Generative AI (GenAI) has shifted from an experimental breakthrough to an enterprise battleground. For many organizations, the early fascination with large language models (LLMs) has given way to a pressing question: How do we operationalize GenAI safely, quickly, and at scale?
This shift marks a critical inflection point. It is not about proving that generative AI models work; enterprises already know it can summarize reports, generate content, or answer support queries. The challenge now lies in integrating GenAI across workflows, protecting data, and aligning outcomes with business value.
The 2025 ISG Provider Lens™ Generative AI Services (Global) report offers timely insight into who is helping enterprises meet that challenge. Among its evaluations covering over 70 providers, the report identifies thirteen midsize providers as leaders in both strategy and consulting services and development and deployment services. Along with building models, they are delivering structured, secure, and domain-aligned GenAI solutions that work in production.
In this blog, we explore how these generative AI companies are enabling enterprise-scale transformation, and the key challenges, best practices, and GenAI trends shaping the path ahead.
The Rise of the Midsize Generative AI Service Providers
These 13 midsize firms earned recognition as some of the best generative AI service providers by focusing on IP, repeatable frameworks, and real-world execution.
Here they are, in alphabetical order:
Apexon
Prices GenAI with outcome-linked tiers that forecast time to value and GPU spend. Sets hallucination thresholds up front. Delivers across text, tables, graphs, images, and conversational UIs for context-heavy work.
Ascendion
Leads with an engineering-driven advisory that emphasizes agent-centric SDLC transformation and modular studios. Scales delivery capacity through acquisitions such as UX Reactor, Moody’s, and Nitro.
Birlasoft
Centers consulting on platform enablers and domain-calibrated SLM strategies for key verticals. Expands the Cogito environment with benchmark instrumentation, token-level telemetry, multi-agent orchestration, and fine-tuning pipelines.
Brillio
Uses industry playbooks and accelerators across telecom, healthcare, and BFSI to target high-readiness use cases. Deploys vendor-agnostic stacks that interoperate with AWS Bedrock, Snowflake, and ServiceNow.
EXL
Shifts strategy toward vertical orchestration and governance-first consulting with platform-led agent design. Strengthens delivery with new agent patterns, prompt augmentation, platform telemetry, and an expanded EXLerate.AI orchestration layer.
Firstsource
Frames consulting around an agentic platform strategy, vertical copilots, and the DEEP lifecycle with ROI-based pricing. Operationalizes through Agentic AI Studio with 50+ task models, LangGraph-ready orchestration, and gated, logged workflows.
HARMAN
Operationalizes domain stacks such as HealthGPT and ForecastGPT with governance checkpoints embedded in LLMOps. Enhances execution via Genesis with prompt scoring, rollback-capable orchestration, memory, and traceability.
Hexaware
Repositions advisory with Decode AI and AssessIQ and embeds security-led governance in solution design. Scales delivery through AgentVerse upgrades, evaluation tooling, telemetry-linked prompt orchestration, modular agents, and custom LLM routing.
Persistent
Builds advisory around agentic blueprinting and global co-creation studios with structured hyperscaler GTM paths. Delivers via GenAI Hub and SASVA with lifecycle controls, private LLM gateways, token-aware routing, and budgeted telemetry.
Trigent
Focuses advisory on UnityGPT frameworks and IRA and Trinity agents with AI Studio for agentic design. Upgrades the platform with a strengthened orchestration backend, modular agent recipes, prompt evaluation, and integrations with OpenRouter, LangSmith, and MLflow.
Unisys
Guides adoption through internal assistants and use-case programs in QSR and CPG with a roadmap for federated agents and Responsible AI. Implements through the Service Experience Accelerator with state-transition logic, telemetry-aware orchestration, curated knowledge, and multilingual workflows.
UST
Runs advisory via modular risk workshops, AWS co-created strategy tools, and broad internal enablement. Operates a delivery stack featuring Smart Genie for agents, CodeCrafter for multimodal code conversion, and Navigator AI for simulation with memory- and compliance-aware retrieval.
Virtusa
Organizes advisory around distributed AI Labs and modular service lanes for platform-integrated consulting. Operates the Helio platform with modular orchestration tools, prompt-layer compliance scaffolds, evaluation-first deployment, audit-friendly agent telemetry, and flexible runtime integrations.
These providers demonstrate a shift in the GenAI market: away from broad strategy conversations and toward practical delivery models that are measurable, replicable, and ready for enterprise use.
The Challenges of Generative AI Adoption
Even with increased investment and growing organizational support, enterprise GenAI adoption often stalls at the rollout stage. The issues are rarely about model performance but rather about architecture, data, and governance.
Disconnected Ownership
When delivery teams, data security, and business units all operate in silos, deployment becomes a coordination problem. Projects slow down or stall due to a lack of aligned accountability.
Infrastructure Gaps
Many organizations still lack the cloud foundation, clean data pipelines, and scalable environments required to support GenAI Models. Hosting large models in production involves more than provisioning compute; it requires orchestration, observability, and cost management.
Incomplete ROI Models
While generative AI development has delivered functional results, many organizations struggle to track business outcomes. Without tying solutions to metrics like processing time, conversion rates, or customer retention, it is hard to justify further investment.
Low Trust from Users
AI systems that offer no transparency on how they generate responses often face internal resistance. Staff are hesitant to rely on black-box outputs without confidence scores or clear sourcing.
Managing AI Deployment Costs
The cost of scaling GenAI systems presents a significant barrier. From the high computational demands of large models to the ongoing operational expenses of maintaining AI pipelines, many organizations struggle to balance innovation with cost-efficiency
These barriers are why choosing the right generative AI consulting firms matters. Experience, tooling, and delivery discipline can reduce friction and shorten the path to impact.
Best Practices for Scaling Generative AI Services
The top generative AI companies above are solving for these realities through specific, replicable practices. Here’s what consistently shows up across their programs.
Start with Use Case Prioritization
Leading firms run workshops to assess value potential, data readiness, and risk tolerance. Rather than chase every idea, they score and rank opportunities based on real feasibility.
Use Retrieval-Augmented Generation (RAG) to Anchor Outputs
RAG improves quality by linking GenAI to trusted enterprise data sources. It ensures the model draws from approved material, reducing hallucinations and boosting accuracy.
Choose Task-specific Models When Possible
Small language models (SLMs) tuned to a specific process often outperform large general-purpose models in cost, performance, and explainability. This is especially true for structured workflows.
Wire Governance into the Build Process
Responsible AI is not a post-launch patch. Bias testing, access control, and logging should be embedded in the development pipeline from the start.
Measure Business Outcomes, Not Just Accuracy
Top generative AI companies help clients track metrics like cycle-time reduction, revenue lift, or service resolution improvement. This turns GenAI services into something executives can actually evaluate.
Maintain Human Oversight
The best systems keep people in the loop. They offer confidence scores, flag review mechanisms, and the ability for experts to correct and retrain models based on real feedback.
Trends Reshaping Generative AI Development
Behind the GenAI hype cycle, a new set of durable trends is shaping how enterprises design, build, and scale real systems. These are not marginal improvements. They are directional shifts in architecture, governance, and ownership that will define successful programs going forward.
Smaller, Domain-tuned Models Are Outperforming the Giants
Instead of chasing ever-larger general-purpose LLMs, enterprises are shifting to smaller models tailored to specific tasks and internal taxonomies. These models are faster to train, easier to govern, and cheaper to run while often delivering better results in regulated or high-context environments.
Synthetic Data is Becoming Part of the Standard Pipeline
Enterprises dealing with sensitive domains, such as healthcare, finance, and legal, are adopting synthetic data generation to improve model training without exposing real records. This makes it easier to comply with privacy rules while accelerating use case development.
GenAI is Expanding from Text to Documents, Visuals, and Workflows
Multimodal systems that can understand layout, charts, diagrams, and scanned forms are gaining ground. These capabilities are proving useful in industries like insurance, logistics, and life sciences, where GenAI is now being applied to structured and semi-structured data sources, not just plain text prompts.
Inference is Moving Closer to Where Work Happens
With model compression techniques maturing, some organizations are beginning to run GenAI at the edge, on devices, in warehouses, and in physical environments. This reduces latency and lowers exposure risk, especially when systems must function offline or handle sensitive operational data locally.
Semi-autonomous Agents are Entering Early Production
GenAI is shifting from answer generation to task execution. Enterprises are testing agentic workflows, where AI not only drafts content but also takes context-aware actions, like initiating approvals or updating systems of record. These workflows are still carefully gated, but the trajectory toward task delegation is clear.
GenAI Development is Being Democratized
Low-code tools and visual builders are enabling domain experts, not just engineers, to shape prompts, connect data sources, and embed GenAI into business processes. Midsize providers are leading here, offering customizable templates and no-code frontends to accelerate adoption across business teams.
Responsible AI is Becoming a Contract-level Requirement
Bias controls, explainability, output restrictions, and audit logs are no longer aspirational. They are being embedded into SLAs, procurement guidelines, and delivery playbooks. GenAI service providers are expected to deliver built-in governance, not external frameworks.
These generative AI trends point to a future where this technology is more composable, more secure, and more embedded in everyday enterprise workflows.
The Hexaware Approach
Hexaware Technologies is a top 13 generative AI services provider that integrates cloud and data engineering with AI using its Decode and Encode frameworks.
Many frameworks focus on generating ideas—Decode goes further, applying a structured evaluation to pinpoint the use cases that are both feasible and transformative, grounded in each client’s data environment and compliance requirements.
Encode then brings these use cases to life with speed and discipline—without sacrificing control. Hexaware’s proprietary platforms drive this execution: RapidX™ maps dependencies across legacy systems to ensure integration readiness, Amaze® transforms brittle logic into modular, cloud-native components, and Tensai® embeds security, privacy, and policy enforcement throughout the AI pipeline.
This model consistently delivers real-world results. In one recent engagement with a global logistics firm, Hexaware deployed a full-scale GenAI solution—from roadmap to production—in just ten weeks. The solution included summarization tools built on internal SOPs and RAG-based ticket classification. As a result, the company reduced its service backlog by 40% and saw a measurable boost in satisfaction across frontline teams.
The Strategy Is Only as Good as the System
The generative AI market is flooded with ideas. Fewer firms can take those ideas and turn them into audited, explainable, operational systems. That’s what separates the providers recognized by ISG in both quadrants.
The midsize generative AI companies featured here are proving that it’s possible to move fast and still meet enterprise expectations. They offer a path forward that is clear, measurable, and repeatable. They do not just advise on where to go—they build the road to get there.
For enterprises ready to make GenAI real, these are the partners already doing the work.
Ready to move from roadmap to results? Contact us to see how a top generative AI service provider like Hexaware can accelerate your enterprise transformation.
