Discover
576 items
- Guide
Evaluating MCP Gateways: Buy Against the Specification, Not the Feature Grid
An MCP gateway's real job is to enforce, at your boundary, controls the Model Context Protocol already makes mandatory. That makes the strongest evaluation questions conformance questions with verbatim answers in the spec — not feature questions, where every vendor says yes. The current revision also changed the session model and deprecated a registration mechanism, so shortlists assembled from 2025 material are testing for the wrong things.
- Comparison
Choosing an Enterprise Vector Database: Benchmarks, Deployment Models, and TCO
There is no credible neutral benchmark that ranks Pinecone, Weaviate, Qdrant, and Milvus against each other on your workload — and the vendors' own documentation explains why one could not exist. What that documentation does give you is enough to model the decision honestly: published memory formulas, quantization ratios, deployment models, and rate cards whose units reveal what each vendor is really charging for.
- Guide
Securing LLM Applications: Prompt Injection, the OWASP Top 10, Red Teaming, and API Gateways
The current OWASP list for LLM applications is the 2025 edition, and its first entry is still prompt injection — a class OWASP itself says has no fool-proof prevention. This guide works from that admission: what the platform guardrails from AWS, Microsoft, and Anthropic actually screen (and what their own docs say they skip), which architectural controls survive a bypassed filter, how to red-team the result, and what belongs at the API edge.
- Insight
The Enterprise AI Market: Trends, Predictions, M&A, and 2027 Planning
US enterprise AI adoption has climbed from 3.8% of businesses in late 2023 to roughly one in five by mid-2026, with large firms adopting at nearly twice that rate. This outlook reads the market from primary data only — adoption surveys, compute economics, printed vendor prices — and turns it into concrete 2027 planning decisions on budgets, vendor risk, and M&A exposure.
- Insight
Decision Intelligence: 10 Use Cases for Strategy and Planning Teams
Strategy and business development is the second most common business function where AI-adopting firms deploy AI — 45% of users, ahead of IT.[^census-ces-wp-26-25] The teams getting real value are not buying a "decision intelligence platform"; they are instrumenting specific recurring decisions. This analysis maps the ten planning workflows where that instrumentation pays, and the evidence standards that keep it honest.
- Insight
AI in product management: 11 workflows that have already changed
AI has already changed product management, but unevenly. The workflows that genuinely shifted are the language-heavy ones — synthesizing feedback, summarizing research calls, drafting PRDs and release communication — because that is where controlled experiments show large, repeatable gains. Prioritization scoring and experiment interpretation remain assistive. This piece maps eleven workflows, grades each against the evidence, and tells you where to start.
- 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.
- Use Case
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.
- Use Case
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.
- Use Case
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.
- Use Case
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.
- Guide
The Unified GTM AI Stack: ABM, Lead Routing, and Predictive AI for Customer-Facing Teams
Most firms adopt GTM AI as scattered point tools, and federal survey data shows adoption stays narrow — most adopters use AI in three or fewer business functions. The durable alternative is a unified GTM stack built in four layers: resolved identity, predictive scoring, orchestrated routing, and service analytics that feed the loop. This guide covers each layer, the build order, and where unification honestly breaks down.
- Use Case
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.
- Use Case
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.
- Use Case
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.
- Use Case
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.
- Use Case
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.
- Use Case
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.
- Use Case
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.
- Use Case
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.
- Use Case
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.
- Comparison
AI Across Regulated Industries: Adoption Benchmarks and Cross-Sector Lessons
The assumption that regulated industries trail on AI does not survive contact with the adoption data. Finance and insurance adopt at well above the national rate, and large professional-services firms sit near the top of the distribution. This comparison benchmarks adoption across finance, healthcare, and professional services, maps what actually gates deployment in each sector, and extracts the governance lessons that transfer.
- Use Case
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.