Attributed Spokesperson: Sukruth Pillarisetti, Senior Vice President, Analytics & AI, Straive

The transition from an AI pilot to enterprise-wide deployment is often more complex than the initial development of the model. A solution that performs well in a controlled environment may encounter very different conditions when deployed across business units, where data resides in multiple systems, processes are interconnected and operational exceptions are more frequent. 

Enterprises need to begin by identifying the business outcome they want to improve. In fraud management, AI should help investigators prioritise suspicious transactions, reduce false positives and resolve cases more efficiently. Achieving these outcomes requires the model, underlying data and operational workflow to be designed together.  

The choice of technology is equally important. Traditional machine learning remains effective for many structured, data-intensive decisions, while generative AI can be particularly useful for processing documents, interpreting unstructured information and supporting knowledge-intensive work. In several cases, a combination of these approaches, supported by domain expertise, may deliver the best results. 

Enterprises should assess AI investments through measurable business outcomes, including processing time, decision accuracy, cost-to-serve and operational capacity. Improvements in processing time, decision accuracy, cost-to-serve and operational capacity provide a more meaningful assessment of value. If these outcomes are not evident, enterprises need to examine how the solution has been implemented and integrated into the business. 

Banking and payments offer significant opportunities in fraud detection, credit risk management, collections and transaction monitoring. These functions involve large volumes of data and decisions where both speed and accuracy have direct financial implications. Advanced analytics and AI can help identify emerging patterns, prioritise cases and improve the allocation of operational resources. 

Collections is one example. Predictive models can help institutions identify accounts that may be at risk of default, while analytics can support decisions on which accounts to prioritise and what interventions may be appropriate. In fraud management, investigators need contextual information to distinguish genuine threats from legitimate activity, particularly when false positives increase operational costs and affect customer experience. 

In retail, demand forecasting, inventory planning, pricing and customer analytics offer considerable scope for improvement. Better forecasts can help businesses balance product availability with inventory costs, while more informed pricing and customer decisions can support margins. Even incremental improvements can have a material effect when applied across large product portfolios and store networks. 

The appropriate technology will vary by use case. Traditional analytical methods and machine learning can be highly effective where the problem is structured and historical data is available. Generative AI has a more specific role in applications involving language, documents and unstructured information. The priority should be to identify where better intelligence can improve a business decision, and then select the most suitable approach. 

A lot depends on how well these capabilities work together. Data engineering ensures that the information is available and usable. Machine learning and AI use that information to generate predictions or insights. The business workflow determines what happens next. 

Problems can arise at any of these stages. Model performance depends on the quality and context of the underlying data. Predictions also need to reach the right decision-makers within their operational workflows to inform timely action. 

Fraud detection is a useful example. The system needs reliable transaction data, the model needs to identify suspicious activity, and the investigation team needs enough information to assess the case. What the team learns from that investigation should also help improve future decisions. Each step depends on the others. 

This is why I would encourage organisations to involve their data, technology and business teams early in the process. They need to agree on what information is required, how the output will be used and how performance will be assessed once the solution is live. 

The work continues after deployment. Data changes, business conditions shift and new situations emerge. Teams need to monitor results and make adjustments as they learn more about how the solution performs in practice. Over time, this is what helps make AI a dependable part of business operations. 

Having led a global analytics team of more than 300+ people, I have seen how important it is to bring technical skills and business understanding together. Data scientists and engineers may know how to build a solution, but the people who understand the client's operations often know which problems are worth solving and what could go wrong when the solution is put to use. 

That understanding becomes especially important when an organisation operates across countries and business units. Different teams may use different systems, follow different processes and work within different regulatory requirements. A solution that works in one market may need adjustments before it can be used in another. 

There needs to be some consistency in how data is managed, models are evaluated and risks are handled. At the same time, teams need room to account for their own operating requirements. Getting this balance right takes coordination between technology teams, business leaders and domain experts. 

I would also give business teams an active role in developing and evaluating AI solutions. They understand the exceptions, practical constraints and customer requirements that may not be obvious during development. Their feedback becomes particularly valuable once a solution is in use. 

For organisations starting to scale, it makes sense to begin with a few clearly defined use cases. As teams gain experience in deploying and maintaining these solutions, they can apply what they have learnt to other parts of the business. This gradually builds the people, processes and technical capabilities needed for wider adoption. 

At Straive, our focus goes beyond building AI solutions to operationalizing AI within enterprise environments. We help organisations integrate AI into their existing systems, embed it deeply into business workflows and run these capabilities in ways that deliver sustained value. This means ensuring AI works within the realities of each business, rather than treating it as a standalone technology. 

We start by understanding the business outcome, the existing technology stack, the available data and how teams work. Our approach is agile and technology-agnostic, allowing us to adapt to the client's environment rather than requiring them to adopt a prescribed set of tools or platforms. Depending on the use case, we bring together data engineering, analytics, machine learning, generative AI and domain expertise to develop an approach that fits the business. 

The real value lies in making AI work in practice. Beyond developing solutions, we integrate them into existing workflows and, where needed, help reimagine processes to make the most of AI. We also focus on running these solutions in production, with experts in the loop to validate outputs, conduct appropriate checks and evaluations, monitor performance and make adjustments as business needs evolve. This helps ensure AI is not just deployed, but becomes a reliable part of day-to-day operations. 

Scaling AI requires more than technology. It calls for operational agility, domain expertise and continuous oversight. Our approach combines the flexibility to adapt as technologies and business requirements change with human expertise to assess outputs, evaluate performance and address issues. This ongoing involvement is essential to maintaining the quality, relevance and effectiveness of AI solutions at scale. 

At Straive, we help enterprises move from AI experimentation to operational adoption. By combining technology-agnostic integration, workflow transformation, production operations and expert-led oversight, we help businesses embed AI into the way they work and continuously improve how they use information, make decisions and deliver business outcomes.