At its October 2026 analyst event in Allen, Texas, Mavenir outlined a strategy to “own the core, monetize AI, and extend the network to space.” Presentations covered the evolution of core network intelligence, agentic service assurance, AI-enabled voice and fraud protection, neocloud infrastructure and AI token monetization, MVNO platforms, and non-terrestrial networks (NTN).

Core software gives Mavenir an established business on which to build an AI strategy

Mavenir’s latest strategy update signals a shift in how the company intends to compete. Instead of trying to match larger suppliers across the entire network stack, Mavenir wants to increase the mobile core’s value to operators. Management repeatedly positioned the core as the indispensable component connecting mobile network operators (MNOs), MVNOs, IoT providers and now NTN. The company’s thesis is that the core can evolve from a system that authenticates subscribers and moves traffic into a platform that improves network efficiency, protects customers and acts as a vehicle to monetize AI-enabled services.
 
Mavenir’s cloud-native IMS (IP multimedia subsystem), packet core, messaging and security assets provide the installed base and subscriber context needed to deliver new functionality without requiring a wholesale network replacement. Mavenir’s stance is that a hardware-agnostic model can enable faster innovation than the full-stack approaches of Ericsson and Nokia, although success will ultimately depend on whether Mavenir delivers simplicity and measurable operating benefits that encourage operators to introduce new suppliers into critical infrastructure.

Mavenir seeks to transform the mobile core into an intelligence platform

Operators are looking for ways their core can deliver value beyond connectivity, particularly as pressure mounts to reduce operating expenses and differentiate services. Mavenir is responding with a distributed-intelligence architecture that places AI and analytics inside network functions, at the edge or centrally, depending on use-case requirements. Its proposed progression from rule-based automation to predictive analytics and generative, agentic reasoning is designed to improve service quality, optimize power consumption, identify fraud and ultimately support more autonomous network operations.
 
Mavenir is introducing embedded analytics and hierarchical agents that can reduce signaling, anticipate demand, assess subscriber experience and automate steps traditionally performed by network operations teams. It also sees AI-assisted engineering and a shared network knowledge base as ways to preserve institutional expertise and speed deployments. Higher-level autonomous orchestration remains further out on Mavenir’s road map, and operators are unlikely to surrender control of critical network actions without clear policy and guaranteed outcomes. Demonstrating energy savings, productivity gains and service reliability will be important initially to encourage early adoption of these solutions, while the sophistication of its AI models will matter more in the long term.

Voice AI offers the clearest near-term opportunity to monetize Mavenir’s installed base

Mavenir conducted a series of demonstrations at the event to show how operators could turn voice solutions from a largely commoditized connectivity service into an extensible portfolio of premium features. Its AI Voice Agent (AVA) enables network-based services on Mavenir or third-party IMS platforms and can use models hosted on premises or in public clouds. That flexibility matters as operators could reserve low-latency or frequently used capabilities for local inference while sourcing less-common functions from cloud models, with network-level controls for subscriber authorization, consent, identity and billing.
 
The showcased functions spanned the entire calling experience. Real-time translation can let callers communicate across languages; AI-powered high-definition (HD) voice can isolate speech, suppress background noise, mitigate packet-loss artifacts and improve intelligibility; and a virtual receptionist can answer calls, collect information and book appointments to help small businesses secure new clients when a human is otherwise unavailable to take the calls. Precall screening agents can flag unfamiliar callers and let subscribers decide whether to answer, while in-call protection can identify suspicious patterns and alert, or potentially disconnect, an at-risk subscriber. Mavenir’s NetAIShield proposition links these capabilities to security functions embedded in the core, giving operators a means to extend fraud prevention beyond handset-dependent screening and after-the-incident analysis.
 
These offerings have strategic value even if individual users do not pay separate fees for each feature. Operators may package voice assistance, translation, noise reduction and fraud protection into premium plans as a way to increase incremental average revenue per user or improve retention. Mavenir is positioned to benefit through software and service opportunities across its installed base of voice customers. Consent, privacy and accurate fraud detection will be essential, especially when AI decisions affect live conversations. TBR expects initial adoption to be concentrated in differentiated bundles and targeted commercial applications rather than being universally enabled.

Neoclouds offer Mavenir a path beyond telecom, provided it owns the commercial layer

Mavenir’s neocloud strategy targets the software layer that makes AI infrastructure usable and billable, including model routing, orchestration, governance, metering, charging and settlement. The company asserts that communications service providers (CSPs), sovereign cloud programs and regulated enterprises need an AI commercial control plane that can convert variable inference consumption into dependable products and invoices. The partnership with neocloud provider Neysa illustrates the division of labor as partners supply the capital-intensive compute and facilities while Mavenir aims to provide platform software and engineering services.
 
The monetization proposition builds on the company’s network usage charging expertise for telecom. Mavenir is developing a BSS overlay that can meter different forms of AI usage, route requests to models based on cost and policy, enforce allowances and place charges on existing operator bills without replacing legacy billing infrastructure. One potential offering is an AI token allowance bundled with a mobile plan, similar to how operators sell voice minutes or data. However, token demand is difficult to predict, model costs can change rapidly, and premium AI services may require more complex pricing than a simple allowance. Mavenir’s ability to manage consumption before charges are incurred could therefore prove as valuable as the customer-facing AI application itself.
 
TBR sees the strongest opportunity in customers that value sovereignty, trusted identity and regulated deployment more than the lowest GPU-hour price. These include operators, government programs and highly regulated enterprises, particularly where existing data centers can be repurposed. Mavenir is also building a forward-deployed engineering (FDE) practice to translate platform capabilities into customer-specific workloads. However, established cloud providers, infrastructure vendors or payment platforms could move into the same monetization layer before Mavenir establishes a repeatable commercial model, creating a potential challenge to this approach.

MVNOs and NTN expand the reach of Mavenir’s core but differ in timing and economics

Mavenir also sees opportunities in MVNOs that are evolving from lower-margin resale models toward greater ownership of core functions, onboarding, charging and differentiated services. Its software portfolio can provide a platform for digital brands to launch more quickly and layer AI-enabled services onto existing connectivity, potentially creating a more accessible route to experimentation than with larger incumbent operators. The opportunity is consistent with the company’s core-centric thesis, but scaling it will require a repeatable, lower-cost delivery model.
 
In NTN, Mavenir seeks to extend its core and communications capabilities into satellite access, roaming and voice services. Speakers presented a long-term ambition to secure more than 30% share in AI-native NTN by 2030, supported by software-led RAN and core participation and future low-bit-rate voice capabilities. That target is aspirational, and standards, service architectures and commercial models are still evolving. TBR views NTN as an option for longer-term diversification and not a substitute for proving the profitability of Mavenir’s established core and nearer-term AI offerings.

Conclusion

Mavenir’s repositioning is grounded in the premise that every wireless service needs a core, but a more intelligent core can do considerably more than carry traffic. The company’s strongest near-term opportunity is to attach AI-enabled voice, security and operational automation to existing operator relationships, while its larger strategic bet is to apply telecom-grade orchestration and network usage charging to the broader AI economy. The company has a compelling differentiation story as a software-focused alternative to full-stack incumbents, but the next test is commercial execution. Demonstrating repeatable operator savings, paid adoption of AI calling and profitable neocloud platform deployments will determine whether Mavenir can turn its core strengths into sustained growth in the AI era.

On Oct. 5-6, Sapiens hosted TBR and a dozen other analysts at the kickoff of the 2026 Ignite event in Nashville and provided presentations from and access to many of the new Sapiens’ executive team including: Mike Ettling, executive chairman of the board; Paul Wheeler, CEO; Glen Ceniza, chief technology officer; James Hannay, chief revenue officer; Yariv Hasar, North America president; Paul Hutchison, chief delivery success officer; Michael Rieder, chief customer success officer; and Franck Cohen, chairman, CYE, and senior board advisor. TBR’s assessment of the event and Sapiens’ recent changes reflect the presentations and conversations in Nashville as well as TBR’s ongoing assessment of Sapiens and its place in the broader technology ecosystem.
 
Forty-year-old companies do not transform at the speed of AI. They do not upend their commercial and delivery models at the same time. And successful transformations almost never start with a wholesale leadership overhaul spurred by new ownership. Well, maybe that last one happens, but the first two do not. For the last nine months, Sapiens has been testing the possibilities of radical AI-enabled change, while continuing to support customers in a highly regulated and hidebound industry. New leadership has challenged the company to move at AI speed while establishing a new standard for quality. Sapiens has made a massive bet that the trust it has built with long-standing clients will carry over as the company shifts its commercial, delivery and technology partner models to a fully AI-enabled way of working. It is a bold bet; now comes the execution.

New leadership, an AI-enabled platform and a SaaS-first model are reshaping Sapiens

Sapiens became a private company on Dec. 17, 2025, when Advent completed its acquisition. Ettling became executive chairman and interim CEO, and Wheeler and Hannay joined as CFO and chief revenue officer, respectively. Ceniza joined as CTO in January 2026, and Hutchison and Rieder were subsequently appointed to lead delivery and customer success, respectively. Wheeler was named CEO in August. With more than 40 years of experience behind it, Sapiens has put that experience to work in SapiensAIP, the Sapiens Autonomous Insurance Platform, launched in September 2026. Sapiens also moved its global headquarters to London as of June 2026.
 
Cohen noted that Sapiens does not view technology and AI as the core challenges facing its clients. Instead, legacy insurance processes create a barrier to change, data remains siloed and the opposite of seamless, and change management confounds even the most agile insurance companies. Hannay added that Sapiens’ clients express an eagerness to learn about AI but do not understand the reality of what AI can (and cannot) do or where to start. Notably, during both an analyst breakout and main-stage presentations, a few clients discussed success they are achieving in AI adoption, although every other client TBR spoke with remained in the exploration and pilot stages, reinforcing Hannay’s assertion.
 
On Sapiens’ evolving business model, both Wheeler and Ettling said the company had shifted its services strategy, deemphasizing services revenue and forgoing the standard time-and-materials commercial model. When TBR asked how those changes could affect ecosystem partnerships, Sapiens executives described prioritizing SaaS commercial models while concurrently packaging services with traditional global systems integrators’ and consultancies’ core offerings. In TBR’s view, Sapiens’ commercial model pivot may prove to be as consequential as the company’s AI platform investment. Sapiens’ ecosystem partners will increasingly expect flexibility, outcomes-based pricing, and a focus, by Sapiens, on staying in its swim lane. Driving a SaaS business model while pulling back on services should make Sapiens a highly attractive business partner to the likes of Capgemini, Accenture and Deloitte.
 
The customer panel with analysts provided tremendously useful insights into the challenges Sapiens faces in the near term, including overcoming legacy perceptions about the company’s products, delivery and customer support. (Note: Sapiens’ newly hired customer success executive was in the room to hear the unvarnished customer feedback.) Customers voiced common concerns about AI and technology adoption, including the need for change management embedded at the start as well as a target operating model, the benefits of evangelists and champions, and the imperative to encourage experimentation. Another customer mentioned that documentation had long been a problem when working with Sapiens but noted his expectations had changed with the new ownership and leadership.
 
In TBR’s view, Sapiens’ CTO’s repeated assertions that the new AI-enabled platform would include near-complete auditability likely addressed the documentation concerns. Overall, the customers on the panel with analysts and on the main stage, as well as those TBR spoke with during the event, all expressed quite clearly that they believed the new leadership brought critically important change to Sapiens.

Sapiens makes auditable AI its competitive advantage

Sapiens built a brain. As simplistic as that sounds, it perfectly captures exactly what Sapiens did in a surprisingly short time. Sapiens had 40-plus years of documentation, software code, change orders and contracts it used as a foundational data layer, which created 500,000-plus nodes and 11 million connections between those nodes. Every Sapiens engagement going forward will be grounded in this AI brain, which AI agents can tap into, provide answers, and make decisions faster and more accurately than Sapiens’ own engineers. Sapiens’ brain provides the knowledge base, the agents do the reasoning, and what Sapiens calls “surfaces” allow actions by Sapiens’ clients and their agents.
 
Notably, whenever Ceniza discussed AI or SapiensAIP, he stressed that Sapiens’ AI included oversight by design and is and will be held to a higher standard than humans. He also noted that Sapiens built the AI platform outside of the core Sapiens software systems, in no small part because those legacy core systems were not built for AI, could not adapt at AI’s speed, and could not be reused in an AI-native manner. Critically — and this point came up during sessions with analysts and on the main stage — Ceniza said the company intended to “set a new standard for AI quality,” which he defined as AI that is “accurate, consistent and fully auditable.”
 
Moving to a 10,000-foot view of the AI age, TBR notes that during the last 18 months “fully auditable” has shifted from a niche concern of governance, risk and compliance consultancies to a near-board-level discussion at many enterprises (perhaps thanks to those consultancies themselves, notably KPMG and Grant Thornton). Sapiens has exceptionally deep domain knowledge around how insurance companies’ software and IT actually work. The company has tapped into that knowledge in a secure and compliant manner while leveraging it to make Sapiens’ AI smarter. And Sapiens is bringing all of that to clients in a unified and comprehensive manner while remaining respectful of the systemic challenges clients continue to face.
 
Without deep expertise on the insurance industry, TBR has to analyze Sapiens’ approach from our extensive technology and services business model knowledge and the perspectives of ecosystem partners. In short, we believe Sapiens’ approach makes tremendous sense. Build a knowledge layer on your own proprietary, but highly relevant, data. Establish proper governance. And unleash AI to make processes and decision making faster. Can the insurance industry keep pace with Sapiens? That is for others to determine, but in TBR’s view Sapiens has assembled nearly all the essential components. Now comes the execution. After Sapiens’ new executive team made a 90-minute pitch about the company’s reinvigorated strategy, TBR asked clients attending the event for their assessments. Responses varied from genuine surprise and excitement to wait-and-see skepticism. As always, delivering results will determine what customers come to believe.

2027 predictions: Not Your father’s Sapiens

A few questions remain for TBR:

• Will becoming more local, placing talent closer to clients, and embracing the forward-deployed engineer model conflict with Sapiens’ overall strategy of retiring the federated model and presenting clients with one view, one experience, one commercial engagement? It might. But if on-the-ground Sapiens professionals can sell, deliver and support the full Sapiens portfolio, maybe these strategies will not create tensions and uncertainties. Being exceptionally local and maintaining a global quality standard has bedeviled IT services companies and consultancies for generations. With its combined AI and SaaS model, perhaps Sapiens will not struggle with this challenge.
• As Sapiens rapidly transitions into AI, how will the company’s slower-moving customers respond? Will the new Sapiens be able to satisfactorily support laggards on the AI journey? Will Sapiens say “no” when customers resist a move toward the new AI-enabled platform? The answers to the last two questions are probably “yes” and “no,” but the company will be looking for customers to match Sapiens’ own acceleration into AI.
• With the new ownership, Sapiens has taken a cudgel to every aspect of its business model: commercial, operations, delivery and (soon) partner. For a 40-year-old company with its own technology, process and skills debts, the question becomes, “What happens to the legacy people, knowledge and culture?” In TBR’s view, uncertain leadership would struggle to navigate these seismic changes. Sapiens’ new leadership is not uncertain.

So, what should we make of Sapiens now and heading into 2027? TBR cannot evaluate what all these changes mean for customers. Like most of the new Sapiens leadership, TBR does not have insurance domain expertise. But TBR understands what these changes mean for Sapiens’ ecosystem partners: a commercially flexible, AI-enabled, well-established, industry-specific software leader should be an exceptionally good partner for an SI or consultancy changing its own commercial, delivery and partner business models. TBR repeatedly stresses three core business beliefs: Do what you do well (and nothing else), do not bore clients with your internal changes, and do everything possible to help your ecosystem partners sell you. Sapiens appears to be moving along those tracks with AI-enabled speed. For the third time: Now comes the execution.

In September 2026, TBR spoke with HCLTech’s Enterprise Transformation & Advisory team about its Total Experience Services portfolio, which combines customer experience, employee experience and user experience into a single, unified framework, and how the advent of AI has accelerated the need for a connected and unified experience. HCLTech leadership also emphasized the need for Responsible AI to ensure that every layer of AI remains safe, governed and trustworthy, an essential aspect for IT services vendors to engage effectively with clients. 

Central platforms to overcome friction in experience

In an ever-connected world, HCLTech established its Total Experience offerings to create a unified user and customer experience for its clients, removing pain points and friction across teams and departments. The transformation begins with the operating model, as many companies have different platforms and experience solutions across functions. The goal of the company’s experience solutions is to connect users across operations by optimizing and redesigning operating models.
 
Lester Lam, HCLTech’s EVP of Enterprise Transformation & Advisory, discussed key market trends that are increasing the need for and relevance of HCLTech’s Total Experience Services. The notable market trend for TBR is the increasing pressure on clients to scale experience transformations. This trend is making it easier for vendors to convince clients to modernize legacy systems and adopt an AI-led operating model.

Lam explained that clients mainly face operational challenges, leading HCLTech to focus on operating model transformation and connecting all parts of the organization. The friction caused by managing disparate systems could be alleviated through AI, creating opportunities for HCLTech around advisory, design, implementation and management. By leveraging AI, HCLTech shortens analysis and workflow planning time, helping clients design systems that address their challenges faster and more cohesively.
 
HCLTech structures its Total Experience Services around three main groups — business, product and platforms. The groups help clients set and achieve outcomes by focusing on internal experience (business), services and products delivered (product), and underlying technology (platform), all grounded in a foundational level of experience. Lam also noted that establishing an AI-led operating model is the key to improving experiences and ensuring the transformation sticks by uniting all groups under one strategy.
 
According to Nish Trivedi, HCLTech’s SVP of Enterprise Transformation & Advisory, enterprises must transform six core levers to reshape their operating models effectively. The six enablers include strategy & alignment; governance, funding & value; data, platforms & AI; organization & roles; talent & culture; and execution model. HCLTech’s clear definition of AI-led operating model transformation and the key levers that need to be modernized to ensure the client can sustain the transformation set the company apart from its peers. Helping clients connect functions across their business through experience and underlying platforms requires all six levers to operate efficiently while also leveraging data and insights from interactions and customer engagements. HCLTech’s strategy aims to push clients’ organizations to operate as one and use standardized base levels that drive lasting change within enterprises and enable seamless cross-collaboration.

XMO

Trivedi outlined the HCLTech Experience Management Office (XMO), part of its Application Management Services portfolio, which aims to deliver business outcomes by integrating technology and human expertise. Embedding the XMO function into its Total Experience services improves HCLTech’s ability to create effective pathways to achieve outcomes, while combining the user experience with human touchpoints for more personalized and seamless experiences. Trivedi emphasized how HCLTech leverages personas and customer journeys to test its platform, identify challenges and determine how to fix them.
 
Using the XMO approach modernizes HCLTech’s ability to personalize the experience with a simple framework that still delivers value. Matching human touchpoints with the technology experience allows HCLTech to strengthen connections between employees and platforms as well as connections across units. The framework’s repeatable nature, paired with specialized personas, helps HCLTech accelerate engagements while maintaining client stickiness.

Change management

HCLTech would be remiss if it did not acknowledge the impact that AI and experience solutions have on traditional workflows, organizational structures and talent needs. According to Lam, the main challenge is distrust of AI, and organizations must recognize the problem to support change management. Lam noted that enterprises need transparency and acknowledgment to guide the talent transition and provide the resources to properly leverage new technology for greater productivity. Additionally, the theme of continuous change across organizations — including talent, portfolio, structure, partner ecosystems and strategy — that TBR has followed closely remains essential for enterprises to keep pace with changing technologies and client needs.
 
Change management is an essential piece of transformation and is considered an experience management lever for HCLTech. Guiding clients through the workflow and process transition ensures they can achieve outcomes, positioning HCLTech to generate value for its clients. HCLTech’s efforts to align its priorities with those of its clients are reflected in its change management approach, which considers AI-native needs and impacts. Lam also noted how HCLTech recognizes change fatigue among enterprises’ employees, given the frequent introduction of new tools, platforms and processes. Addressing these issues while bringing in a cohesive strategy and experience platform to improve workflows will help clients increase productivity. However, doing so requires careful consideration of these challenges and strategies to manage them effectively.
 
To ease these challenges and concerns within clients’ organizations, HCLTech leverages its Responsible AI framework to bring trust and governance across every AI layer. Heather Domin, HCLTech’s VP of Responsible AI, said that to fully realize value, clients need to blend AI capabilities, human adoption and earned trust. HCLTech’s Responsible AI involves educating and building understanding of risk, including how it will evolve. While it is impossible to predict all future risks, HCLTech’s Responsible AI prepares clients to respond to internal and external threats by establishing controls such as risk-based guardrails that depend on AI agent tasks, adding societal safeguards and human checkpoints, and ensuring leadership alignment.
 
Responsible AI presents many opportunities for HCLTech, particularly as concerns about AI use and controls grow. HCLTech can use these capabilities to strengthen clients’ existing AI environments with additional controls and insight into operations. Embedding responsible AI practices across its portfolio will strengthen HCLTech’s value proposition and build client confidence in integrating AI into their operating models by expanding human-in-the-loop capabilities and clarifying how to implement and monitor usage controls.

Client example

Lester explained how a global retail client used Total Experience to improve its brand management, operating model and digital marketing strategy. HCLTech helped the client rewire its operating model to improve demand ideation and enhance workflows. The main transformation focused on the client’s digital marketing strategy and consolidating brand management into one unit to create more cohesive, consistent operations. Additionally, simplifying the operating model helped HCLTech centralize the client’s brand and strategy to better leverage insights and data.
 
The main change that stands out to TBR is the reorganization of products and brands and the resulting impact on the client’s marketing and engagement with consumers. Lam noted that the platform is the same at its core but enables brands to create their own baseline metrics, identify areas of highest engagement, and retain their differentiated qualities.
 
This engagement is a strong use case for HCLTech’s AI-driven operational model transformations and the need for consistent, personalized experiences. It showcases HCLTech’s expertise beyond technology implementation and into talent strategies, including development, simplification, optimization and change management. Emphasizing these capabilities strengthens HCLTech’s position as a trusted AI partner by focusing on talent transformation and optimizing operations so clients can fully benefit from AI, starting with the operating model.

Conclusion

According to TBR’s Digital Transformation: Digital Marketing Services Benchmark, “As one of the most mature components of digital transformation, customer experience continues to push buyers toward omnichannel programs that unify data, workflows and engagement across the customer life cycle.”
 
HCLTech has quickly advanced experience solutions and created customer and employee journeys through its portfolio to capitalize on the demand outlined above. Focusing on cohesive strategies that work together removes friction and increases the value of insights and data through standardized approaches and frameworks. Lastly, HCLTech highlights the value of its change management portfolio, which optimizes talent structures and facilitates the adoption of AI tools to shift toward higher-value services attached to AI-led transformations while securing client relationships.
 
HCLTech’s ©Human Intensity Reduction Index (HIRI) has remained above its peer average as the company is diligent about staying on top of demand and avoiding hiring talent ahead of services needs. While HCLTech progressively embeds AI agents and leverages advanced tools internally to drive productivity, the company has not significantly increased its service volume with a similar amount of talent. Effective management of talent, including training and the addition of specialized skills, helps HCLTech bring in-demand services to clients and will lead to HIRI improvement.