Cloud & AI for the Enterprise
The Adoption Playbook Library provides clear, operational guidance for implementing AI capabilities in real environments. Each playbook follows a consistent structure so leaders can understand what the use case is, why it works, the data required, and how to execute the first 30 and 90 days with confidence.
The goal is to give organizations a practical, repeatable path for moving from concept to measurable results.
Every playbook is grounded in real workflows. You’ll see the conditions that support early wins, the friction points that slow adoption, and the success patterns that appear across industries and functions. This creates a reliable framework for planning, sequencing, and scaling AI initiatives.
Each theme reflects a major enterprise function or domain where AI adoption requires structured guidance.
Operational guidance for deploying AI across service channels, ticketing workflows, routing, quality monitoring, and agent productivity.
Practical steps for implementing AI in forecasting, pipeline management, lead scoring, account intelligence, and sales enablement.
Deployment patterns for content workflows, analytics, personalization, campaign optimization, and audience intelligence.
Guidance for automating document intake, classification, extraction, summarization, compliance review, and knowledge retrieval.
Playbooks for implementing AI‑driven insights, decision support tools, scenario modeling, and real‑time analytics.
Structured approaches for forecasting, scheduling, optimization, quality, logistics, and cross‑functional operational workflows.
Guidance for talent workflows, skills intelligence, workforce planning, employee support, and productivity tools.
Deployment steps for forecasting, reconciliation, reporting, risk analysis, and financial operations.
Patterns for implementing AI in infrastructure management, incident response, security operations, and IT workflows.
Targeted guidance for sectors with unique operational requirements, including: Manufacturing, Financial Services, Healthcare, Retail, Energy & Utilities, Oil & Gas, Public Sector, Media & Entertainment, Life Sciences, Technology, Telecom, Consumer Goods, and Travel & Hospitality.
The grid below contains all published playbooks. Each one follows a consistent structure with clear explanations, operational steps, and early‑stage execution guidance. You can read them in any order. Every playbook outlines what the use case is, why it works, the data required, the first 30 and 90 days, common pitfalls, and the success patterns that appear across organizations.
These playbooks form a core component of the Enterprise AI & Cloud Value Index, giving leaders a practical framework for deploying AI capabilities with clarity, discipline and ROI in mind.
The Adoption Playbook Library provides clear, operational guidance for implementing AI capabilities in real environments. Each playbook follows a consistent structure so leaders can understand what the use case is, why it works, the data required, and how to execute the first 30 and 90 days with confidence.
The goal is to give organizations a practical, repeatable path for moving from concept to measurable results.
Every playbook is grounded in real workflows. You’ll see the conditions that support early wins, the friction points that slow adoption, and the success patterns that appear across industries and functions. This creates a reliable framework for planning, sequencing, and scaling AI initiatives.
Each theme reflects a major enterprise function or domain where AI adoption requires structured guidance.
Operational guidance for deploying AI across service channels, ticketing workflows, routing, quality monitoring, and agent productivity.
Practical steps for implementing AI in forecasting, pipeline management, lead scoring, account intelligence, and sales enablement.
Deployment patterns for content workflows, analytics, personalization, campaign optimization, and audience intelligence.
Guidance for automating document intake, classification, extraction, summarization, compliance review, and knowledge retrieval.
Playbooks for implementing AI‑driven insights, decision support tools, scenario modeling, and real‑time analytics.
Structured approaches for forecasting, scheduling, optimization, quality, logistics, and cross‑functional operational workflows.
Guidance for talent workflows, skills intelligence, workforce planning, employee support, and productivity tools.
Deployment steps for forecasting, reconciliation, reporting, risk analysis, and financial operations.
Patterns for implementing AI in infrastructure management, incident response, security operations, and IT workflows.
Targeted guidance for sectors with unique operational requirements, including: Manufacturing, Financial Services, Healthcare, Retail, Energy & Utilities, Oil & Gas, Public Sector, Media & Entertainment, Life Sciences, Technology, Telecom, Consumer Goods, and Travel & Hospitality.
The grid below contains all published playbooks. Each one follows a consistent structure with clear explanations, operational steps, and early‑stage execution guidance. You can read them in any order. Every playbook outlines what the use case is, why it works, the data required, the first 30 and 90 days, common pitfalls, and the success patterns that appear across organizations.
These playbooks form a core component of the Enterprise AI & Cloud Value Index, giving leaders a practical framework for deploying AI capabilities with clarity, discipline and ROI in mind.
Telecom operators face some of the highest churn pressures of any industry. Customers switch providers for reasons that are often subtle — small drops in network quality, confusing bills, poor support interactions, or unmet expectations around speed and reliability. Traditional churn models rely on lagging indicators and generic segmentation, which means interventions come too late. … Read more
Telecom infrastructure is vast, distributed, and constantly exposed to weather, load fluctuations, and physical wear. Towers degrade, fiber routes experience micro‑faults, and 5G radios require precise calibration to maintain performance. Traditional maintenance models rely on scheduled inspections or reactive repairs, which lead to unnecessary truck rolls, unexpected outages, and higher operational costs. AI gives telecom … Read more
Telecom networks are growing more complex every year. 5G densification, fiber expansion, virtualized network functions, and edge deployments create an environment where manual monitoring simply can’t keep up. Congestion, outages, and performance degradation can happen in seconds, and customers expect flawless connectivity. AI gives network operations teams a way to predict issues before they occur, … Read more
Security teams in technology companies face an overwhelming volume of alerts, rapidly evolving threats, and increasingly complex environments. Manual triage slows response times, false positives drain attention, and sophisticated attacks slip through traditional detection methods. AI gives security leaders a way to detect threats earlier, automate investigation steps, and coordinate response with far greater precision. … Read more
Support organizations inside technology companies are under pressure from every direction. Ticket volumes rise as products grow more complex. Customers expect instant answers. Knowledge bases become outdated faster than teams can maintain them. Meanwhile, support agents spend too much time searching for information, rewriting the same responses, and escalating issues that could have been resolved … Read more
Cloud spending has become one of the largest and least predictable line items for technology companies. As architectures shift toward microservices, multi‑cloud deployments, and usage‑based pricing, costs become harder to understand and even harder to control. Engineering teams often lack visibility into the financial impact of their decisions, and finance teams struggle to forecast spend … Read more
Revenue teams in technology companies operate in an environment where deal cycles are unpredictable, customer expectations shift quickly, and competitive pressure is constant. Forecasts often rely on subjective judgment, CRM data is incomplete, and deal risks surface too late. AI gives sales and revenue operations leaders a way to analyze patterns across pipeline activity, product … Read more
Engineering teams in technology companies face constant pressure to deliver more with less. Backlogs grow faster than teams can clear them, technical debt accumulates, and onboarding new engineers takes too long. AI‑powered engineering copilots give teams a way to reduce repetitive work, improve code quality, and accelerate delivery without burning people out. When implemented with … Read more
Customer success teams in technology companies carry the weight of renewals, expansion, and long‑term account health. As products grow more complex and customer bases scale, it becomes harder to understand which accounts need attention, which are ready for growth, and which are quietly drifting toward churn. AI gives customer success leaders a way to analyze … Read more
Technology companies live and die by reliability. Outages damage trust, slow growth, and drain engineering time that should be spent on product innovation. As systems grow more distributed and complex, traditional monitoring and manual triage can’t keep up. AI gives SRE and platform teams a way to detect anomalies earlier, understand root causes faster, and … Read more