Top 12 Generative AI Services Providers Driving Real Enterprise Outcomes

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 GenAI works; 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 2024 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 eleven 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 12 generative AI services providers didn’t earn their position through headcount or headline partnerships. They got there through focused IP, repeatable frameworks, and a real understanding of what it takes to move from idea to interface.

Here they are, in alphabetical order: 

1. Apexon focuses on AI-driven workflow and CX enhancement, with strengths in model training, fine-tuning, and compliance-led deployment. Its AI CoE accelerates innovation and experimentation with structured infrastructure and dedicated talent.
2. Ascendion delivers adaptable GenAI solutions aligned with business goals, combining scalable models with strong data integrity practices. Its India-based GenAI Studio enables immersive client collaboration and rapid prototyping.
3. Birlasoft helps enterprises improve efficiency through AI automation and tailored model fine-tuning. Its Cogito platform offers guided acceleration from exploration to full-scale deployment.
4. Brillio integrates GenAI into enterprise workflows with a strong focus on change management and outcome-based delivery. Its Microsoft Azure OpenAI partnership strengthens compliance, governance, and AI risk oversight.
5. EXL specializes in GenAI for data-heavy industries like healthcare and finance, combining domain depth with scalable LLM applications. A strategic alliance with NVIDIA enhances its AI-powered insight engines and smart agents.
6. GlobalLogic integrates GenAI into digital ecosystems to drive innovation and automation. Its Hitachi–Google Cloud Business Unit supports scalable enterprise adoption using platforms like Vertex AI.
7. HARMAN delivers GenAI for real-time decision-making across healthcare, automotive, and smart tech. Its ForecastGPT platform powers predictive analytics with built-in explainability.
8. Hexaware Technologies combines cloud and data engineering with AI delivery via its Decode and Encode frameworks. Platforms like RapidX™ and Tensai® ensure compliant, scalable deployment, backed by active upskilling programs.
9. Persistent Systems delivers scalable GenAI built to integrate with legacy infrastructure. Its Microsoft-backed Population Health platform demonstrates strength in compliance, care outcomes, and operational AI design.
10. Unisys guides enterprises through GenAI adoption with tailored strategies for governance, risk, and ethical deployment. Its solutions prioritize alignment with business goals and legacy system integration.
11. UST Global offers end-to-end GenAI delivery with rapid pilot-to-production execution. Its Retail GenAI platform helps clients embed GenAI into core operations for speed and scalability.
12. Virtusa empowers organizations with its Helio suite of accelerators and consulting services. It enables fast, scalable GenAI deployment tied directly to digital transformation goals.

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 12 providers 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

Leading 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 

Among the 12 leaders, Hexaware’s Decode AI and Encode AI model stands out for its structured, outcome-focused approach. 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?