As marketing becomes more digital, dynamic, and data-intensive, enterprises face growing pressure to deliver personalized, real-time experiences that drive measurable results. Traditional systems of record and engagement merely store data or manage interactions, but they do not convert insights into action. Despite access to rich customer insights, marketing teams remain burdened by disconnected tools, manual processes, and delayed execution. Systems of Execution address this gap by processing behavioral signals, applying AI-driven decision-making, and autonomously orchestrating actions across the marketing value chain. This report explores how Systems of Execution transform fragmented MarTech environments into unified, AI-powered ecosystems that act, automating personalization, journey orchestration, and campaign optimization across touchpoints. Designed for CMOs and CIOs, the report highlights how Systems of Execution enables adaptive engagement, accelerates decision-making, and improves key outcomes, from customer lifetime value to marketing RoI. Scope All industries and geographies Contents In this report, we examine: What Systems of Execution means for marketing Pre- Systems of Execution and post- Systems of Execution tech stack Functional transformation through Systems of Execution in marketing The Systems of Execution implementation roadmap in marketing Strategic Systems of Execution considerations for CMOs and CIOs

Life sciences companies are at a major crossroads: The fruits of the traditional blockbuster model have been consumed, and enterprises realize that the way forward is a targeted approach to diseases. Traditional business and operating models are being reviewed and often replaced by new strategies designed to accommodate the rapidly evolving and globalized marketplace. Supply chains are already experiencing disruption across industries, catalyzed by on-demand delivery models and cost optimization drives. The ripple effects of these changes are also being felt on life sciences supply chains. The traditional hub-and-spoke model is no longer sufficient to meet the needs of the life sciences industry, as it gradually shifts to a precision medicine model. However, challenges such as the lack of end-to-end visibility, rampant counterfeiting and theft, as well as process inefficiencies, still need to be addressed to make supply chains resilient. This report recommends our ADAPT framework for life sciences supply chains to adopt and adapt according to business requirements and deliver enhanced value to patients. Scope Geography: global Industry: life sciences Sources leveraged: expert analyst inputs, Everest Group research, publicly available secondary data sources Contents In this research, we analyze the life sciences supply chain market across the following dimensions: Current state of life sciences supply chains Impact of COVID-19 on life sciences supply chains Everest Group ADAPT framework for life sciences supply chains Life sciences enterprise supply chain initiatives Membership(s) Life Sciences IT Services (ITS)

An industry traditionally slow to adopt technology is now clamoring to become digital. Stakeholders in the life sciences industry are now asking what technology can do for their organizations and how they can automate processes to enable their staff to focus on strategic activities to improve business outcomes. Life sciences enterprises are finally starting to adopt intelligent automation. Recent turbulences, from political crackdowns on price rises to changing regulations to evolving business models, have expedited automation adoption so that these companies can do more with less manual intervention. The intelligent automation ecosystem itself is evolving from basic automation for transactional tasks toward intelligent automation for judgment-intensive processes. RPA and AI solutions are augmenting each other to achieve desired business outcomes. In this viewpoint, we evaluate the current level of intelligent automation adoption in the life sciences industry and assess the growth potential over the next five years. Content Executive summary Current state of the life sciences industry Intelligent automation in life sciences Market size and growth potential of the global life sciences intelligent automation market Key challenges Key considerations for enterprises to successfully adopt intelligent automation Conclusion Membership(s) Life Sciences IT Services (ITS) Service Optimization Technologies (SOT)