The clearest way to see where data science services are heading is to follow how one large enterprise might change over time. Picture an industrial manufacturer with several business units, legacy operational systems alongside newer cloud platforms, and data science work progressing at different speeds across the business.
The stages below are an editorial interpretation of the market progression reflected in the ISG Provider Lens® Advanced Analytics and AI Services – Large and Midsize 2025 U.S. Quadrant Report (the report itself does not define these four stages). An enterprise may sit at more than one stage at once, with a mature forecasting practice in one function and early experiments in another.
Stage One: Isolated Experimentation
The manufacturer does not begin with an enterprise program. It begins with a few people trying to solve problems in their own corners of the business. Supply chain builds a forecasting model. Finance tests another use case. Maintenance experiments with failure prediction. Each team gets something useful from the work, but they use different definitions, tools, and documentation. The knowledge around a model often stays with the people who built it.
ISG describes deployment maturity as uneven across company size, business domain, and data quality. It finds that 28 percent of organizations manage AI centrally within IT, while another 23 percent use a formal center of excellence. The remaining organizations are not all operating at this first stage. Some have hybrid or distributed models. Even so, the figures show that centralized coordination is far from universal.
A provider entering here may help the company make its early work repeatable. That can include choosing a viable use case, preparing the data, building and integrating the model, and putting basic governance in place before the number of pilots grows.
Stage Two: Coordinated Experimentation
Once the early pilots begin attracting attention, the manufacturer puts some order around them. A data science center of excellence is formed; a senior data or AI leader takes charge; and teams begin using common policies. Funding becomes easier to coordinate, and each new project no longer starts from a blank page.
The weak point tends to appear after deployment. A model leaves the center of excellence, passes to IT, and then lands with a business team. Responsibility becomes blurred along the way. The team that built it may no longer run it, while the team using it may not have the authority or skills to manage it over time.
ISG sees a wider move from isolated projects to coordinated transformation portfolios. More than half of AI initiatives now have C-level sponsors. Large enterprises are also strengthening centers of excellence, connecting data governance with enterprise architecture, and formalizing senior data and AI roles.
The provider’s role is to give that sponsorship an operating structure. Common evaluation methods, clearer ownership, production handovers, and stronger links between data science, engineering, architecture, and the business become central to the engagement.
Stage Three: Embedded Operations
Stage three begins when the manufacturer learns that a model can be live and still go stale. A demand forecast may stop reflecting buying patterns. A maintenance model may miss a new type of fault. Performance may vary from one region to another.
The answer is to manage models as production systems. Monitoring, version control, performance reviews, compliance checks, and retraining become part of the normal workload.
ISG calls model lifecycle execution a decisive differentiator. The average enterprise AI initiative now affects more than 1,600 employees, so poor monitoring can quickly become an operational problem. Large providers are building quality checks, drift monitoring, compliance validation, and retraining into their delivery environments. Midsize providers often work more closely with business teams and bring strong domain knowledge, although their capabilities in unified monitoring and automated retraining are less consistent.
At this stage, the engagement cannot stop at deployment. It must cover how the model is used, monitored, reviewed, and retrained.
Stage Four: Enterprise Decision Intelligence
By stage four, data science sits inside the systems used to run the company. Forecasts feed planning tools. Optimization recommendations appear in operational workflows. Predictive analytics, generative AI capabilities, and other enterprise AI services support employees while they are making decisions, rather than sending them to a separate dashboard or specialist team.
ISG presents decision intelligence as a growing business use of advanced analytics and AI. The expected gains appear in areas such as operational decision-making, customer experience, forecasting accuracy, and workflow automation.
Proving those gains remains difficult. Enterprises continue to struggle to connect individual AI initiatives with financial results, and many fall short of their expectations for cost savings, productivity, and revenue growth. A good model can influence hundreds of decisions without leaving a clear trail back to the financial measures used by the business.
That is why value definition, KPI design, and ROI modeling have to enter the program early. Stage four describes where enterprise data science is heading. It should not be read as evidence that most organizations are already there.
How the Provider Role Changes
Data science service providers enter for different reasons at different points in the data science maturity model. Early on, the manufacturer may need help turning a promising pilot into something the business can keep using. Once a center of excellence is in place, the discussion changes. Who owns the model after launch? Who watches its performance? Who steps in when the data changes? Further along, the provider is also expected to link the model’s use with the operational and financial measures already followed by the business.
ISG’s assessment of midsize providers is less about where they sit on the curve and more about the kind of work they handle well. They are often chosen for contained, business-led assignments where domain knowledge and quick decisions are important. Working directly with functional teams can also reduce the time lost between defining a requirement and putting a model into use. Their appeal usually comes down to speed, flexibility, and familiarity with the industry.
The limitations tend to appear when the program must work uniformly across many business units or regions. Enterprise-wide telemetry, auditability, common governance, and consistent performance monitoring may require capabilities that are less developed across some midsize providers.
Provider selection therefore depends on more than the use case. The size of the estate, the number of teams involved, and the level of governance required after deployment all influence the decision.
What Enterprises Should Establish Before Choosing a Provider
Every model does not need to progress to stage four. A regulated company running a small number of high-value models may gain more from a disciplined stage-three practice than from placing analytics inside every executive workflow.
The first task is to understand how the current practice operates. Who owns a model once it goes live? Who notices when its performance begins to decline? What triggers retraining? How are governance decisions recorded? Can the company connect the model’s use with an operational or financial measure?
Those answers reveal the work the provider must take on. They also prevent an engagement from being scoped around an ambition that the organization is not yet equipped to support.
To see why Hexaware is positioned as a Leader in the Data Science and AI Services (Midsize) quadrant in the ISG Provider Lens™ 2025 Advanced Analytics report, click data science services.
