provides a foundation for agentic AI development and deployment. The challenge for AFS is whether the use of AI in the federal sector will remain experimental through 2027 or if AI will finally begin to scale across agencies particularly in the civil space.
 
In contrast, TBR believes CGI does not need an AI boom to continue growing in the federal sector. The company can use Google Cloud Gemini and other tools to improve design, testing, automation, security and shared-services offerings, and enhance its flagship Momentum and FWA platforms — solutions agencies already buy. IBM’s approach is more technology-led, built around watsonx, AIOps, Red Hat, hybrid cloud and defense platforms. The technology is highly relevant; the commercial proof is still catching up.

In FFY27 federal IT vendors will parlay AI capabilities into repeatable platforms, measurable outcomes and mission impact

In the defense and intelligence space, AI-enabled mission systems, missile defense, cybersecurity, ISR, secure cloud and edge computing drive federal IT spend. AFS has been deliberately trying to increase its relatively modest defense exposure (currently just 20% of AFS revenue, per TBR estimates). Garrett Berntsen’s appointment as chief AI officer is telling: His background includes leadership roles in the Department of Defense’s (DOD) data and AI organization.
 
AFS can meaningfully expand as defense AI spending accelerates, and TBR strongly believes it will. Similarly, TBR estimates roughly 30% of IBM’s federal revenue comes from DOD and intelligence accounts, and TBR believes IBM’s Digital Intelligence Suite for Defense could deepen that presence by turning secure AI capabilities into a repeatable platform. IBM has also secured a position on the Missile Defense Agency’s SHIELD program, although it has not yet received task orders as of the publishing of this report.
 
For IBM, the SHIELD program illustrates both the opportunity and the problem. The company’s AI, cybersecurity, data governance and systems integration portfolio fits future defense requirements well, but contract access must translate into actual task-order revenue before the strategic potential matters financially.
 
Echoing trends TBR sees across the commercial sector, federal technology and services buyers are consolidating work around strategic partners that can combine AI, cloud, data, cybersecurity and operations into repeatable delivery and measurable outcomes. Agencies are moving away (slowly now but accelerating soon) from labor-based consulting and toward platform-enabled managed services that reduce cost, improve security and accelerate mission execution.
 
CGI appears to be the closest vendor to that model in federal IT. Its platform-led business, contract momentum and embedded AI strategy align cleanly with agency demand for efficiency and operational continuity. AFS is rapidly transforming toward the model through alliances and will be well positioned to capture new AI-based implementation awards if agencies finally move AI into production at scale. IBM has the technology foundation, but it needs stronger commercial traction and a more visible federal ecosystem strategy.
 
In TBR’s view, in FFY27 the federal sector will not reward the providers that simply sell AI capabilities. Instead, the IT services companies that can turn AI in lower costs, stronger security, faster mission execution and measurable results will reap the benefits of this emerging growth area, stronger security, faster mission execution and measurable results. CGI is there today; AFS has the ecosystem to scale; IBM still needs to prove it can convert technology relevance into sustained federal growth.
 
For additional insights join the Oct. 8 TBR Insights Live Webinar: From Contraction to Opportunity: What’s Next for the Federal IT Market and Leading Systems Integrators?

The Time to Start Building Your Multiparty Alliance is Now

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“Our leadership sees partnering with GSIs and the largest consultancies as a strategic priority now and into next year!”
 
Over the last few months, TBR has heard that refrain repeatedly from established software companies, AI-native product companies and startups, from companies older than Microsoft to companies with just a dozen employees. Everyone, it seems, has decided that consultancies and systems integrators provide the best path to growth.

Why do consultancies and systems integrators seem like the best path to growth?

AI has made everyone in the technology space, particularly in services, question the value they can bring to enterprise clients, and what they can provide that AI cannot. Companies are also looking at how to protect their value proposition in a shifting ecosystem, where new players are constantly emerging with new technology and business models. Companies are being challenged to adapt their old partnering models — sell to, sell through, sell with; channels; and traditional joint business groups — to an AI age. Overall, AI has made companies across the ecosystem rethink their partnering strategies.

What is the fastest way to ally, expand and grow with GSIs?

Partnering smartly with global systems integrators (GSIs) and large management consultancies comes down to answering three basic questions:

  1. Can I help them with client retention? Keeping their clients motivates GSIs and consultancies perhaps more than almost any other factor. Retention lets them expand the breadth of services clients pay for, boosting revenue and improving margins. Every ecosystem partner needs to contribute to client retention.
  2. Can I be flexible on pricing? Before the AI age, commercial constructs fell to sales teams and alliance leads. Now, customers expect flexibility, especially for longer-term engagements, and boards want to know how partners can help adjust commercial models to meet those changing demands. An ecosystem partner that cannot, at a minimum, accommodate discussions around outcome-based pricing risks losing opportunities with GSIs and consultancies.
  3. Can they explain what I do? Clients want to know why a GSI or consultancy recommends a specific product, solution or platform. “Technology agnostic” died sometime around 2022, and clients expect their services partners to bring the most applicable, scalable and innovative technologies, and on favorable terms. Plus, few things are more credible than someone telling your story well (especially if it’s a customer-zero story, though that won’t always be the case).

TBR has the data to back this up. The three questions above come from 30-plus years of examining the business of companies in the technology space and, over the last 10 years, analyzing how much their business depends on the ecosystem.
 
TBR Graph: How Services Partners Separate Themselves from the Pack

What will the IT services and tech ecosystem look like in 2 years?

Everything in the AI age changes fast, so what will smart strategies for partnering with GSIs and consultancies look like in 2029?
 
TBR expects multiparty relationships will become the norm. While it’s challenging to get commercial terms and sales teams aligned, in the last few years we have seen a growing number of examples where a consultancy, an IT services vendor and a cloud hyperscaler have developed joint solutions and go-to-market motions that result in multiparty alliances delivering on complex engagements. Add OEMs, chip makers and AI-natives, such as Anthropic, to the mix, and multiparty will become what clients always expect.
 
Within those alliances, TBR expects certain relationships will take the idea of strategic partner to the next level. Explicitly, publicly designated partnerships will enjoy a most-favored-nation-like arrangement, deepening the business group model pioneered by Accenture and SAP. The experiences, lessons learned, and benefits of going to market with codeveloped solutions will compound, truly and forever quashing the idea of a company within the ecosystem being technology agnostic.
 
Underpinning all this, of course, will be the imperative among all players to carefully and completely delineate who does what.
 
TBR Graph: Best Description of Services Respondent's GenAI Commercial Models Used

Strategic steps for right now

Every software and technology company should be building multiparty alliances through codeveloped solutions supported by joined-at-the-hip sales teams. Additionally, executives should be thinking about, planning for, acting on and beginning to deliver around outcome-based pricing.
 
GSIs and consultancies will be compelled to sell and deliver on outcome-based engagement structures and will want to partner with technology vendors that can do the same.

What Enterprise IT Leaders Are Prioritizing Next: Insights from TBR’s Infrastructure Strategy Survey

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Enterprise IT organizations are entering a new phase of infrastructure transformation. While AI remains a major catalyst for investment, organizations continue to balance modernization initiatives with ongoing pressure to optimize costs, improve operational efficiency and prepare infrastructure for increasingly complex hybrid environments.
 
TBR’s Infrastructure Strategy Customer Research, based on a survey of U.S. enterprise IT decision makers, reveals how organizations are prioritizing infrastructure investments, where budgets are shifting, how AI is influencing infrastructure strategy and what vendors must do to remain competitive.

In the on-demand webinar below, Principal Analyst Angela Lambert and Senior Analyst Ben Carbonneau share their latest survey findings and what results mean for infrastructure vendors, technology strategists and enterprise IT leaders. Key topics include:

  • How AI is reshaping infrastructure spend, including investment priorities, deployment challenges, and the infrastructure capabilities organizations are building to support AI workloads
  • How enterprise infrastructure strategies are evolving, including changing workload placement decisions across data centers, cloud, edge and colocation environments, amid emerging trends such as cloud repatriation and hybrid IT
  • How IT organizations are modernizing operations, including priorities for automation, infrastructure management, services and vendor selection as enterprises work to improve efficiency with limited resources

 

 
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.

AltitudeIQ and the Coming Disruption of AI-powered Enterprise Automation

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In late August, TBR met with AltitudeIQ Founder and CEO Acyr da Luz and AltitudeIQ Board Advisor Michael Yadgar to discuss the startup’s proprietary technology, its role within the technology ecosystem, and its potential to disrupt the broader IT services, consulting and AI space. The following reflects that discussion as well as TBR’s ongoing research and analysis of SAP, IT services companies, consultancies and AI. 
 
Reducing the human effort needed to complete repetitive and low-thinking tasks ranks prominently among AI’s promises in the enterprise space. If a well-trained robot can do it, AI will do it, freeing humans for higher-value work. The reality to date, however, is that bots remain more expensive than people, and the costs of running AI continuously can be ruinous. When that begins to change, TBR expects startups like AltitudeIQ to be at the technological forefront, with business-model disruption right behind them.
 
While TBR does not rank speeds and feeds and does not measure latency or token usage, the business impacts AltitudeIQ has demonstrated with clients imply considerable changes coming for IT services companies and consultancies helping their clients adopt emerging and, maybe more significantly, mature but complex technologies, such as SAP’s S/4 HANA. In short, AltitudeIQ could help enterprises realize AI’s productivity promise while significantly upending IT services as we know it.

What AltitudeIQ is and is not

Founded less than a year ago, AltitudeIQ offers an agentic AI software solution to an age-old business problem: how can a company design, build, test and deploy enterprise applications faster? If every phase — from business process and efficiency through data migration to training and enablement — could be reduced from multiple months to weeks, even days, by deploying AI-enabled agents, enterprises could spend less time and money on consulting and IT services, keep their top IT talent engaged on niche and challenging problems, and derive more value from their ERP systems, such as SAP.
 
By tackling every phase with a combination of prebuilt skilled agents, expert-directed agents and human-led decision making, AltitudeIQ has begun proving to clients that a 30% reduction in time and expenses is possible, even in a large-scale enterprise with complex IT environments and reporting requirements, according to Luz and Yadgar. TBR notes that one key characteristic that may separate AltitudeIQ from other startups in the agentic AI space is the founder’s 20-plus years of experience as a consultant. He and the team have designed a technology to solve a business problem, rather than designing a technology solution and then looking for a business application.
 
Further, Luz and Yadgar repeatedly assured TBR that AltitudeIQ will remain a software company content to partner with services companies. As Luz commented, “Some clients are asking us to take services, and we’re inviting consulting firms to partner with us … one client asked for help with the business case and implementation, and we said, ‘OK, good, but I’m going to bring another partner.’ … I don’t want to be doing the service work there … we don’t want to go down that path … That’s strategic for us to stay on software.” In TBR’s view, this combination of a business-problem mindset and a focus on just software makes AltitudeIQ an ideal partner for IT services companies and consultancies — at least for now.
 
Speed cannot be all that an AI-enabled solution delivers, particularly for enterprise clients with substantial governance, risk and compliance (GRC) considerations. Speed must be paired with quality and trust. For every startup, proving quality and gaining trust present challenges, and AltitudeIQ has approached one by upending the established order and the other by following a tried-and-true strategy.
 
Luz and Yadgar relayed an experience with an enterprise client’s IT team: “The technical people were very skeptical,” according to Luz. But AltitudeIQ took on a problem that had festered for nine months and solved it in two and a half hours. While not every problem will be as easy to solve, this example shows AltitudeIQ’s mindset: that its work can withstand scrutiny and testing and it can handle enterprise clients’ most complex problems. Partnering with global brands such as Big Four firms, global systems integrators (GSIs), and large management consultancies also conveys AltitudeIQ’s quality to clients.
 
Consultancies and IT services companies repeatedly tout trust as part of their value proposition. For example, Big Four clients expect any startup a Big Four firm introduces to be thoroughly vetted, which builds trust. Additionally, close partnerships with these trusted brands help transfer some of that trust to AltitudeIQ.
 
AltitudeIQ does not rely on strategic alliances and faith in its technology. The company upends the established order — and reinforces quality and trust — through documentation, governance and reporting. Rather than generating documentation as a matter of course during software development and rollout, Luz explained, “You generate documentation just in time, fully updated when you need it … you go and generate because the agents can code and do the testing and everything for you. Now I need something for audit or something for validation. Now we generate the documents you need.” Are clients ready for this? Not yet, in Luz’s view, but they will come to appreciate it.

The disruptive impact of AltitudeIQ and companies like it

AltitudeIQ’s success could be hugely disruptive to the traditional consulting and IT services landscape. Drastically shortened timelines for implementation threaten system integrators’ traditional commercial models. Enterprises will rethink how they deploy and update their ERP systems and what is required for GRC, potentially reducing the need for large offshore staff dedicated to managed services around IT stacks.
 
When generative AI burst onto the scene, we read about (and maybe predicted a little bit ourselves) this kind of disruption to consulting and IT services, but adoption at scale by enterprise lagged well behind the hype. And IT services companies and consultancies continued to provide value through change management, GRC, and the management of increased complexity wrought by AI adoption. AltitudeIQ’s offerings, along with similar products in the market, significantly accelerate the time it takes to adopt and manage large ERP systems. However, this could create challenges for the very partners that AltitudeIQ currently collaborates with, at least in the short term. These companies and firms may need to adjust their own value propositions, technologies and capabilities in response.
 
The wild card remains change. Enterprise will likely be slow to fully adopt agents, or at least fully rely on them, for compliance and reporting. Typically, startups do not handle or consider change management, relying on consulting partners to guide clients, which can be another potential brake on disruptive adoption. But in the current AI age, even cultural and structural impediments to change seem to be overwhelmed by the need for speed. Yadgar noted, “I think for people that have got a problem today, the take-up is much faster. They go, ‘That’s my problem today. How can I use [AltitudeIQ’s software] tomorrow?’”
 
As AltitudeIQ scales, knowledge management will present another challenge. Currently, IT services companies and consultancies bring AltitudeIQ into their client meetings. If those opportunities scale quickly, will these consulting and SI partners be able to tell the AltitudeIQ story, from both a technology and a business perspective, without AltitudeIQ in the room?
 
White labeling and reselling make sense while AltitudeIQ remains in startup mode, but will GSIs and consultancies consistently consider AltitudeIQ IP, especially when other similar tech stacks are available? Alternatively, the AltitudeIQ IP may be so good that SAP or another tech giant might buy it, which could be the best path forward for a company operating in a paradoxical environment.
 
One more alternative path: small and midsize businesses. TBR has heard repeatedly in 2026 that GSIs and large consultancies have been investing in their SMB practices and offerings.
 
In TBR’s view, GSIs and global consultancies will need all the proven IP they can get to make it worthwhile for SMB clients to listen. Absent productization of services, the GSIs will not be able to meet SMB budgets. AltitudeIQ seems to have the right pricing and go-to-market model that will not interfere with GSIs’ SMB plans, should AltitudeIQ opt for hundreds of small clients rather than a dozen large ones? One thousand SMB clients using off-the-shelf AltitudeIQ IP? Or 10 Fortune 100 clients demanding customization, leading AltitudeIQ to expand into more services, against its strategic intentions? Knowledge management, client selection and selling out are all business-model challenges for AltitudeIQ, particularly as the company continues to grow.

Conclusion

TBR does not evaluate technology solutions; we evaluate businesses. While small relative to the companies TBR reports on quarterly, AltitudeIQ receives investment support from established technology investors with track records of building scaled businesses. TBR typically does not evaluate startups, but AltitudeIQ’s consulting heritage, relatively quick partnerships with leading IT services companies and consulting firms, and seamless fit within an exceptionally well-established technology environment (the vast SAP landscape) led TBR to take a closer look, and we will keep AltitudeIQ on our radar as 2026 winds down and return to hear more of their story in 2027.

Could Responsible AI Spark Transformative Change at HCLTech?

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In mid-August, TBR met with Heather Domin, HCLTech’s vice president and head of Office of Responsible AI and Governance, and Grace Davin, Responsible AI and Governance Thought Leadership and Enablement leader, to discuss HCLTech’s Responsible AI Advisory Services practice. The following reflects that conversation as well as TBR’s ongoing analysis of HCLTech, the IT services ecosystem, AI and disruptions across business models in the technology space.

 
HCLTech’s strengths lie in its engineering DNA; entrenched and well-managed client relationships; and extensive alliance partnerships, which include critical technology players and even HCLTech competitors. Playing to those strengths has been one element separating HCLTech from IT services peers in the last few years, especially with respect to profitability. With a suite of AI Advisory Services, the company continues to follow its proven strategy: focus on core values, deliver relentlessly, partner smartly, and expand within an existing client base.
 
Responsible AI, a critical subset of AI Advisory Services, could serve as a strategic enabler of HCLTech’s market leadership ambitions, elevating its market stature and strategic relevance with clients, peers and ecosystem partners.

Responsible AI: The basics

AI Advisory Services is aligned with HCLTech’s overall organization and strategy including partnerships across technology companies, industry organizations, nongovernmental organizations and governments. In Domin’s view, staying current with the fast-changing AI landscape requires partnering broadly as well as putting HCLTech through the rigors of leading AI, technology and organizational certifications. Domin noted HCLTech’s partnerships with Microsoft and IBM, as well as a growing collaboration with the Massachusetts Institute of Technology.
 
HCLTech’s core client value proposition combines Responsible AI governance with large-scale engineering and transformation delivery. Unlike niche governance or AI red-teaming providers, HCLTech’s AI advisory capabilities span AI red teaming, AI governance, enterprise strategy, agentic engineering, and road-mapping from prototype through production. The company brings technical talent, local regulatory knowledge, customization by industry and geography, and the ability to embed governance into major AI programs rather than treating it as a stand-alone compliance exercise.
 
According to Domin, HCLTech has seen increasing demand for AI advisory services from clients, particularly in financial services, healthcare, and energy & manufacturing, all areas that face significant regulatory challenges. HCLTech has developed expertise in addressing governance, risk and compliance (GRC) issues for clients in part by hiring talent from legacy GRC firms and by recruiting professionals with backgrounds combining legal, compliance and AI experience. In TBR’s view, HCLTech’s decision to explicitly call out Regulatory Services as a component of AI Advisory Services provides a subtle but significant differentiator when contrasting HCLTech with its closest IT services peers.
 
HCLTech bakes Responsible AI into every element in the AI Advisory Services suite, as well as across the broader set of HCLTech’s AI offerings, such as AI Force (see TBR’s assessment from late 2024). Domin acknowledged that Responsible AI should be at the forefront of any engagement with an AI component and not, like change management traditionally, left to the end of the engagement process.

Responsible AI: The nuance

When discussing whether HCLTech’s Responsible AI services offering allowed the company to interact with decision makers beyond the usual CIO clients, Domin noted that organizations have different approaches to “placing the responsible AI responsibilities … sometimes they put it in the technology office, sometimes in the CIO, sometimes it’s in the chief risk officer’s office, and so [CIOs will] bring in the right people.”
 
These changing roles and responsibilities help expand HCLTech’s influence and footprint within a client. Domin added that some “big companies that we work with already have pretty well-established [AI and tech] broad capabilities. What they need is to understand how to deploy a specific AI deployment with the right guardrails integrated. So it just depends on the company. We might not even need to go to that other buyer, but in some cases we do.” In TBR’s view, expanding HCLTech’s role by providing Responsible AI services will undoubtedly bolster client retention and help prove the company’s overall value.
 
In multiple client use cases presented by Domin, HCLTech’s AI Advisory Services practice essentially solved complex problems driven by a lack of responsible AI practices or guardrails and emerging technology deployments. For example, one client’s use of GitHub Copilot had returned underwhelming results and created unwanted risks, providing an opening to use nearly the full suite of HCLTech’s AI services. In TBR’s view, these brownfield opportunities will likely remain plentiful as hype around AI’s promise coupled with fears of being left behind outpace responsible approaches to AI deployments.
 
TBR noted Domin and HCLTech’s emphasis on governance, risk, and compliance, as well as the operational aspects of AI and not the specific technology itself (discussion touched on broader aspects beyond the common technology aspects such as which large language model to choose, vibe coding, retrieval-augmented generation, or token consumption). Every IT services company does tech. Not every company understands and delivers on broader governance and risk management.

What Responsible AI really means for HCLTech

Domin discussed HCLTech’s customer-zero story and how the Responsible AI team participated in the firm’s ongoing internal AI deployment, including ISO 42001 certification and an AI ethics board. She added that, “some of our experience around our own tooling, it’s been useful. Like where clients are deploying similar tools or things, we’ll say, ‘Oh yeah, but our team is doing that internally. Go talk to them.’ And so yes, [customer zero] is very much a core integral part of what we do.”
 
In TBR’s view, HCLTech’s customer-zero story around Responsible AI could be part of a catalyst for change within the company. Not only will the customer-zero story resonate with clients and with HCLTech’s technology and ecosystem partners, but employees will also be able to see the company’s value to clients in a different way.
 
Domin’s presentation and the discussion with TBR included frequent mentions of “board-level narratives” and deliverables from HCLTech ready for “audit committee review,” all wrapped around the Responsible AI and AI Advisory Services offerings. TBR had not previously considered that HCLTech’s value proposition includes board-level contributions, except for highly specific, technology-centric issues, such as large-scale IT implementations or business-model-changing AI deployments at scale.
 
Domin subtly suggested the company’s Responsible AI professionals would consult consistently at the board and audit committee levels, bringing HCLTech’s brand repeatedly into that environment in a different way.
 
Notably, Domin commented on a “cultural transformation of our teams” as part of the positive effects of internal AI adoption at HCLTech. Continued adoption and cultural transformation could help the company position itself as a different kind of partner to boards and C-Suites than other IT services companies. That shift would likely strengthen HCLTech’s standing as a strategic advisor to clients and ecosystem partners, creating opportunities for additional services engagements beyond Responsible AI.

Grant Thornton Expands Its Oracle Practice Through Leadership, Industry Expertise and AI

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TBR recently spoke with Brandon Johnson, Oracle Advisory Leader at Grant Thornton, about the firm’s growing Oracle practice, his experience prior to joining Grant Thornton, and his expectations for Oracle and Grant Thornton over the next few years. The following reflects that discussion as well as TBR’s ongoing analysis of both companies, their competitors and peers, and trends across the entire technology landscape.

 
TBR believes Grant Thornton has built a differentiated Oracle practice around senior leadership, direct field access, and focused industry investment, all centered on midmarket clients. Grant Thornton has Oracle breadth without bloat, connecting finance, human capital management (HCM) and supply chain with tax, risk, cyber and industry advisory while keeping experienced leaders close to the work.
 
Existing demand supports growth, and, notably, Oracle generates about half of Grant Thornton’s leads, while Grant Thornton creates the remaining pipeline internally, reflecting the depth of the two companies’ relationship. Scale remains the biggest challenge, with Johnson admitting the bench needs strategic growth and a better balance of offshore and nearshore talent.
 
Despite this constraint, TBR expects Grant Thornton’s annual Oracle practice revenue to grow in the high teens to low 20% in the near term, potentially higher if Grant Thornton institutionalizes more of a relationship-led practice through standardized delivery, a broader talent pipeline, and reusable AI offerings with defined governance and outcome measures.
 
In short, Grant Thornton recruited the right leadership and developed tight relationships, and now it must execute on Oracle’s platform changes plus client demand.

Leadership, industry focus and co-selling have deepened the Oracle alliance

Grant Thornton hired Johnson, a 25-plus-year Accenture veteran, where he had successfully built a substantial Oracle business and established strong relationships with Oracle’s sales, product and alliance teams. He has been joined by additional experienced professionals specializing in Oracle HCM, finance, supply chain and enterprise transformation.
 
In TBR’s assessment, Grant Thornton gained unusually broad and deep access to Oracle for a firm of its relative size, and its long-standing experience working with Oracle provides it with the discipline and confidence to treat Oracle’s consulting practice as a delivery partner rather than a competitor.
 
For decades, professional services firms, particularly the Big Four, have maintained strained — at best — relationships with the consulting arms of software giants such as SAP, Microsoft and Oracle. In contrast, according to Johnson and Grant Thornton Chief Marketing Officer David Clarke, Grant Thornton enjoys a more mature, cooperative understanding of different consulting opportunities and roles.
 
Grant Thornton, in their view, leads transformation and draws on Oracle for specialized infrastructure and product depth. Of course, the age-old challenge of converting personal access and relationships into consistent field enablement and referrals remains.
 
According to Johnson, Grant Thornton’s Oracle practice concentrates on financial services, healthcare, hospitality and travel, professional services, transportation and logistics, with new investments in energy, utilities, oil and gas, and consumer sectors.
 
TBR’s research has consistently shown that concentrated industry specialization, rather than serving all industries, yields faster, more sustained revenue growth. Technology vendors increasingly reward partners that combine industry talent, subindustry offerings and reusable IP; broad but shallow coverage carries less value than proven depth in a limited set of markets.
 
Like many consultancies, Grant Thornton creates demand for Oracle through a presales team that demonstrates the technology, joint go-to-market campaigns and introductions to existing Grant Thornton clients.
 
Not surprisingly, Oracle, according to Clarke, promotes Grant Thornton in partner forums. More surprising, Oracle generates about half of Grant Thornton’s Oracle pipeline, reinforcing a shift from simple referral flow to co-selling.
 
TBR believes a technology company’s ability to explain a consulting partner’s value and tell that consultancy’s story remains the most critical indicator of the current depth and long-term potential of a tech vendor-consultancy alliance. A tech vendor must be able to effectively communicate a consultancy’s story to generate significant pipeline, and clearly, Oracle is doing this well for Grant Thornton.
 
Further cementing the alliance, Grant Thornton’s relatively early participation in Oracle’s AI Agent Studio gives the firm a foothold in the transformation layer between applications and infrastructure, enabling the firm to use agents to connect data, automate processes and coordinate decisions across Oracle and non-Oracle systems.
 
Oracle’s Fusion Agentic Applications expand that opportunity rather than reduce services demand. Clients still need integration, controls, process redesign, data readiness and change management, and Grant Thornton should be able to tie each deployment to measurable gains in close cycles, workforce operations, procurement, service or supply chain.

Grant Thornton’s Oracle sweet spot: Complex midmarket transformations

Grant Thornton’s ideal clients share a few basic characteristics: large enough to have outgrown boutique consultancies and IT services companies; small enough to neither need nor want to pay the higher prices of a Big Four firm or global systems integrator; and experiencing rapid growth fueled by acquisitions, carve-outs and increased technology investments while contending with fragmented IT systems and business model disruptions.

How does Grant Thornton marry its client base and Oracle’s technology?

  • Integrated Fusion transformations: Grant Thornton positions HCM with finance and supply chain; it reported to TBR that roughly 70% of its Oracle pipeline includes HCM packaged into a broader bundle. Oracle’s common application and data architecture enables the firm to frame workforce, financial and operational change as a single program.
  • Private equity (PE) carve-outs and post-acquisition integrations: Grant Thornton combines PE relationships and preconfigured designs to establish finance, consolidation, HR and operational systems quickly, especially when a portfolio company must separate from a parent or prepare for another transaction. Undoubtedly, Grant Thornton brings its customer-zero experience to these opportunities.
  • Industry-specific transformations: In the healthcare, financial services, transportation, hospitality and energy verticals, Grant Thornton can combine Oracle with regulatory, tax, risk, cyber and operating expertise. Grant Thornton is not unique, but by staying focused on a handful of industries, the firm’s expertise sets it apart from its peers. TBR notes that even if the industry transformations are not unique among services companies, there is a level of differentiation among tech partners. In our opinion, Oracle is one of the very few SaaS vendors that offer fully prepackaged industry applications, giving firms like Grant Thornton opportunities to build around these applications, helping them lead with business discussions and work backward into horizontal finance and HR transformations.
  • AI-enabled finance and enterprise transformation: For Grant Thornton’s clients, Oracle agents can improve finance, HR, procurement, supply chain and service workflows, but only when Grant Thornton pairs the deployments with process redesign, governance, controls, data readiness and workforce change.
  • Alternatives to large integrators: According to Johnson and Clarke, Grant Thornton’s senior-level attention and practical delivery style appeal to buyers concerned about junior staffing, change orders or standardized playbooks. Sustaining that advantage may require Grant Thornton to make its capacity model as credible as its relationship model.

In TBR’s view, Grant Thornton will likely avoid infrastructure-heavy Oracle Cloud Infrastructure (OCI) programs, complex database modernization and very large global transformations until it builds deeper capabilities — though these may be necessary as OCI matures and continues to underpin the broader platform and applications portfolio.
 
OCI will also become more essential to the One Oracle positioning and sales strategy Oracle will increasingly emphasize with its partners. Until then, Grant Thornton can own the transformation agenda with its clients from the apps layer and continue developing its deep relationship with Oracle without overstating its full-stack depth.

Capacity, field awareness and technology depth will set the ceiling

Talent remains the most pressing constraint. With almost no Oracle bench, each large win for Grant Thornton creates staffing pressure, even as the firm continues building a talent base in India and recruiting top-flight nearshore talent as well as considers investing in Philippines-based capacity ahead of demand, all without weakening the firm’s promise to lead every delivery with senior, experienced professionals.
 
Compounding the talent shortage, Oracle field awareness of Grant Thornton’s strengths remains uneven. Executive access opens opportunities, but scale requires a formal enablement system that gives sellers concise guidance on target clients, industry plays, credentials, reference architectures, available capacity, and reasons to select Grant Thornton. The firm could also develop more depth across analytics, data and OCI.
 
Although becoming an infrastructure outsourcer would not fit Grant Thornton’s strategy or brand, strengthening OCI architecture, integration, security and data governance would prove more credible ownership of the transformation layer.
 
To further differentiate from peers, Grant Thornton could assign ownership, governance, version control and performance metrics to its own accelerators and link each asset to faster deployment or better client outcomes. Those metrics could also be a catalyst for disruption of the commercial model, should Grant Thornton and its clients together see benefits in fixed-fee and/or value-led commercial structures.
 
In TBR’s view, consulting commercial models have begun to evolve rapidly toward explicitly tying fees to adoption, productivity, transaction speed, working capital, workforce efficiency and/or financial close improvement. Grant Thornton’s Oracle practice could be an internal catalyst or accelerator for that change.

Productize, scale, prioritize, invest and attach

Leadership, client demand, alliance access and proven delivery competency give Grant Thornton the foundation to make its Oracle practice one of its fastest-growing technology businesses. With C-Suite-to-field-level relationships that run deeper than market awareness, the practice has room to take market share before larger competitors react.
 
Adding capacity, converting beta AI work into marketable solutions, proving industry plays, and expanding Oracle-originated opportunities could all help Grant Thornton when it comes to hiring, delivery, and field awareness.”
 
TBR believes Grant Thornton will likely accelerate its growth and increase its relevance to Oracle and shared clients by:

  • Productizing a small set of repeatable offerings in Oracle’s AI Agent Studio, including financial close, procurement, HR service delivery (HRSD), workforce planning and supply-chain, through ideally defined deployment methods and outcome metrics
  • Scaling through delivery pods, which will preserve senior client leadership while expanding offshore and nearshore leverage across the entire Oracle stack
  • Prioritizing sectors where Oracle commitment, Grant Thornton relationships and midmarket demand overlap, especially financial services, healthcare, energy, transportation and logistics, and hospitality
  • Investing in OCI architecture, Oracle Integration Cloud, data governance, security and cross-platform orchestration rather than commoditized infrastructure
  • Attaching optimization, managed services, agent governance and adoption support to implementations while providing recurring revenues

To bring the greatest value to its clients and to Oracle, Grant Thornton does not need scale parity with the largest Oracle partners — a point Johnson and Clarke made repeatedly to TBR. Instead, the firm will institutionalize its alliance access, focus its industry model and bring measurable results to its midmarket  clients. Capacity, repeatability and field execution will help Grant Thornton punch well above its weight.