Index Insider: What Acquisitions Reveal About M&A Priorities in 2H24 and Beyond
M&A has long been a key driver for revenue growth in the IT and business services sector.
ISG helps enterprises minimize risk, reduce stranded costs and unlock hidden value across the entire merger and divestiture lifecycle.
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.
ISG is a leader in proprietary research, advisory consulting and executive event services focused on market trends and disruptive technologies.
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Learn MoreThere is a familiar refrain echoing across boardrooms and pipeline reviews that the channel is underperforming, that partners are not delivering and that ecosystems are noisy and inefficient and somehow past their prime. The implication is that something fundamental has broken. That conclusion is convenient, but it is wrong. The channel is not broken. What is broken is how most organizations design, enable and experience their partners. The failure is not structural. It is experiential.
The emergence of cloud computing has had an enormous impact on all segments of the IT industry, including data platforms. All providers of data platform products have enabled their products to be deployed in the cloud and/or consumed as cloud-hosted managed services. To date, the cloud has arguably had the largest impact on analytic data platforms, where cloud infrastructure led to the emergence of a new analytic data platform architecture: the data lakehouse. Now ubiquitous, the data lakehouse decoupled compute and storage and enabled enterprises to take advantage of data lakes based on cloud object storage combined with open file formats, open table formats and analytic data processing engines. Many data platform providers are now looking to bring some of the same advantages of the data lakehouse to operational data platforms.
IT service management (ITSM) is at an inflection point. For two decades, ITSM platforms have operated as structured systems of record and workflow orchestration layers, capturing tickets, routing tasks and enforcing process consistency. That model assumed humans were the primary actors and automation was deterministic, limited and rule-based. That assumption no longer holds.
What I observed at Enterprise Connect was the disappointing specter of an industry that is collectively missing the opportunity to clarify and define its own future. Despite incredible technological advancements, many companies are instead playing defense against threats that they can’t (or won’t) articulate.
The shift roster has always been a place where strategy becomes personal. It decides who works, when they work and how predictable their lives can be from one week to the next. When scheduling becomes more automated, the consequences show up immediately in worker sentiment, manager workload, overtime spend and service levels. That is why the rise of artificial intelligence (AI)-driven scheduling feels different than other HR technology shifts. It is not just another feature in the stack, because it touches time, fairness and trust all at once.