Use Cases
69 items
- Use Case
AI in facilities management: 10 use cases for the connected workplace
The best-documented result in AI for building operations reduced data-center cooling energy by up to 40 percent — inside the most heavily instrumented buildings on earth. This guide ranks ten facilities AI use cases by how well they survive contact with an ordinary commercial portfolio, and gives facilities and real-estate leaders a buy order grounded in verifiable evidence rather than vendor decks.
- Use Case
11 Generative AI use cases in R&D that actually made it to production
Generative AI is in production in R&D wherever three conditions hold: the output is independently checkable, the domain has deep structured data, and a human or physical validation step sits between model and consequence. Eleven use cases meet that bar today — anchored by peer-reviewed results like AlphaFold and GNoME, not vendor decks — and the rest are still pilots.
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Employee Service Agents: HR Help Desks, Onboarding, and IT Support
Employee service agents — HR help desks, onboarding orchestration, and IT support — are the most tractable internal agent deployments: demand is repetitive, the answers are already written down, and the systems of record expose APIs. The decision that matters is the line between answering and acting: buy the answer layer from the suite you already own; govern the action layer — resets, provisioning — like production identity infrastructure.
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AI for HR and Talent: Recruiting, Performance, Retention, and Compliance
AI now touches every stage of the employee lifecycle: screening candidates, drafting performance reviews, predicting attrition, and recommending internal moves. The technology is mature enough to buy; the evidence base and the law are what should shape how you buy it. This guide maps the four HR AI workloads, the evaluation questions that matter for each, and the compliance layer that spans them all.
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AI in Finance and Procurement: FP&A, AP Automation, and Source-to-Pay
Finance is where AI's efficiency promise meets the enterprise's hardest control environment. This guide maps the three lanes where AI is actually working in the office of the CFO — FP&A forecasting, accounts payable and expense automation, and source-to-pay procurement — and the sequencing, platform, and SOX-grade control decisions each one forces.
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AI for Customer Service and Success: Agentic Support, Health Scoring, and Sentiment
Customer service is the rare AI use case with a randomized field experiment behind it: a generative assistant raised support-agent productivity 15% on average, with novices gaining most. The decisions that matter now are altitude (assist, deflect, or act), the escalation contract with humans, and whether service signal flows back into renewals and product.
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AI in Supply Chain and Logistics: Forecasting, Inventory, Routing, and Sustainability
Supply chain AI concentrates in four decision lanes: demand forecasting, inventory optimization, transportation routing, and emissions accounting — plus the warehouse automation that executes what they decide. The 2026 buying reality is that hyperscalers have retired or rebranded their turnkey point services, so the durable choices are a planning suite, general ML infrastructure, or a narrow solver API. This guide maps which lane earns which.
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AI for Sales Teams: SDR Agents, Conversation and Revenue Intelligence, Forecasting, and Proposals
Sales and marketing is the business function where firms deploy AI most often — and the one where vendor claims outrun public evidence by the widest margin. This guide maps the five sales-AI workloads, states what verifiable evidence supports each, and gives a sequencing plan: adopt assistive coaching and drafting first, pilot scoring and forecasting against your own baseline, and treat autonomous outreach as an experiment with kill criteria.
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AI for Marketing: Content, Email, SEO, Social, and Analytics
Sales and marketing is the most common business function where AI-adopting firms deploy the technology, and the tool market is correspondingly crowded. This guide maps the five marketing lanes where AI is actually working — content, email, SEO, social, and analytics — and the architecture, procurement, and compliance decisions each one forces.
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Legal AI in Practice: Research, Contracts, Intake, Billing, and the Vendor Landscape
Legal AI works where outputs are checkable and fails where they are citable. Peer-reviewed testing found leading AI legal research tools hallucinate between 17% and 33% of the time[^arxiv-2405-20362], so the deciding factor in every legal AI deployment — research, contracts, intake, billing — is the verification workflow you wrap around the model, not the model itself.
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AI in Manufacturing: Visual Inspection, Predictive Maintenance, Digital Twins, and Vendors
Manufacturing AI concentrates in three production-proven use cases: visual inspection, predictive maintenance, and digital twins. The biggest 2026 buying signal is negative — AWS, Microsoft, and Google have retired most of their turnkey industrial AI services — so the real decision is now between industrial specialists, platform incumbents, and building on general-purpose ML infrastructure you control.
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AI for E-Discovery, Litigation, and Due Diligence
AI earns its keep in litigation-adjacent work in five places: e-discovery document review, privilege screening, legal hold scoping, M&A due diligence, and patent search. The deciding factor is never raw model accuracy — it is whether the workflow around the model survives challenge from a court or opposing counsel. This guide maps each workload to the rules that govern it and the validation discipline that makes AI use defensible.
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AI in Banking and Capital Markets: Underwriting, Wealth, Trading, and the Vendor Landscape
Banking runs four very different AI workloads — credit underwriting, wealth management, trading, and stress testing — and each answers to a different regulatory gatekeeper. The stack decision is not one platform choice but four: where explainability is a legal requirement, where fiduciary duty constrains personalization, where alpha decays on contact, and where the supervisor runs the model that matters.
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AI for Healthcare Administration: RCM, Claims, Prior Auth, HIPAA, and the Vendor Landscape
The strongest near-term case for AI in healthcare is administrative: medical coding, claims processing, denial management, and prior authorization. The work is high-volume, text-heavy, and rules-driven, and a 2024 CMS final rule now puts hard 2026-2027 deadlines behind automating it. The real constraints are HIPAA architecture and vendor diligence, not model capability.
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Clinical AI: Imaging, Drug Discovery, Trials, Scribes, and Patient Chatbots
Clinical AI is five distinct procurement problems wearing one label. Imaging AI is a regulated medical device with real trial evidence; drug-discovery AI compresses one R&D step; trial matching and ambient scribes are workflow software with emerging clinical literature; patient chatbots are a liability boundary. This guide maps each workload to its evidence bar, regulatory posture, and the stack decision it actually forces.
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AI for Fraud and Financial Crime: Transaction Monitoring, KYC/AML, and Claims Fraud
AI is now the default architecture for three financial-crime workloads: real-time transaction fraud, KYC/AML compliance, and insurance claims fraud. The live decision is not whether to use machine learning but where to buy a governed product, where to build on a general-purpose ML platform, and how to keep every model defensible in front of examiners while the vendor landscape itself keeps shifting.
- Use Case
Enterprise Agent Use Cases That Work: Coding, Research, Data, Security, and IT Operations
Where AI agents actually work in production is predicted by two properties of the task, not by the industry or the vendor: a verifiable feedback loop the agent can iterate against, and a blast radius you can bound. Coding leads by a wide margin; research, analytics, data engineering, SOC triage, and IT operations follow — each with a different verifier, a different checkpoint, and a different honest maturity grade.
- Use CaseAgentic AI in HR
Predictive AI for Learning & Development: Skill Gaps Before They Become Skill Crises
Workforce signal models, content recommendation engines, and pathway prediction tools are reshaping how L&D leaders identify skill gaps, prioritize development investment, and keep capability aligned with business strategy. This guide walks through the operational logic, key use cases, vendor categories, and implementation pitfalls.
- Use CaseAgentic AI in Finance
Agentic AI in treasury: cash visibility and autonomous liquidity moves
Treasury teams are moving past static reporting into agentic AI systems that monitor cash positions in real time, recommend FX hedges, and execute intraday liquidity moves within defined guardrails. This deep dive maps the use cases, the architectural requirements, and the questions treasury leaders should be asking before they hand an agent any execution authority.
- Use CaseAgentic AI in Marketing
Agentic AI in marketing: from campaign brief to multichannel execution
Agentic AI systems can now take a campaign brief and carry it through audience segmentation, content creation, channel scheduling, and performance optimization with minimal human intervention. This piece examines how that pipeline actually works, where governance must intervene, and what separates a productive autonomous agent from a brand-safety liability.
- Use CaseDecision Intelligence
Decision Intelligence use cases for risk leaders: beyond dashboards
Dashboards tell risk leaders what happened. Decision Intelligence tooling tells them what to decide next—and why. This deep dive maps the use cases, decision types, and accountability structures that matter in regulated industries.
- Use CaseAgentic AI in Legal & Compliance
AI-Powered Contract Analysis and Legal Workflow Automation
How legal teams can deploy AI for contract review, due diligence, regulatory research, and legal drafting.
- Use CaseAgentic AI in Legal & Compliance
AI Contract Review & Lifecycle Management
Accelerate contract review, extract key terms, and manage obligations at scale
- Use CaseAgentic AI in Legal & Compliance
AI-Powered Due Diligence for M&A
Accelerate deal review with AI that reads, extracts, and flags issues across thousands of documents