Every card carries a planning horizon — Now: Decisions in flight this year · Next: 2027 planning horizon · Beyond: 2028–2029 structural shifts.
Annual Outlook
Our flagship yearly view of where enterprise AI and CX are heading, built from first-party benchmark data and dated predictions you can hold us to.
Horizons — forward-looking points of view
Signed, dated positions on what changes next and what enterprise leaders should decide now. Each piece states a thesis and closes with implications by role.
The Agentic Enterprise in 2027: what changes when software does the work
Our view of what the enterprise looks like in 2027 once agents move from assisting people to completing work, and the decisions leaders have to make in the next four quarters to be ready for it.
What happens to the contact center workforce by 2027
Our view of how contact center work, staffing models and economics change through 2027 — what automation absorbs, what it cannot, and how to plan the workforce without betting the service level on a forecast.
The cost curve of enterprise AI: what leaders should assume through 2029
Model prices fall every year and enterprise AI budgets keep growing. Our view of where the cost really sits through 2029, and how to build a plan that does not depend on the price of tokens.
Who is accountable when an agent acts: governance for 2027 and beyond
When a system takes an action rather than making a suggestion, accountability has to be assigned before the incident, not after it. Our view of how enterprise governance changes through 2027.
AI and the employee contract: the workplace enterprises will run in 2027
Employee experience is where most enterprises will feel AI first and govern it last. Our view of how the employee contract changes through 2027 and what leaders should design now.
The 2027 CIO agenda: where enterprise AI budget should go next
Our view of how a 2027 technology budget should be allocated for AI: the long-lead investments that determine everything downstream, the line items to stop funding, and the decisions that cannot be deferred another year.
Advisory guides
The decision-grade guides behind each practice — what to build, what to buy, how to sequence it and what it costs, grouped by the practice that owns the work.
The enterprise guide to Agentic AI
Copilots demo well; agents change the P&L. This guide is the enterprise reference for what Agentic AI actually is, where it belongs in the operating model, and how CIOs, COOs and Chief AI Officers are moving programs from pilot to portfolio.
Agentic AI for Financial Services — A Practical Guide for Banks, Insurers and Wealth Managers
A practical guide to deploying agentic AI in regulated financial services. Covers KYC refresh, fraud triage, compliant collections, servicing and underwriting agents — with the governance, model-risk and audit patterns that keep examiners comfortable.
Agentic AI for Healthcare Providers — A Practical Guide for Hospitals, Health Systems and Physician Groups
A practical guide to deploying agentic AI across provider operations. Covers scheduling and access agents, prior authorization automation, revenue cycle and denials, ambient clinical documentation, and patient contact center — with HIPAA, HITRUST and safety governance patterns.
Agentic AI for Healthcare Payers — A Practical Guide for Health Plans and Managed Care Organizations
A practical guide to deploying agentic AI inside health plans and managed care organizations. Covers member services, claims, appeals & grievances, provider ops and utilization management — with the HIPAA, CMS interoperability and NAIC governance patterns that regulators and auditors expect.
Agentic AI for BPO Providers — A Practical Guide for Contact Center and Back-Office Outsourcers
A practical guide to agentic AI for contact center and back-office BPOs. Covers portfolio strategy, gain-share commercial models, agent-assist and autonomous voice, back-office IDP, QA and coaching — with the client-security, multi-tenant governance and delivery patterns BPOs need to protect and grow revenue.
Agentic AI for Retail & E-Commerce — A Practical Guide for Brands, Marketplaces and Omnichannel Retailers
A practical guide to agentic AI for retail and e-commerce. Covers conversational shopping, service and returns, merchandising and content operations, marketplace and seller ops, and store & associate assist — with brand-safety, margin and privacy governance patterns.
Playbooks, architectures and implementation guides
Delivery-level material for the teams doing the work: platform implementation guides, reference architectures and downloadable playbooks.
Conversational IVR: replacing menu trees with intent
The menu tree is the oldest surviving artefact in the contact center, and it is the single largest source of avoidable customer effort. Conversational IVR replaces it with intent capture at the front door — but only if fallback, authentication and knowledge are designed before the first prompt is written.
Call center automation software: how to evaluate the stack
Every vendor in this category claims the same outcomes, so the demo is useless as a selection instrument. This guide breaks the stack into five layers, gives the scoring criteria that actually separate vendors, and covers the cost lines buyers routinely miss.
Agent assist software: what actually reduces handle time
Agent assist is the fastest-payback workload in contact center automation and the easiest to deploy badly. The difference is not the model — it is whether the assist reduces cognitive load or adds another panel the agent learns to ignore.
Contact center automation use cases by industry
Automation programs are funded on specifics, not on capability slides. This guide lists the use cases that reach production most often in each industry, what each is worth, and which constraint decides whether it ships.
AI readiness assessment: the framework that predicts delivery
Most readiness assessments produce a radar chart and no decisions. A useful one predicts which workloads you can actually ship in the next two quarters, and names the specific remediation standing in the way of the rest.
Generative AI consulting: from proof of concept to production
The proof of concept is the cheapest part of generative AI and the part every vendor is happy to sell. This guide covers the expensive part: selecting workloads that survive contact with real data, and the production gates between a convincing demo and a system your risk function will approve.
Use cases, ROI models and platform selectors.
Ungated tools you can use right now — filterable use cases, defensible ROI calculators and short assessments. No email required.
Use Case Library
30+ real enterprise use cases across AI, CX and automation — filterable by service, industry and partner platform.
Browse use cases →Contact Center AI ROI
Model containment, AHT reduction and agent cost savings for voice AI, agent assist and automated QA.
Estimate savings →AI Business Automation ROI
Estimate hours and dollars returned from intelligent automation across document processing, rework and transaction workflows.
Model automation ROI →All Tools & Assessments
AI readiness assessments, CCaaS platform selectors and every calculator in one place.
Explore all tools →Explore the rest of the library
- Benchmark reportsIndustry benchmarks & data
- PlaybooksStep-by-step delivery plans
- Reference architecturesBlueprints for engineers
- InsightsPoint-of-view essays
- Case studiesEnterprise outcomes
- Platform comparisonsSide-by-side buyer guides
- DownloadsBrochure & capabilities deck
- BPO · AI · CX · CCaaS HubIndustry hub for BPO leaders
Want this applied to your 2027 plan?
We run 60-minute private working sessions with executive teams — bring the plan, we bring the research and the delivery view.