productivity gains in business processes with AI, automation and analytics
of organizations are investing in integrated process-automation platforms by 2026
Across industries, the pace of change has never been faster. Organizations are navigating a perfect storm of economic volatility, technological disruption, and rising customer expectations — forcing leaders to rethink how work gets done.
Enterprise-wide digitalization is accelerating. A majority of global enterprises are now integrating AI, automation and analytics directly into their business-process transformation roadmaps to achieve measurable productivity and agility gains
Platform-based ecosystems are replacing siloed systems. We are seeing an increasingly strong shift toward cloud-native, composable architectures for business support functions
The next frontier is intelligent, outcome-based operations — where human insight and digital intelligence converge to deliver agility, transparency, and measurable business value.
Amid this transformation, the differentiator between those who merely adapt and those who lead are the ability to turn business support to business value.
We help organizations to leverage AI-driven innovation to redesign and optimize operations to deliver measurable business outcomes. Combining deep industry expertise, data-driven insights, and proven advisory methodologies, we partner with clients to reimagine their operating models, aligning people, processes and technology to achieve agility, efficiency and growth.
We help you go beyond incremental improvement to achieve transformation that lasts.
Finance & Accounting (F&A)
Procurement
Human Resources (HR)
Customer Experience (CX)
Facilities Management
Industry-specific Operations
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Learn MoreCollaborative Supply Chain Forecasting (CSCF) is a business discipline involving multiple supply chain stakeholders, including suppliers, distributors and sellers, that is aimed at producing a more accurate forecast of future demand and supply. Participants share supply and demand data, forecasts, assumptions and insights to improve visibility, reduce inefficiencies and attenuate supply-demand mismatches. ISG asserts that by 2028, one-third of larger enterprises will have adopted some form of CSCF to reduce costs and improve customer service and supplier relations. However, there are serious challenges to widespread adoption.
The emergence of natural language analytics interfaces driven by generative artificial intelligence (GenAI) models has accelerated enterprise initiatives to enable data democratization—making data available to business decision-makers without the need to train them to use business intelligence (BI) tools. It has also heightened the need for agreed semantic models and business metrics, as well as technologies that facilitate the sharing and consumption of data as a product. As I previously discussed, data as a product is the process of applying product thinking to data initiatives to ensure the outcome—the data product—is designed to be shared and reused for multiple use cases across the business as it enables enterprises to streamline and accelerate the delivery of analytics and artificial intelligence (AI) initiatives. The market for software that enables the design and delivery of data products is evolving rapidly, especially among providers of data catalog-based data intelligence software.
The idea of customer experience (CX) as a business practice relies heavily on being able to understand and track customer journeys. And that can be tricky because there isn’t a standard definition as to what a journey consists of. For example, inside a contact center the journey is perceived in terms of the steps a customer takes to achieve a particular result. Sometimes that is broadened out to include aspects of the customer’s history, especially when the business wants to use historical data to craft an offer or change a tactic.
The advances in data, analytics, Generative AI (GenAI) and agentic AI are transforming how healthcare and life sciences (HCLS) organizations deliver care, accelerate research, manage operations and engage patients. These innovations are driving measurable improvements in efficiency, accuracy and patient outcomes; however, adoption is not without challenges. Regulatory complexity, talent shortages, operational pressures and the need for responsible AI use demand thoughtful strategies. Organizations that can simultaneously innovate, scale responsibly and work closely with analytics partners to translate analytics goals into tangible outcomes will be the ones to succeed.
The Finance and Accounting Outsourcing (FAO) market in 2025 is undergoing one of the most significant shifts it has seen in the past decade. In contrast to 2024, when discussions surrounding AI, generative AI (GenAI) and advanced analytics primarily focused on pilots and early-stage PoCs, the current landscape is defined by operationalized innovation. Providers have transitioned from experimentation to deployment, embedding these technologies into live delivery environments and demonstrating measurable business impact.
