ISG helps enterprises minimize risk, reduce stranded costs and unlock hidden value across the entire merger and divestiture lifecycle.
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Mergers, acquisitions and divestitures are complex – and costly when mishandled. Common pitfalls include:
Transitional service agreement (TSA) delays and late notices driving unexpected costs
High stranded IT and license costs
Missed or misplaced contracts slowing Day 1 readiness
Poor communication with suppliers and new entities
Insufficient resources to manage execution
ISG helps you sidestep these risks with early vendor engagement, our proven playbook and expert contract lifecycle management.
Whether you’re planning an acquisition, executing a divestiture, or stabilizing operations post-Day 1, ISG supports you at every step.
Our proven methodology:
Assess – Opportunity scans (looking for areas to improve), due diligence (clearly defining what is in scope and the state of the supplier contract landscape ) and financial impact analysis (impact to current costs and planning for the transition costs).
Design – Strategy and target operating model development, detailed definition of service and program planning included.
Integrate/Separate – Contract separation, TSA planning, program management and systems transition.
Transform – License optimization, process redesign and synergy / non-synergy capture.
Operate – Governance, risk mitigation and ongoing optimization.
Every transaction is different. We tailor our approach to deliver value for mergers, spin-offs, and everything in between.
When the stakes are highest, global enterprises trust ISG. With more than $475B in sourcing deals advised and experience across thousands of complex integrations and separations, we bring unmatched data, independence and expertise.
AI investment is accelerating, but results remain uneven. Only one in four initiatives is meeting revenue impact expectations, at an average spend of $1.3M per use case. Enterprises are no longer asking whether AI works. They are being asked to prove that it pays.
We help you identify where AI agents deliver the most value, restructure workflows around them and build the accountability models that keep autonomous execution auditable. The enterprises that win won't be the ones that reacted. They'll be the ones that designed for it first.
We give enterprises transparent, benchmarkable pricing models that tag each resource unit with the autonomy level used to deliver it. As AI capability advances, your pricing keeps pace. Both buyers and providers can quantify what that progress is worth.
We bring analysis of more than $2.6 billion in tracked AI spend to every sourcing decision. Procurement, technology and finance leaders get the independent intelligence to rationalize vendor portfolios and hold providers accountable to measurable outcomes.
We embed controls at the point of data creation, define accountability for autonomous actions and build adaptive frameworks that keep pace with AI without impeding it. Enterprises that get this right don't just manage risk. They build the trust that lets them scale faster.
We ground strategy in research across 2,400 enterprise use cases, aligning investment to where impact is proven and designing the data, talent and governance foundations that move AI from pilots into the workflows that drive commercial results.
We benchmark your AI readiness against peers across 75 countries, identify the dimensions holding you back and give you a personalized roadmap to close the gap.
AI investment is shifting decisively toward revenue-generating functions. CRM automation, sales enablement and forecasting have replaced chatbots and IT productivity tools as the leading use case priorities, reflecting enterprise recognition that productivity gains alone do not satisfy board-level scrutiny. At the same time, use cases in production have doubled since 2024, and the portfolio is diversifying rapidly, with over 300 distinct function and industry-specific use cases now in active deployment.
ISG research across 2,400 enterprise use cases shows that the strongest AI returns are currently concentrated in compliance, risk management and quality control, not in the growth and cost outcomes most enterprises originally set out to achieve
The gap between where enterprises are investing and where AI is actually delivering is the defining commercial tension of 2025. Organizations that close it by targeting functions with structured, revenue-attributable data and clear ROI measures will establish performance benchmarks that compress the window for competitors still cycling through pilots. The standard is being set now.
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Learn MoreThe growing strategic importance of sales and operations planning (S&OP) reflects the confluence of two major trends evolving outside of enterprise operations. One is the ongoing disintegration of the post-World War 2 liberal trade environment that began in the early 2010s and has accelerated since. The second is the increasing sophistication and approachability of S&OP software. These applications are designed to align an enterprise’s sales and marketing objectives with its operating environment (including its production and distribution assets as well as its supplier ecosystem) while respecting financial constraints. The availability of more accurate and nuanced machine learning (ML)-based forecasting systems and the growing availability of agents that shorten planning and execution cycles will enable enterprises using software to better adapt to change, anticipate risks and evaluate trade-offs to determine the best course of action faster with greater intelligence.
There is a growing disconnect between what digital commerce platforms were originally built to do and what modern revenue teams now need them to deliver. Most traditional platforms were designed to manage storefronts, catalogs and transactions. They perform well when the goal is to display products, enforce static pricing and process payments. But in today’s revenue environment, the challenge is no longer limited to completing a transaction. It is about orchestrating revenue across multiple moments, touchpoints and decision makers over time. As organizations adopt recurring revenue models, usage-based pricing and faster deal cycles, commerce technology that cannot adapt to these realities becomes a constraint on growth rather than a driver of it.
The mission to enable an autonomous enterprise, as I have articulated, remains aspirational for most organizations. Few have aligned business and technology leadership, strategy and execution to support autonomous operations at any scale. A shared definition is essential. An autonomous enterprise follows an operating model in which intelligent systems can sense conditions, decide actions, execute outcomes and continuously learn, that is orchestrated across the business, with humans setting intent, governance and accountability.
The U.S. healthcare environment is undergoing a structural shift as economic pressure, demographic change and technological acceleration reshape how payers, providers and life sciences organizations operate. With healthcare already representing 17 percent of the region’s GDP and advancing toward a 20 percent share by 2032, rising medical demand is paralleled by an unprecedented expansion in technology investment. Aging populations and the growing burden of chronic disease are increasing healthcare consumption, while intensifying cost pressures are heightening the urgency for new digital operating models that reduce friction and enable more sustainable delivery.
The manufacturing industry is undergoing a seismic shift, driven by the convergence of digital technologies, sustainability imperatives and evolving customer expectations. As manufacturers strive to modernize legacy systems, integrate emerging technologies and build resilient supply chains, they face a complex landscape of challenges and opportunities. This transformation is not uniform across geographies or company sizes. Mid-tier and specialist IT firms must navigate a complex landscape to deliver scalable, costefficient and domain-specific solutions.
