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.
Advertisers are more sensitive than ever to where their messages appear. One misplaced ad next to harmful, controversial, or misaligned content can damage brand trust and trigger costly pullbacks. Media companies face the same pressure — you’re balancing monetization with safety, compliance, and audience expectations. Manual review can’t keep up with the volume of content … Read more
Subscription businesses run on a simple truth: growth is meaningless if churn is uncontrolled. You’re managing rising acquisition costs, shifting viewer habits, content saturation, and fierce competition across streaming, news, gaming, and creator platforms. Traditional churn analysis relies on lagging indicators — cancellations, complaints, or expired payment methods — which means you only react after … Read more
Rights and contracts are the backbone of the media business. Every piece of content — a show, a song, a clip, a character, a likeness, a logo — is governed by layers of agreements that determine where it can be distributed, how it can be monetized, and what obligations must be met. Yet most organizations … Read more
Ad‑supported businesses live and die by yield. You’re managing fluctuating demand, fragmented inventory, seasonality, platform constraints, and pressure to maximize revenue without degrading user experience. Traditional yield management relies on static rate cards, manual pacing, and reactive adjustments that leave money on the table. An AI‑driven ad inventory yield management and dynamic pricing capability helps … Read more
Studios, networks, and streaming platforms all face the same pressure: you’re investing heavily in content, but audience attention is unpredictable. A show can spike overnight or disappear after one episode. Release timing, competitive drops, platform algorithms, and shifting viewer habits all influence performance. Traditional forecasting relies on historical averages and executive intuition, which can’t keep … Read more
Creative is still the single biggest driver of campaign performance — but it’s also the slowest, most expensive part of the workflow. You’re juggling tight deadlines, fragmented channels, endless format requirements, and the pressure to personalize at scale. Creative teams are stretched thin, and marketers often rely on a handful of generic assets because producing … Read more
Audience attention is fragmented across platforms, formats, and devices. You’re managing shifting viewer habits, privacy constraints, rising acquisition costs, and pressure to deliver measurable ROI. Traditional segmentation methods — demographic buckets, static personas, broad affinity groups — no longer reflect how people actually behave. An AI‑driven audience segmentation and predictive targeting capability helps you understand … Read more
Public safety agencies operate under constant pressure. You’re managing emergency response, crime prevention, community outreach, resource deployment, and compliance — all while dealing with rising call volumes, staffing shortages, and public expectations for transparency. Traditional reporting tools can’t keep up with the real‑time data coming from CAD systems, RMS databases, sensors, and community channels. An … Read more
Government agencies are constantly balancing limited staff against rising service demands. You’re managing vacancies, overtime, seasonal surges, compliance deadlines, and unpredictable public needs — all while trying to maintain service quality. Traditional workforce planning relies on spreadsheets, intuition, and historical averages that don’t reflect real‑time conditions. An AI‑driven workforce allocation capability helps you deploy staff … Read more
Government programs operate under strict rules — eligibility criteria, reporting requirements, financial controls, safety standards, procurement regulations, and statutory mandates. Yet compliance teams are often understaffed, working across legacy systems, and reviewing thousands of records manually. Issues slip through, audits become reactive, and agencies struggle to maintain consistent oversight. An AI‑driven compliance monitoring capability helps … Read more