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This report explores the seismic shift unfolding in the consulting landscape as generative AI advances from experimental novelty to core service enabler. In the face of rising client expectations for faster insights, hyper-personalized recommendations, and outcome-based pricing, traditional time-and-materials models are giving way to AI-driven asset-based delivery. By surveying the state of large language models, agent architectures, and emerging multi-agent orchestration platforms, the report highlights why consulting firms must move quickly to integrate generative AI into both client-facing solutions and internal workflows.
Drawing on analyses of leading providers’ strategies – from Bain’s OpenAI partnership and PwC’s enterprise-grade deployments to Accenture’s multi-billion-dollar AI investments – the report offers a clear, two-tiered roadmap. First, act now use cases to deliver immediate value through automated data cleansing, AI-powered reporting bots, and scenario-generation tools. Second, invest and experiment initiatives to prepare firms for next-generation capabilities such as autonomous advisory assistants and end-to-end AI platforms. Together, these recommendations equip consultants to unlock measurable productivity gains, reduce cost overruns, and identify new revenue streams.
Whether you lead a strategic practice, oversee technology implementation, or direct data advisory teams, this report provides actionable frameworks to prioritize AI investments, redesign talent mixes, and evolve operating models. Readers will gain practical guidance on governance and risk/change management to ensure that AI deployments not only accelerate delivery but also uphold quality, compliance, and ethical standards.
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In today’s dynamic innovation landscape, Research, Development, and Innovation (RD&I) teams face the dual challenge of accelerating time-to-market while operating under strict budgets. Traditional portfolio management approaches rely on manual processes and fragmented data, often resulting in delayed, misaligned, or suboptimal decisions.
Systems of Execution (SoE) represent a transformative approach to pipeline and portfolio prioritization. By leveraging agentic AI for contextual decision-making, automating execution workflows, and enabling real-time data integration across systems, SoE close the gap between insight and action. This Viewpoint presents a four-pillar framework covering AI orchestration, process automation, data architecture, and governance readiness.
The result: improved resource allocation, better alignment with strategic goals, faster portfolio responsiveness to market shifts, and minimized costly missteps. With providers accelerating SoE deployments, RD&I leaders have an opportunity to rethink prioritization at an enterprise scale.
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In recent years, life sciences enterprises have reprioritized and overhauled their R&D investments. They now focus on streamlining their pipelines and shifting toward new and large molecules. This transformation has compelled aggressive cost containment initiatives, prompting organizations to seek innovative commercialization strategies for their drugs and devices. Additionally, the growing focus on patient-centricity and customer experience has accelerated outsourcing to specialized providers.
Providers are essential in this transformation. They offer expertise in generative AI, advanced analytics, and AI/ML, to streamline workflows and maximize efficiency. In response to market needs, they have expanded their portfolios to offer comprehensive, end-to-end solutions supporting pre-launch and post-launch commercialization activities.
In this report, we assess 30 providers featured on the Life Sciences AI and Analytics Commercial Services PEAK Matrix® Report. Each provider profile highlights the service focus, key IP/solutions, domain-specific investments, and case studies.
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Agentic AI is fast becoming the most talked-about breakthrough in enterprise technology, promising transformative outcomes across cost, efficiency, and revenue generation. Yet as organizations explore its potential, they also face rising uncertainty around trust, data governance, ROI, and a rapidly evolving vendor ecosystem.
Watch Everest Group experts Chirajeet Sengupta and Anil Vijayan as they unpack the current state of agentic AI adoption, the pressing considerations for enterprises, and what the road ahead may look like. This session offered insights into where agentic AI is delivering value, how leaders are managing key risks, and what changes we can expect in the next 2–5 years. Whether you’re an enterprise executive or a technology provider, this is your chance to separate signal from noise and understand the trajectory of this powerful innovation.
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Custom Application Development (CAD) continues to be a strategic priority for enterprises seeking digital agility, scalability, and competitive differentiation. Despite challenges such as geopolitical instability, economic uncertainty, and talent shortages, demand for customized solutions remains strong. Enterprises are embracing platform-centric approaches, modern development methodologies, and AI-augmented tools to streamline delivery and meet evolving user expectations. In this context, providers are not only expected to deliver engineering talent but also act as transformation partners with deep domain expertise and a focus on business outcomes.
This report profiles 31 leading CAD providers and assesses how they are helping enterprises navigate today’s complex IT and business environment. Each provider profile includes details on service focus, proprietary solutions, recent investments, and case studies. The analysis also highlights how providers are differentiating through low-code platforms, CI/CD tools, and AI-powered development assistants. This compendium supports decision-makers in identifying partners that align with their digital and operational priorities.
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Private Equity (PE) firms are navigating a shifting landscape marked by rising borrowing costs, regulatory pressures, and evolving limited partner expectations. In response, general partners are emphasizing creating operational value over traditional financial engineering. IT and BPS have become central to this strategy, enabling faster transformation, scalable operations, and enhanced reporting. The report highlights how PE firms are embedding digital, AI, ESG, and cloud capabilities across the investment life cycle, from diligence to exit, to accelerate returns and differentiate in a competitive fundraising environment.
With deal execution slowing, creating value within portfolio companies has become a top priority. This report examines demand trends across sectors such as healthcare, SaaS, FinTech, and industrial technology, where digital readiness and regulatory momentum drive investments. It also outlines emerging PE models and offers practical guidance for providers to build scalable, outcome-linked solutions that align with PE firms’ unique strategies. Through sector-specific insights and a PE value impact framework, the report provides actionable strategies to navigate today’s complex market dynamics.
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Capital market firms face pressure from limited revenue growth, rising costs, and stringent regulatory mandates. Despite margin constraints, these firms are investing selectively in technology areas such as algorithmic trading platforms, AI-driven analytics, cloud-native platforms, security operations centers, and real-time data pipelines to improve efficiency, resilience, and client experience.
This report explores enterprise priorities across key capital markets segments including equities, fixed income, derivatives, and asset management. It examines how generative AI is reshaping trading strategies and how cloud adoption is enabling scalable data architectures. The report also reviews cybersecurity challenges linked to digital asset custody and real-time settlement, as well as how firms are addressing them.
Regional insights cover regulatory drivers such as MiFID II updates in Europe, DORA compliance, T+1 adoption in the US, and digital exchange developments in APAC. The report assesses how IT providers are aligning their portfolios to deliver specialized capital market IT solutions. Examples include low-latency market data distribution, algorithmic trading engine modernization, AI-driven risk management platforms, and post-trade reconciliation automation. It also highlights the importance of domain expertise and integrated managed services in meeting evolving enterprise expectations.
As outsourcing activity stabilizes after a period of caution, the report outlines growth opportunities for 2025 and beyond – including managed platform services, automation-led transformation, and data-as-a-service offerings.
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Europe’s digital and regulatory environment is reshaping how cloud services are consumed and governed. With growing geopolitical tensions, increasing data protection mandates, and rising concerns around foreign surveillance laws, the sovereign cloud has become central to enterprise and public sector digital strategies.
This report examines the sovereign cloud market in Europe through strategic, regulatory, and operational lenses. It outlines the drivers pushing sovereignty from compliance to a digital architecture priority, including the importance of AI and data security, jurisdictional clarity, and operational autonomy. The report introduces a detailed sovereignty framework across data, operational, and technical dimensions and evaluates three dominant delivery models: hyperscaler-led, datacenter-led, and national sovereign platforms. It highlights enterprise experience gaps, outlines enterprise-grade sovereignty, and shares implications for enterprises, service providers, and technology providers shaping Europe’s digital sovereignty future.
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AI is moving from isolated pilots to enterprise-wide initiatives, but infrastructure strategies often lag. Many organizations face compute constraints, fragmented tooling, and unclear deployment or sourcing approaches. As AI workloads grow in scale and complexity, the need for purpose-built, scalable infrastructure aligned with business goals becomes essential.
This report introduces AIMPACT, Everest Group’s framework for structured AI infrastructure planning. It offers a clear lens on how enterprises can profile AI workloads, assess readiness, and make informed decisions on deployment models, sourcing strategies, and stack design. The report also explores the broader shifts influencing how infrastructure is designed, operationalized, and governed in an AI-first world.
This report provides a practical roadmap to move from fragmented pilots to scalable execution. CIOs, infrastructure leaders, AI teams, and service partners can use this report to bring structure to decision-making, align technology investments with business goals, and build infrastructure strategies that are grounded, adaptive, and future-ready.
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Agentic AI is an evolved form of artificial intelligence that enables autonomous agents to set goals, make decisions, and take adaptive actions independently. These agents can decompose complex workflows into manageable subtasks, align their actions with defined objectives, and execute processes with minimal human oversight. By combining real-time reasoning, contextual memory, and the ability to interact with diverse enterprise systems, agentic AI allows for truly end-to-end, goal-driven automation.
This new paradigm surpasses the capabilities of traditional intelligent automation and generative AI by enabling autonomous, context-aware systems that have the potential to make processes more time-efficient and less dependent on humans. As a result, agentic AI is rapidly gaining traction across industries and business functions.
In this report, we examine the global agentic AI market, including the evolution of agentic automation, market characteristics, and the adoption landscape segmented by geography, industry, buyer size, and business process. We evaluate key solution characteristics such as training and support, partner ecosystems, deployment models, and commercial models. Additionally, we analyze the competitive landscape along with exploring emerging technology trends shaping the agentic ecosystem, such as large action models, model context protocol, agent-to-agent communication, and neuro-symbolic programming.
