Movate offers CIOs a trail map to reorganize operations for AI adoption

Enterprise AI adoption has given CIOs a case of commercial and governance whiplash. AI is changing how work gets done faster than enterprises can measure, price, or control it. Developers now regularly use copilots, agents, and automation platforms to speed up the software development lifecycle (SDLC), but operating models are stuck in the old world. Most CIO organizations are trying to deploy AI into operating models built for labor-based delivery. That mismatch is creating governance chaos, cost uncertainty, and unclear productivity metrics. Some service providers are responding to the need for new frameworks by using a Human+AI strategy, not just for productivity gains, but to change how work is done, and they price and measure that work accordingly. Movate is one such provider, evolving its strategic direction by reorganizing to a model that could help enterprises better embrace AI-enabled work.

The Services-as-Software™ evolution is the future model driving operational change

HFS sees the industry entering a structural shift from services defined primarily by labor to services enabled by software, engineering assets, and AI-augmented expertise. We call this emerging model Services-as-Software (SaS), where the most effective providers combine deep domain native talent with reusable accelerators, automation frameworks, and AI-assisted engineering workflows. HFS expects the SaS market to exceed $1.5 trillion by 2035, as enterprises increasingly favor service providers that embed domain expertise into software assets, automation frameworks, and AI-driven engineering capabilities, rather than relying solely on large pools of developers and staff.

Reflecting this shift, Movate is embedding AI across engineering, operations, and customer experience services while redesigning its delivery model. The objective is to move away from traditional labor-based services toward AI-enabled services that operate more like software platforms.

Movate’s approach to AI starts with operational model change

When service providers are no longer simply supplying people but software-driven systems that produce engineering outcomes, CIO organizations must rethink how they measure productivity, structure contracts, and govern delivery because the old labor-based assumptions no longer apply. Movate has made a committed effort to move away from work priced by the hour, scoped by headcount, and governed by delivery capacity.

AI-led and augmented provider capabilities are the first frontier of Services-as-Software

In the last year, Movate has reorganized its AI strategy into the key focus areas where it can help clients re-operationalize:

• AI-led application engineering. Movate is building an AI-enabled SDLC platform that supports the entire development lifecycle from requirements documentation through testing and deployment. This includes AI copilots for developers, testers, and analysts, as well as the use of knowledge graphs to contextualize enterprise data automation across 35%–40% of SDLC activities. The platform is built through a partnership ecosystem (including Lyzr, GitHub, Azure, Gemini, and Opsera), with Movate contributing intellectual property from its knowledge graphs and ontologies that contextualize enterprise data.

• Data engineering for AI readiness.To double down on building a strong data foundation, Movate acquired Prescience, a data engineering firm with about 125 engineers and existing IP in data cleansing and governance tools.

• AI-led enterprise operations.Movate is embedding AI into IT operations and enterprise support services, including application management services (AMS), infrastructure operations, and service desk or workplace support. A key change here is evolving support structures, such as introducing new hybrid support tiers where AI augments human agents and reduces the need for specialized expertise.

• AI-led customer experience.Given its heritage in contact center services, Movate is investing heavily in AI-enabled CX capabilities, focusing on call deflection and voice bots, agent-assist tools, automated call summarization, and post-call analytics and quality monitoring. The CX service line will depend heavily on partner platforms such as Salesforce, Genesys, NICE, Krisp.ai, MelodyArc, Kahuna Labs, and AmplifAI.

• Enterprise agentic solutions.The outer layer of Movate’s AI strategy focuses on business-facing AI agents, built primarily on enterprise platforms such as Salesforce, ServiceNow, and Microsoft. Movate has already developed over 120 pre-built agent use cases across industries such as healthcare, retail, and high-tech.

Movate’s internal AI platform orchestrates agents and workflows

At the center of the strategy is the Mova iO platform, which orchestrates AI agents across engineering, operations, and service workflows to drive intelligent outcomes for enterprises. This marketplace serves as a hub, providing AI agents, orchestration frameworks for agent workflows, knowledge graph integration across enterprise systems, and digital twins for roles such as developers and testers. The platform allows enterprises to define roles, permissions, and governance controls over how agents operate within delivery environments.

This contextual layer allows AI agents to understand how enterprise systems, workflows, and artifacts relate to one another. Instead of generating generic responses, AI agents can operate with a deeper understanding of the organization’s development lifecycle, operational processes, and historical incident data. For enterprises struggling to productionize AI, this contextualization layer may prove essential. Without it, AI remains a tool; with it, AI becomes an operational system for services delivery.

The most profound changes are organizational, not technical

AI adoption requires redesigning how operational teams work. Traditional support structures, such as L1–L2–L3 service hierarchies, are already evolving as AI agents assist in diagnostics, knowledge retrieval, and problem resolution. With contextual AI systems supporting agents, many support tasks can become skill-agnostic, enabling organizations to create new hybrid roles that blend human judgment with AI automation.

The platform also enables a shift in commercial models. Instead of charging for developer hours, Movate is pricing some engagements based on outputs, such as story points delivered per sprint or improvements in testing coverage. This reflects a fundamental Services-as-Software dynamic: when platforms and agents perform much of the work, services can be priced based on what is delivered rather than how many people are deployed. The opportunity is clear, but CIOs must also realistically evaluate how these models align with their current maturity, particularly in data visibility and governance.

The Bottom Line: Services-as-Software is the future, but it can’t succeed without pivoting the operational model.

This shift could allow enterprises to consolidate support layers, improve resolution speed, and redeploy skilled resources toward higher-value activities. Realizing these benefits requires much more than technology; enterprises must work with their forward-thinking partners on retraining teams, redesigning workflows, and embedding AI into everyday operational processes. AI is not simply augmenting services; it is transforming them into software-driven systems for delivering business outcomes.