Acquisitions, being attentive to clients’ bottom-line demands, and implementing AI into IP are backbone of CGI’s “built to grow and last” strategy

On June 5, CGI hosted its Industry Analyst Summit. CEO Francois Boulanger and CGI Board of Directors Executive Chair Julie Godin commenced the meeting, detailing how CGI’s business culture, proximity model and decentralized approach, acquisitions and cocreation with clients are key to the company’s growth strategy. Throughout each session CGI leaders highlighted the company’s emphasis on meeting client objectives, providing flexibility and codeveloping solutions as necessary. Cocreating on projects not only delivers more relevant solutions to the client but also provides CGI with new intellectual property (IP) that it can bring to other clients.

 

Notably, CGI is leaning into its proximity model by acquiring more companies that build out the company’s footprint in metro markets. This is particularly evident with the purchases of U.S.-based Daugherty and Novatec. Other acquisitions such as Momentum Technologies and Apside expand the company’s local presence in Canada. Access to more markets across the U.S., Canada and Europe, alongside new client-led solutions, is broadening the company’s opportunities, particularly around AI-related projects. CGI has also enhanced its data and AI capabilities through the strategic acquisitions of Apside, Novatec and Aeyon.

 

CGI shared insights into its recent AI endeavors, including an example involving the deployment of an air gap solution for the North Atlantic Treaty Organization (NATO). For this project, CGI collaborated with NATO’s Allied Command Transformation in Norfolk, Va., to construct and tune models that accelerate the classification, editing and analysis of documents using the knowledge agent AI Felix. Outside client-led solutions, CGI is embedding AI across its existing portfolio to enhance delivery to clients across industries.

As CGI remains largely unaffected by DOGE, enhancements across the company’s public sector portfolio allow it to dig deep on federal deals

Stephanie Mango, president of CGI Federal, led an hourlong panel discussion with executives representing CGI’s U.S. Federal, Canadian and European public sector operations. Globally, CGI’s various government clients are encountering similar challenges associated with rising levels of economic and geopolitical uncertainties and governmentwide changes. Common across the company’s roster of government customers is the enduring demand for IT modernization. CGI is currently helping governments transform outdated legacy IT infrastructures, prioritize digital transformation initiatives, address talent shortages in cybersecurity and AI, adopt zero-trust security architectures, implement sovereign AI and cloud solutions, enhance the security and resilience of government supply chains, and protect critical public sector infrastructure.

 

TBR believes having such a broad swath of activities provides CGI’s public sector practices globally with case studies and success stories to showcase when pursuing new opportunities, talent with relevant experience that can be redeployed to new government markets, and solutions codeveloped with government clients highly relevant to public sector agencies elsewhere. A common go-to-market approach CGI employs across all public sector markets is to help government IT departments and IT decision makers retain a modernization mindset as the company firmly believes governments must view digital transformation as a long-term, multiyear strategy. TBR believes CGI also effectively leverages its client proximity approach to codevelop solutions with government clients and to optimize its agility in responding to fast-changing market dynamics.

 

In the U.S. federal market, where the arrival of the Trump administration and its Department of Government Efficiency (DOGE) has caused sectorwide upheaval, TBR believes CGI Federal is well positioned to capture a growing share of digital modernization work that we expect to accelerate, after the initial shock of billions of dollars in budget cuts and reallocations. Although CGI was included on DOGE’s initial hit list of consultancies under scrutiny, CGI Federal only generates 2% of its sales from “discrete consulting services,” which TBR assumes is a reference to the type of management or strategic consulting services most vulnerable to DOGE.

 

CGI Federal generates over 50% of its revenue from outcome-focused engagements, which are typically structured as fixed-price contracts. According to TBR’s Federal IT Services research practice, federal IT contractors can expect a general shift from cost-plus to fixed-price arrangements as agencies adopt a more outcome-focused mindset regarding new IT outlays. When the federal IT procurement environment begins focusing more on outcome-based contracting, it will shift more risk of cost-overruns or delivery delays to the vendors — a potentially margin-erosive scenario for federal system integrators (FSIs) that fail to maintain strong program execution.

 

CGI Federal is confident it can adapt to outcome-focused contracting in federal IT but is uncertain how quickly the transition can be completed. CGI Federal has been a perennial margin leader in TBR’s Federal IT Services Benchmark due to its traction with its ever-expanding suite of homespun IP-based offerings like Sunflower and Momentum, and demand for these offerings will at least endure, but likely increase, under DOGE.

 

TBR anticipates additional opportunities for CGI Federal will stem from its proprietary Sunflower (cloud-based asset management) and Momentum (financial management) solutions, as improving asset and financial management are among DOGE’s chief objectives and are in high demand by civilian and defense agencies looking to enhance fiscal and supply chain management, especially to comply with DOGE-related mandates.

 

CGI Federal has ongoing engagements that the company will showcase to win future federal work. For example, CGI Federal is implementing a cybersecurity shared services platform for the Department of Homeland Security (DHS), while the Department of Transportation’s use of the Momentum platform will serve as the case study for similar engagements across the federal civilian market.

 

In the Department of Defense, CGI Federal expects to leverage its fiscal and asset management offerings to capitalize on the recent mandate from Secretary of Defense Pete Hegseth that all U.S. service branches pass financial audits, and the company will cite its recent success on the U.S. Marine Corps Platform Integration Center (MCPIC) engagement to illustrate the full range of its capabilities. In the U.S. state government market, CGI leaders also mentioned that an unnamed state government had established its own version of DOGE with similar efficiency objectives and noted that other states are likely to follow suit, creating an expanding addressable market for the company’s asset and fiscal management platforms to prevent fraud, waste and abuse and to maximize operational transparency.

 

CGI Federal’s 2024 acquisition of Aeyon added process automation and AI capabilities that TBR believes will have high relevance for not only U.S. federal agencies but also state governments. The company also provides low-cost onshore managed services in the U.S. from delivery centers in Lebanon, Va., and Lafayette, La., staffed by 2,000 CGI professionals. Low-cost onshore delivery is also common across CGI’s public sector operations in Canada, but less so in Europe.

 

TBR believes CGI’s alliances with cloud hyperscalers (Amazon Web Services, Google and Microsoft), platform providers (Salesforce, SAP and ServiceNow) and others (UiPath, TrackLight and NetApp) will be key to its future success in not only the U.S. federal market but also public sector markets globally. These partners are also enablers of the company’s IP-focused solution strategy — as important as client-partners in developing new technologies and solutions.

 

CGI does not believe the advent of generative AI (GenAI) marks the beginning of the end for traditional IT services. Rather, CGI intends to leverage its expanding GenAI capabilities to migrate its public sector portfolio of offerings away from lower-value services and embrace higher-value offerings designed to maximize the value and potential of GenAI for public sector agencies. Higher-value services will require CGI to lean more heavily into its well-established proximity model as clients may need more guidance to fully reap the benefits of new offerings as capabilities become increasingly complex. In the long term, CGI may turn to more offshore resources as clients demand greater support.

BJSS provides short-run revenue relief but demonstrating AI competency to clients will be key

Vijay Srinivasan, president of U.S. Commercial and State Government operations, led the session on CGI’s banking segment by highlighting the company’s dedication to servicing clients long-term and holistically, providing flexibility to address clients’ objectives rather than focusing only on selling financial services solutions. Following opening remarks, CGI discussed recent trends and concerns in the sector supported by annual interviews of business and IT executives. First, as many banks strive for increased personalization, they demand AI and real-time capabilities on mobile applications. Unsurprisingly, banking clients are facing challenges deciphering market expectations, given the unpredictable nature of ongoing tariffs. In turn, banking clients are looking for new ways to generate revenue and maintain profitability. The banking industry, alongside many others, is experiencing a deterioration of institutional knowledge with retirements, fueling demand for AI tools.

 

As banks demand more AI tools and other new technologies, they need to modernize legacy IT systems, migration support, and application modernization. These modernization efforts also help banks execute on their cost-cutting initiatives. During the second half of 2024, CGI modernized a U.S. financial services company’s loan origination system with CGI Credit Studio and implemented its Trade360 platform for Bladex.

 

Despite the recent interest rate cuts made by the European Central Bank (four reductions thus far in 2025) and by the U.S. Federal Reserve (three reductions in 2H24), ongoing uncertainty is driving the need for streamlined processes enabling cost efficiency. CGI shared an example where the company supported a Canadian bank to optimize over 110 core applications, many of which were running on legacy systems. CGI was able to reduce the bank’s run costs by more than 35% year-to-year. Although the transformation began eight years ago, CGI has a long-standing relationship with the client, and the deal serves as a blueprint for similar contracts in the industry. Leading digital transformation efforts that support bottom-line initiatives is particularly important in the current environment.

 

Although the financial services sector is experiencing ongoing volatility, the sector and the manufacturing, retail and distribution sector are roughly equal contributors to CGI’s overall revenue. Finding new revenue opportunities and honing strategy within the segment will be vital to sustaining growth. Many IT services companies, including CGI, experienced revenue decline in their financial services sector in 2024, CGI’s financial services revenue declined in 1Q24, 2Q24 and 3Q24 before increasing by low-single digits in 4Q24.

 

In January 2025 CGI completed the acquisition of BJSS, a U.K.-based engineering and technology consultancy with industry expertise in financial services. The acquisition contributed to 8.6% year-to-year growth in the sector. CGI is not the only company prioritizing acquisitions that boost struggling verticals. Accenture recently purchased U.K.-based Altus Consulting, which will improve digital transformation capabilities in the financial services and insurance industries. Accenture experienced similar segment revenue growth declines as CGI in the first half of 2024. TBR believes BJSS will have a meaningful impact on revenue in the short run, but CGI may need to be more persistent with adding new solutions. Although introducing AI capabilities enhances client experience, it may not signal AI competency in the same way as new solutions. CGI may benefit from using acquisitions and more portfolio investments, similar to its investments in the public sector, to foster organic growth.

 

TBR believes CGI’s coinnovation with clients will create new opportunities tailored to industry needs; however, at the same time, other vendors in the past year have been leveraging partnerships to expand market share and provide industry-specific solutions. For example, Cognizant and ServiceNow expanded their partnership to reach midmarket banking clients, and Accenture is collaborating with S&P Global to jointly pursue financial services clients. Joint offerings may motivate clients to invest in these solutions rather than only implementing AI into existing solutions.

 

CGI illustrated how BJSS adds value to the company’s capabilities in its banking vertical in the U.K., specifically related to end-to-end services and product deployment. BJSS’ emphasis on meeting client goals made it a strong cultural fit for CGI. The acquisition came six months after the purchase of Celero’s Canadian credit union servicing business, which deepened CGI’s reach in Canada. These recent acquisitions, alongside recent interest rate cuts and continued additions of AI capabilities in banking solutions, position CGI well for strong performance in the sector. Further, in the company’s most recent earnings call, Boulanger announced the company is seeing “early signs in quarter two of renewed client spending in the banking sector.”

Adaptability with manufacturing clients provides deal opportunities even in a challenging environment

After the public sector, the financial services and the manufacturing, retail and distribution sectors are CGI’s next-largest revenue contributors, according to company-reported data. Similar to financial services, revenue growth in the manufacturing, retail and distribution sector was also volatile throughout 2024. Manufacturing clients will continue to experience a challenging environment with tariffs also contributing to uncertainty. During the industry session, CGI leaders discussed the increased importance of supply chain resiliency, stating that clients are seeking alignment with the company’s talent, data and technology. Investments to improve resiliency, such as in data-sharing ecosystems and capacity management, will be vital for clients, serving as a revenue opportunity for CGI.

 

To capture more revenue opportunities, CGI is completing acquisitions that bolster its standing in manufacturing, similar to recent purchases made to expand in financial services. Although CGI leaders did not directly discuss it during the session, the company’s recent purchase of Novatec expands CGI’s reach into the manufacturing sector in Germany and Spain, specifically in the automotive industry. The acquisition brings capabilities in digital strategy, digital product development and cloud-based solutions, which will help CGI with growing demand for supply chain resiliency. Further, manufacturing sectors are beginning to experience the effects of knowledge loss associated with large numbers of retirements. Increased automation in the sector will help close gaps. CGI is investing in implementing AI across its manufacturing portfolio, as well as the entirety of the business.

 

CGI is finding its clients are at different places in their digital journey, and the divide is only increasing. To address this gap, CGI will need to lean into its adaptable nature to meet each client’s needs. CGI included two examples to demonstrate the company’s approach. CGI highlighted a key deal with the Volkswagen Group around digitization, where the two companies jointly created a governance model and collaborated on Agile DevOps. The two formed a new entity, known as MARV1N, a unit that will provide the group with the necessary development support for digitalization projects. Additionally, CGI modernized the group’s legacy systems while developing new IT systems designed to cut operation expenses. The example from the session emphasizes one of CGI’s main themes of the summit: helping clients holistically, specifically around providing meaningful outcomes that improve clients’ bottom line.

 

Similarly, CGI developed and is managing Michelin’s supply chain and planning production manufacturing, underpinning the client’s Customer Experience and Services & Solutions focus areas. CGI was able to increase supply chain resiliency by enabling inventory prediction and implementing AI and business performance monitoring. CGI also supported Michelin with a machine learning project. The collaboration reflects CGI’s mission to increase automation to improve productivity and enhance supply chain resiliency. In contrast, the demand for AI comes mainly from intrigue in manufacturing, rather than leveraging it to boost productivity. TBR believes it will become important for CGI to signal to manufacturing clients how AI tools can help boost productivity, which is what CGI did recently in an engagement with Rio Tinto. CGI deployed AI tools to help Rio Tinto reduce production breakdowns, helping the client capture additional revenue.

As CGI’s proximity model and ideology provide longevity, investing in the right next-generation technology will position the company competitively

CGI’s focus on cocreation, infusing AI into its IP, and recent acquisitions has fueled revenue growth that is currently outpacing most other IT service vendors, largely due to its robust acquisition pace that is surpassing that of its peers, many of which are prioritizing smaller acquisitions with specialized capabilities. Additionally, as concerns of a recession rise, other IT service companies are turning their attention to startups. For example, Accenture has been ramping up its investments in startups, recently investing in AI prediction company Aaru and Voltron Data, which has GPU-powered data processing capabilities.

 

Similarly, Capgemini is collaborating with ISAI, a France-based tech entrepreneurs’ fund, launching ISAI Cap Venture II centered on investing in B2B startups. Although CGI made brief mentions of its startup framework, CGI Unicorn Academy, and discussed its AI-powered service delivery approach, CGI DigiOps, the company has not made many public announcements about investments or new initiatives. Capturing new technology, especially amid economic uncertainty, could help CGI secure a competitive edge on future revenue opportunities.

 

In CGI’s FY2025, which ends in September, CGI is likely to maintain its M&A pace. Although acquisitions may help CGI gain an edge over its peers, especially if the targets are well aligned culturally and able to significantly widen CGI’s reach into metro markets, the accelerated revenue growth will need to be coupled with AI investments that signal productivity improvements to maintain momentum in the long term so that CGI can attract more clients based on its innovation capabilities. However, TBR anticipates CGI will continue to expand revenue faster than its peers during its fiscal year, likely growing 5.0% in Canadian dollars and 1.2% in U.S. dollars. Nevertheless, the event reinforced CGI’s reputable strength: forming strong, in-depth, long-term relationships with its clients.

In late May, Red Hat welcomed thousands of developers, IT decision makers and partners to its annual Red Hat Summit at the Boston Convention and Exhibition Center (BCEC). Like the rest of the market, Red Hat has pivoted around AI inferencing, and this conference marked the company’s entry into the market with the productization of vLLM, the open-source project that has been shaping AI model execution over the past two years. Though Red Hat’s push into AI inferencing does not necessarily suggest a deemphasis on model alignment use cases (e.g., fine-tuning, distillation), which was the company’s big strategic focus last year, it is a recognition that AI inferencing is a production environment and that the process of running models to generate responses is where the business value lies. Red Hat’s ability to embed open-source innovation within its products and lower the cost per model token presents a sizable opportunity. Interestingly, Red Hat’s prospects are also evolving in more traditional markets. For instance, Red Hat’s virtualization customer base has tripled over the past year, with virtualization emerging as a strategic driver throughout the company’s broader business, including for communication service providers (CSPs) adopting virtualized RAN and within other domains such as their IT stacks and the mobile core.

Red Hat pivots around AI inferencing

Rooted in Linux, the basis of OpenShift, Red Hat has always had a unique ability to resolution assets to expand into new markets and use cases. Of course, AI is the most relevant example, and two years ago, Red Hat formally entered the market with Red Hat Enterprise Linux (RHEL) AI — the tool Red Hat uses to engage AI developers — and OpenShift AI, for model lifecycle management and MLOps (machine learning operations) at scale. These assets have made up the Red Hat AI platform, but at the Red Hat Summit, the company introduced a third component with AI Inference Server, in addition to new partnerships and integrations further designed to make agentic AI and inferencing realities within the enterprise.

 

AI and generative AI (GenAI) are rapidly evolving, but the associated core challenges and adoption barriers, including the high cost of AI models and the sometimes arduous nature of providing business context, remain largely unchanged. Between IBM’s small language models (SLMs) and Red Hat’s focus on reducing alignment complexity, both companies have crafted a strategy focused on addressing these challenges; they aim not to develop the next big AI algorithm, but rather to serve tangible enterprise use cases in both the cloud and the data center.

 

Everyone is aware of Red Hat’s track record of delivering enterprise-grade open-source innovation, and if Red Hat’s disruption with Linux over two decades ago is any indication, the company is well positioned to make real, cost-effective solutions for the enterprise based on reasoning models and AI inferencing.

Red Hat productizes vLLM to mark entry into AI inferencing

Though perhaps lesser known, most large language models (LLMs) today are leveraging vLLM, an upstream open-source project boasting roughly half a million downloads in any given week. At its core, vLLM is an inference server that helps address “inference-time scaling,” or the budding notion that the longer the model runs or “thinks,” the better the result will be. Of course, the challenge with this approach is the cost of running the model for a longer period of time, but vLLM’s single-server architecture is designed to optimize GPU utilization, ultimately reducing the cost per token of the AI model. Various industry leaders — namely NVIDIA, despite having its own AI model serving stack; Google; and Neural Magic, which Red Hat acquired earlier this year — are leading contributors to the project.

 

Leveraging its rich history of turning open-source projects into enterprise products, Red Hat launched AI Inference Server, based on vLLM, marking Red Hat’s first offering from the Neural Magic acquisition. AI Inference Server is included with both RHEL AI and OpenShift AI but can also run as its own stand-alone server. Though perhaps inclined to emphasize IBM’s watsonx models, Red Hat is extending its values of flexibility, choice and meeting customers where they are to AI Inference Server. This new offering supports accelerators outside IBM, including NVIDIA, AMD, Intel, Amazon Web Services (AWS) and Google Cloud, and offers Day 0 support for a range of LLMs. This means that as soon as a new model is released, Red Hat works with the provider to optimize the model for vLLM and validate it on Red Hat’s platform.

 

Building on vLLM’s early success, Red Hat launched LLM-d, a new open-source project, announced at the Red Hat Summit. LLM-d transcends vLLM’s single-server architecture, allowing inference to run in a distributed manner, further reducing the cost per token. Due to the cost, most will agree that inferencing will necessitate distributed infrastructure, and there are several recent examples across the tech landscape that have alluded to this. LLM-d is being launched with support from many of vLLM’s same contributors, including NVIDIA and Google (LLM-d runs on both GPUs and TPUs [tensor processing units]).

Partnership with Meta around MCP is all about empowering developers and making agentic AI enterprise-ready

If Google’s launch of A2A (Agent2Agent) protocol is any indication, Anthropic’s Model Context Protocol (MCP), which aims to standardize how LLMs discern context, is gaining traction. At the Red Hat Summit, Red Hat committed to MCP by announcing it will deliver Meta’s Llama Stack, integrated with MCP, in OpenShift AI and RHEL AI.

 

To be clear, Red Hat supports a range of models, but Meta went the open-source route early on, bringing Llama Stack, an open-source framework for building specifically on Llama models, into the Red Hat environment. This not only exposes Red Hat to another ecosystem but also provides APIs around it. Enlisting Meta at the API layer is an important aspect of this solution, as it enables customers to consume the solution and build new agentic applications with MCP playing a key role in contextualizing those applications within the AI enterprise. It is still early days for MCP, and making the protocol truly relevant in enterprise use cases will take some time and advancement in security and governance. But Red Hat indirectly supporting MCP within its products signals the framework’s potential and Red Hat’s role in bringing it to the enterprise.

Who would have thought we would be discussing virtualization in 2025?

In 2025 and the world of AI, you don’t often hear of a company putting virtualization at the top of its strategic imperatives list. However, everyone has seen how Broadcom’s takeover of VMware has caused a ripple in the market, with customers seeking cheaper, more flexible alternatives that will not disrupt their current cloud transformation journeys. In fact, when we surveyed enterprise IT decision makers, 42% of respondents indicated they still intend to use VMware, but most plan to do so in a reduced capacity. Of those planning to continue using VMware, a notable 83% are still evaluating other options*.

 

“Options both have increased the prices across the board, 20% to 30%, which is pretty significant. So, you could say myself and my peers are not very happy with the Broadcom method on that, and we’re looking at, you know, definitely options to migrate off VMware when possible. We’re definitely looking at Citrix, and then options from Red Hat and Microsoft.” — CTO Portfolio Manager, Consumer Packaged Goods

 

As a reminder, after Red Hat revolutionized Linux in the early 2000s, the company’s next big endeavor was virtualization. With the rise of cloud-native architectures, Red Hat quickly pivoted around containers, and this is where the company remains most relevant today. However, through the KVM (kernel-based virtual machine) hypervisor, which would eventually be integrated with OpenShift, virtualization has always been a part of the portfolio. Over the past year, given the opportunity surrounding the VMware customer base, Red Hat has actively revisited its virtualization roots in a few primary ways.

 

First, given the risky nature of switching virtualization platforms, Red Hat crafted a portfolio of high-touch services around OpenShift Virtualization, including Migration Factory and a fixed-price offering called Virtualization Migration Assessment. These services from Red Hat Consulting, which are offered in close alignment with global systems integrator (GSI) partners, help customers migrate virtual machines (VMs) as quickly as possible while minimizing risk, which largely stems from helping customers migrate VMs before modernizing them.

 

Secondly, Red Hat has focused on increasing public cloud support. Red Hat announced at the summit that OpenShift Virtualization is now available on Microsoft Azure, Google Cloud and Oracle Cloud Infrastructure (OCI), in addition to previously announced support for IBM Cloud and AWS, officially making the platform available on all major public clouds. Making OpenShift Virtualization applicable across the entire cloud ecosystem reinforces how serious Red Hat is about capturing these virtualization opportunities. These integrations will make it easier for customers to use their existing cloud spend commitments to offload VMware workloads to any cloud of their choice while maintaining the same cloud-native experience they are used to.

 

Of course, there will always be a level of overlap between Red Hat and the hyperscalers, but ultimately the hyperscalers recognize Red Hat’s role in addressing the hybrid reality and enterprises’ need to move workloads consistently across clouds and within data centers, and they welcome a more feature-rich platform like OpenShift that will spin the meter on their infrastructure.

With virtualization, Red Hat is allowing partners to sell infrastructure modernization and AI as part of the same story

At the conference, we heard from established Red Hat customers that have extended their Linux and container investments to virtualization. Examples included Ford and Emirates NBD, which has over 37,000 containers in production and is now migrating 9,000 VMs to Red Hat OpenShift Virtualization for a more consistent tech stack. Based on our conversations with customers, these scenarios — where VMs and containers run side by side — are not an easy sell and require a level of buy-in across the organization.

 

That said, if customers can overcome some of these change management hurdles, this side-by-side approach can offer numerous benefits, largely by creating greater consistency between legacy and cloud-native applications without significant refactoring. Though some GSIs may be better suited to the infrastructure layer than others, partners should recognize the opportunity to use OpenShift Virtualization to have client discussions around broader AI transformations. One of the compelling aspects of Red Hat is that even as it progressed through different phases — Linux, virtualization, containers and now AI — the hybrid platform foundation has remained unchanged. If customers can modernize their infrastructure on the same platform, introducing AI models via OpenShift AI becomes much more compelling.

Virtualization remains a key driver of telecom operator uptake of Red Hat solutions, but AI presents a significant upsell opportunity

Over the past few years, Red Hat has leveraged its virtualization technology in the CSP market, making significant progress in landing new CSP accounts and expanding its account share within this unique vertical. The company’s growth in this market has been aided by factors such as Broadcom’s acquisition of VMware, which initially caused a wave of CSPs to migrate to Red Hat due to the uncertainty surrounding VMware’s portfolio road map. Broadcom’s price hikes are causing a second wave of switching that TBR anticipates will continue for several years.

 

However, Red Hat has also succeeded in more deeply penetrating the telecom vertical due to its savvy marketing, which at times emphasizes that its solutions are “carrier-grade,” along with persistent efforts to raise awareness within the CIO and CTO organizations of CSPs that virtualization and hybrid multicloud strategies will have significant ROI for CSPs. This has led to strong adoption of Red Hat OpenStack and OpenShift, although the Ansible automation platform has lagged in terms of CSP adoption, as this customer segment prefers to use the free, open-source version of Ansible.

 

As CSPs iterate on their AI strategies, Red Hat has the opportunity to play a significant role, including with its new Red Hat Inference Server, as CSPs increasingly embrace edge compute investments. CSPs need to invest upfront to capitalize on the cost efficiency and revenue generation opportunities offered by AI, and Red Hat can help guide them in this direction. CSPs have difficulty moving quickly when new, disruptive technologies emerge, and, with AI specifically, have trouble evaluating and testing AI models themselves due to a lack of in-house expertise. Additionally, they feel constrained by regulations and are concerned about compromising data privacy. Red Hat’s dedicated telecom vertical services can help alleviate these concerns and accelerate CSPs’ investments in AI infrastructure.

Final thoughts

Based on our best estimate, roughly 85% of AI’s current use is focused on training and only 15% on inferencing, but the inverse could be true in the not-too-distant future. Not only that, but AI inferencing will likely occur at distributed locations for the purposes of latency and scale — which, due to its hybrid platform and ability to help customers “write once, deploy anywhere,” remains core to Red Hat’s value proposition. That is one of the compelling aspects of a platform-first approach; even as new components such as AI models are introduced, the core foundation remains unchanged.

 

Though all of Red Hat’s new innovations, including AI Inference Server and the LLM-d project, do not necessarily suggest a deemphasis on model alignment with assets like InstructLab, it is clear Red Hat is pivoting to address the inference opportunity. With its trusted experience productizing open-source innovation and its ability to exist within a broad technology ecosystem of hyperscalers, OEMs and chip providers, Red Hat is in a somewhat unique position to help transition AI inference from an ideal to an enterprise reality.

 

Further, Red Hat’s virtualization prospects are growing, as TBR’s interactions with customers continue to indicate that they are looking for new alternatives. If the hyperscalers’ recent earnings reports are any indication, the GenAI hype is waning, and we suspect many enterprises will refocus on infrastructure modernization to ultimately move beyond basic chatbots and lay the groundwork for the more strategic applications that inferencing will enable. It will be interesting to see how Red Hat capitalizes on new virtualization opportunities with its hyperscaler and services partners as part of a joint effort to bring customers to a modern platform, where VMs and containers can coexist and drive discussions around AI.

 

*From TBR’s 2H25 IT Infrastructure Customer Research

Connect, grow, deliver

TBR spent two days in Atlanta, listening to and speaking with Sage’s management team as part of the company’s annual Analyst Summit, and we walked away impressed. This is a company that knows itself and its strengths. It knows where it needs to improve. It knows where the pain points and constraints are, and has always done a good job of navigating between the two.

 

Most importantly, the company knows its customers, which should come as no surprise considering how long Sage has been serving its SMB install base. Sage has leveraged these strengths and established a large, sticky install base from which to pursue opportunities adjacent to its core business.

 

Sage is focused on three interlocking areas — connect, grow, deliver — which President Dan Miller described during the event:

• Connect through trusted partner networks
• Grow by winning new logos through a verticalized suite motion
• Deliver real, measurable productivity using AI

Each pillar represents a separate part of the company’s go-to-market strategy, but Grow stands out as the most vital to the company’s growth trajectory. Landing and expanding with new logos is the company’s greatest source of revenue growth, with vertical-specific and business operations solutions offering some of the greatest upsell potential. Aligned with this strategy, the company is a disciplined but active acquirer, onboarding new IP to enhance these sales motions.

 

Long-term, AI presents opportunities for the company to upsell into its finance and accounting (F&A) core. As Sage leans into its strengths while building for the future, its ability to scale AI and industry depth across a known and trusted customer base may prove to be the company’s most valuable asset.

Landing with F&A, then expanding with payroll, HR and operations management

Sage’s land-and-expand strategy starts with a stronghold in finance and builds outward through operational adjacencies. Most customers enter through core accounting —typically via Intacct — and expand into areas like payroll, HR, and inventory or distribution management as their needs mature. Vertical-specific modules are critical to this motion, especially in midmarket industries where Sage can tailor functionality to operational nuances.

 

The company reinforces expansion by packaging these capabilities into suites, streamlining procurement and positioning itself as more than just a financial system. Sales teams are trained to identify expansion triggers early; signs like API adoption, workflow customization or manual process bottlenecks often indicate opportunities. Although the company’s product maturity varies across the portfolio, Sage has seen success in service- and product-centric verticals, enabling the company to upsell and cross-sell. This approach, combined with a focus on ease of integration and strong partner involvement, is helping Sage grow account value without overpromising in its product road map.

AI at Sage: Workflow-first, ROI-driven

Sage management spent much time discussing its ambitions in AI. From TBR’s perspective, the tone was very grounded. Although the company will never be at the cutting edge of AI innovation, management did a great job of articulating the current opportunities to upsell AI capabilities. Finance and accounting workflows offer many sales opportunities for Sage to pursue, and the company is investing in R&D to capitalize on them. Similar to many of its application peers, Sage intends to approach agentic AI and generative AI development on a use-case-by-use-case basis. In Sage’s case, this is even more prudent as SMB customers face greater budgetary restrictions and require ROI to be realized in the first year.

 

Sage management highlighted AP automation, time-saving prompts and variance analysis as key areas where the company is achieving success with AI-powered automation. Like several peers, the company’s Copilot solution serves as the unified user interface (UI) for engaging embedded AI tools. Long-term, management expects to see this UI become more adaptive, guiding the user through an automated workflow. Guided prompting was another area of focus, and the company is building a library of prompts for end users to leverage as they perform specific tasks. Under the hood, the company intends to run its AI tools on internally trained models built on top of a third-party. CTO Aaron Harris discussed two of these tools: Sage Accounting LLM and APDoc2Vec.

 

As a reminder, Sage partnered with Amazon Web Services (AWS) over a year ago to collaborate on F&A models, and management highlighted the continued effort to build a new multitenant, dependency-based stack.

 

Long-term, TBR expects this work to be pivotal in reducing the cost of running AI workloads, while internally developed models with lower parameter counts than big-name large language models (LLMs) will further enhance cost efficiency at inference. Meanwhile, Sage is still figuring out how to monetize AI, but the industry default is to implement a tiered system. Some high-compute copilots may eventually carry usage-based fees, especially in forecasting, but for now, the priority is to show clear value and price accordingly.

 

In 2025 no conversation is complete without recognizing the platform implications of agentic workflows. Behind the scenes, Sage is preparing for an agent-first architecture by integrating emerging frameworks, such as Model Context Protocol (MCP) or Agent 2 Agent (A2A), directly into its platforms. The long-term goal is to coordinate these through super agents and plug into the broader agent ecosystem (Salesforce, Microsoft, Google), but this is still only part of the long-term road map.

 

That said, the company is building for the future, with an emphasis on data model consistency, dependency-based deployment, and orchestration layers capable of managing multi-agent chains. This is all being done with AWS in the background, keeping the platform anchored at the infrastructure layer.

Sage deepens its partner relationships

Sage’s partner and go-to-market strategy is built for focus and leverage. The company cannot cover every vertical or service need on its own, so partners are central to how it sells, delivers and scales. The revamped Sage Partner Network is tighter, with clear roles across sell, build and serve motions, and expectations tied to growth, not just activity. Multiyear vertical plans, coinvestment and execution discipline are now baseline requirements.

 

Internally, the GTM engine runs through SIGMA, which ties product planning to what the direct and partner channels sell. Sales teams are trained to package suites, identify expansion triggers, and position the platform by vertical need, rather than a feature checklist. To prepare for the platform’s evolution, Sage is already laying the groundwork for a more extensible ecosystem, including plans for an agent marketplace that would give partners a direct path into the next wave of product delivery.

Staying the course and preparing for what lies ahead

Sage’s story at its annual Analyst Summit was not necessarily one of reinvention. Land and expand has been the company’s strategy for years, and it has worked well so far. By anchoring in finance, expanding through vertical suites and operation management, and keeping partners close to the motion, Sage is executing with clarity around who it serves and how it wins. Meanwhile, the platform is evolving, AI is taking shape, and the architecture is catching up to the ambition. None of the company’s claims felt like overpromising.

 

In a market filled with transformation stories, Sage is running a disciplined play. The question is whether it can maintain that discipline as it scales and converts its product investments, especially in AI and agentic workflows, into tangible value for the customers it already knows best.

From May 20 to 21, 2026, in Dallas, Hitachi Digital Services hosted close to 100 analysts and advisers for a two-day summit featuring Hitachi leaders, clients and technology partners. The following reflects TBR’s observations from the event, informal conversations with Hitachi leaders and clients, and TBR’s ongoing analysis of the overall IT services and professional services space.

Hitachi Digital Services perfectly positioned to take advantage of IT-OT convergence and AI adoption

Two tectonic technology shifts happening now and opening massive revenue opportunities play straight into Hitachi’s strengths, positioning Hitachi Digital Services for significant growth over the next five years. With IT and OT convergence accelerating and enterprises increasingly adopting AI at scale, Hitachi Digital Services can rely on Hitachi’s engineering DNA, deep expertise in specific industries, and firsthand experience in manufacturing as the foundation for a build-integrate-operate value proposition. The analysts and advisers event highlighted how well Hitachi Digital Services brings the entire Hitachi company to its clients, remains rooted in Hitachi’s engineering approach to everything, and maintains its focus on strategic priorities, even as the market shifts all around them.

 

Although smaller than the typical IT and professional services firms covered by TBR, Hitachi Digital Services warrants close attention in the coming years. Its distinctive business model, strategic direction and performance — particularly its combination of IT, OT, AI and domain expertise — position Hitachi Digital Services as a potential outlier in the IT services landscape.

Hitachi’s broader capabilities and assets help Hitachi Digital Services deliver unique combination of IT, OT and AI

Though only a two-day event, Hitachi included an impressive breadth of client speakers and panel participants, all from Hitachi Digital Services’ core industries, while also bringing technology partners into the discussion, occasionally simultaneously. One major telecom player described their relationship with Hitachi Digital Services as a client and with Hitachi overall as a go-to-market technology partner. An automotive client praised Hitachi Digital Services’ ability to “make our problems their problems to solve” and called out Hitachi’s “asset knowledge,” which blended IT capabilities with OT experience.

 

During onstage presentations and informal discussions with TBR, clients noted the importance of Hitachi Digital Services’ ability to seamlessly tap into the larger Hitachi organization and bring deeply technical engineering expertise to bear. Not surprisingly, clients attending the event also noted their well-established relationships with Hitachi, reinforcing Hitachi Digital Services’ messages around sustained focus on clients’ evolving issues. TBR’s assessment of Hitachi Digital Services’ client service value proposition and technology partner alliance strategy could be best summed up by a comment from a cloud vendor on stage with Hitachi Digital Services: “showing up together with Hitachi, delivering results, day after day.”

 

On Day 1 of the summit, a senior Hitachi Digital Services executive said the company maintained an “R&D and engineering first, consulting second mindset,” a sentiment echoed throughout the event. Another executive noted that innovation “starts with Hitachi engineering R&D.” Discussions around IT-OT convergence and Industry 5.0 included elements of consulting but remained rooted in engineering. For example, when discussing IT and OT convergence, Hitachi leaders described the company’s state-of-the-art railcar manufacturing plant in Maryland, a real, physical, operational test bed for digital innovations and Hitachi Digital Services’ IT + OT + AI value proposition. For Industry 5.0, Hitachi executives discussed Hitachi Digital Services’ overall AI strategy and noted the confluence of human and AI collaboration aided by AI agents.

 

In TBR’s view, the consistent undercurrent of engineering and R&D separates Hitachi Digital Services from peers in the IT services and consulting space, in part because of the credibility behind Hitachi’s “engineering first” assertion. Any company can say it has engineering capabilities; Hitachi builds railcars.

Domain expertise and organizational intelligence needed for broader AI adoption and agentic AI

Regarding AI, leaders at Hitachi Digital Services emphasized that effective AI begins with deep knowledge. Hitachi brings a strong foundation in business, operations, design, manufacturing and maintenance — an extensive base of expertise that it leverages to power its AI initiatives. In short, Hitachi Digital Services professionals argued they have more robust intelligence to feed into AI models and algorithms than most peers. Hitachi leaders also highlighted the company’s focus on organizational intelligence and its long-standing capabilities around “capturing the intelligence of complex organizations.”

 

In TBR’s view, Hitachi’s only complex organization likely provides a useful starting point for understanding the applicability of AI in organizational intelligence. Regarding agentic AI, Hitachi Digital Services leaders said AI agents must be specific to a domain, such as industrial AI, engineering AI or cybersecurity AI — an assertion that plays to Hitachi’s strengths. They also expect agentic AI adoption will accelerate in 2026, particularly around process and application modernization. One Hitachi Digital Services leader anticipated a shift from “minimum viable product” to “minimum viable agent,” a neat turn of phrase that might indicate both a shift toward a more consulting-focused mindset and internal pressures to deploy more AI-enabled assets. TBR notes that every one of Hitachi Digital Services’ competitors is working through those same shifts.

Mission-critical IT + OT + AI

In TBR’s view, Hitachi Digital Services’ ability to lean on the broader Hitachi company’s engineering expertise and real-world physical assets provides Hitachi Digital Services with clear separation from peers in the IT services space. If Hitachi Digital Services can deliver results in mission-critical — and constantly moving — settings, such as railway operations, clients in less dynamic environments can be assured of its capabilities.

Hitachi leaders noted shifts in the broader IT and OT spaces, such as the greater attention being paid to cybersecurity risks in OT and CFOs being generally more aware of the costs of funding fully separate IT and OT systems, which TBR believes may accelerate IT-OT convergence. Complemented by AI and infused with domain expertise, Hitachi Digital Services, in TBR’s view, should maintain a clearly differentiated position within the digital transformation and IT services space.

 

As a potential partner for large ISVs and cloud hyperscalers, Hitachi Digital Services’ combination of IT, OT and AI capabilities provides easily understandable differentiation. For large enterprises in Hitachi Digital Services’ target industries, Hitachi’s track record in mission-critical environments and the company’s sustained R&D and innovation create a compelling value proposition.

 

TBR will be more closely tracking Hitachi Digital Services’ evolution, particularly as IT-OT convergence accelerates and Hitachi Digital Services’ role in the broader ecosystem grows.