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.
Coding is one of the most detail‑intensive steps in the healthcare revenue cycle. You feel the pressure every time a claim is delayed, every time documentation doesn’t match the codes submitted, and every time coders spend hours searching through dense clinical notes to find the right details. Most EHRs weren’t built to translate clinical language … Read more
Prior authorization has become one of the most painful bottlenecks in healthcare. You see it in delayed treatments, frustrated clinicians, overwhelmed utilization‑management teams, and patients stuck waiting for approvals that should take minutes, not days. Most of the friction comes from manual document review, inconsistent payer requirements, and EHR workflows that weren’t designed for real‑time … Read more
Clinical documentation has become one of the biggest sources of friction in healthcare. You see it in the late‑night charting, the rushed notes between patient visits, and the administrative backlog that keeps clinicians from focusing on care. Most EHR systems capture data, but they don’t help clinicians synthesize it into clear, usable summaries. AI‑generated clinical … Read more
AML teams are under constant pressure. You’re dealing with rising transaction volumes, increasingly sophisticated laundering techniques, and regulators who expect faster detection with fewer false positives. Most institutions rely on rule‑based systems that generate overwhelming alert queues, leaving analysts buried in noise while real risks slip through. AI‑driven AML monitoring gives you a way to … Read more
Customers expect more from their financial institutions than static dashboards and generic advice. You see it in the questions they ask, the uncertainty they feel about saving or investing, and the frustration that comes from one‑size‑fits‑all recommendations. Most institutions have the data to offer personalized guidance, but it’s scattered across accounts, transactions, credit profiles, and … Read more
Claims are the moment of truth in insurance. You feel the pressure every time a customer submits a claim and expects a fast, fair decision. Behind the scenes, adjusters are juggling documents, policy details, loss descriptions, photos, and third‑party reports — often across systems that don’t talk to each other. Manual review slows everything down, … Read more
Regulatory pressure in financial services keeps rising, and you feel it in every audit cycle, every new rule interpretation, and every scramble to assemble reports from systems that were never designed to work together. Compliance teams spend countless hours stitching data from core banking platforms, trading systems, CRM tools, and manual spreadsheets. The work is … Read more
Customer expectations in financial services have shifted sharply. People want fast, accurate answers about their accounts, claims, policies, and transactions — and they want those answers without waiting on hold or navigating confusing menus. At the same time, service teams are under pressure from rising inquiry volumes, complex products, and legacy systems that make it … Read more
Credit decisions sit at the center of every lending business. You feel the pressure from customers who expect instant approvals, regulators who expect fairness and transparency, and portfolio leaders who expect consistent risk discipline. Traditional underwriting relies on a mix of bureau data, manual reviews, and static scorecards that struggle to keep up with new … Read more
Fraud pressure in financial services has never been higher. You’re dealing with faster payment rails, more digital touchpoints, and customers who expect instant decisions with zero friction. At the same time, fraudsters are adapting quickly, using automation, synthetic identities, and coordinated attacks that slip past traditional rule‑based systems. Most institutions know their fraud controls are … Read more