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.
Returns are one of the most expensive and operationally disruptive parts of retail. You feel the impact every time a warehouse gets backed up with inbound parcels, every time associates spend hours processing returns at the counter, and every time a perfectly good item sits in limbo instead of going back to stock. Most retailers … Read more
Customer behavior is the engine behind every retail outcome — conversion, loyalty, basket size, repeat visits, and even returns. You feel its importance every time a promotion underperforms, every time a shopper abandons a cart, and every time two customers with similar profiles behave completely differently. Most retailers have the data to understand these patterns, … Read more
Store performance is where strategy meets reality. You feel it every time two stores with similar assortments perform differently, every time labor is misaligned with traffic, and every time local conditions shift faster than your reporting cycles can catch up. Most retailers rely on static dashboards, weekly reports, or manual analysis that can’t keep pace … Read more
Inventory is the heartbeat of retail. You feel it every time a shelf goes empty, every time a popular SKU sells out faster than expected, and every time excess stock ties up cash that should be working elsewhere. Most replenishment processes rely on static rules, manual overrides, or outdated forecasts that can’t keep up with … Read more
Product descriptions are one of the quietest but most influential drivers of retail performance. You see their impact every time a shopper hesitates because the details aren’t clear, every time a product page underperforms despite strong demand, and every time merchants scramble to rewrite descriptions for new assortments. Most teams rely on manual writing, outdated … Read more
Pricing is one of the most powerful levers in retail, but also one of the hardest to manage. You feel the tension every time demand shifts unexpectedly, competitors adjust their prices, or inventory levels swing faster than your teams can respond. Most retailers still rely on static price lists, manual overrides, or weekly pricing cycles … Read more
Population health is where clinical care meets strategy. You feel its weight every time you look at rising chronic‑disease rates, avoidable ED visits, gaps in preventive care, and the uneven outcomes across different patient groups. Most organizations have the data to understand these patterns, but it’s scattered across EHRs, claims systems, registries, and social‑determinant sources … Read more
Diagnosis is one of the most cognitively demanding parts of medicine. You see the strain every time a clinician juggles symptoms, labs, imaging, history, and comorbidities under time pressure. Even the best clinicians face information overload, fragmented data, and subtle presentations that are easy to miss. AI‑driven diagnostic support gives you a way to surface … Read more
Patient communication is one of the most underestimated drivers of care quality. You see its impact every time a patient misses an appointment, forgets a medication change, misunderstands discharge instructions, or feels lost between visits. Most communication workflows rely on manual outreach, generic templates, or EHR messages that patients rarely read. AI‑driven patient communication automation … Read more
Care plans are the connective tissue of patient care. You feel their importance every time a patient transitions between settings, every time multiple clinicians need to stay aligned, and every time a chronic condition requires long‑term management. Yet most care plans are either too generic to be useful or too time‑consuming for clinicians to update … Read more