M&A and Divestiture Services

There’s a subtle but important truth I’ve seen play out across dozens of organizations: HR software doesn’t define your strategy—but it will expose whether you have one. Too often, organizations treat the HR software stack as a series of independent decisions: a new ATS here, a learning platform there, maybe a refreshed core HRIS every few years. Each decision is justified on its own. Each serves a real need. But when you step back, the result is a patchwork of applications that don’t align, don’t communicate and don’t reinforce a coherent strategy.

Enterprises are accelerating digital initiatives powered by APIs, services, IoT and agentic artificial intelligence (AI), expanding non-human identity (NHI) risk faster than legacy Identity Access Management (IAM) and Privileged Access Management (PAM) can govern. CIOs, CISOs and IT leaders need an identity strategy that treats NHIs as first-class identities, integrates with IAM/PAM and advances enterprise security posture management. Purpose-built NHI software provides visibility, lifecycle automation and least privilege across machine identities, reducing credential exposure and compliance gaps. This analyst perspective explains why specialized NHI solutions are required to complement IAM and unify governance across human and non-human identities.

There's a saying among military historians that amateurs debate strategy while professionals talk about logistics. In a similar vein, those rooted deeply in the workings of information technology (IT) pay close attention to data because it is fundamental to the proficiency and effectiveness of any IT system. Visionaries speak of data being the oil or gold of the information economy; practitioners recognize that it is the structural steel. Potentially, one of the unexpected benefits of the artificial intelligence (AI) revolution may be the ability and willingness to address the decades-long problems of data quality and availability. More powerful data management tools and the motivation to invest in and use them are likely to significantly reduce (but by no means eliminate) these issues. This, as much as the power of AI—in all its forms—to streamline and even automate business process execution, will lead to a step-function increase in the productivity of the Office of Finance.

Collaborative 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.