Straight answers to the questions enterprise AI buyers ask.
Every answer is written to be quoted: a direct response first, then the first-party benchmark figures behind it, each linked to the Pronix research it comes from. Last reviewed 2026-08-31.
6 answers · Enterprise AI & Agentic AI
What is an AI systems integrator?
An AI systems integrator designs, builds, integrates and operates AI inside an existing enterprise estate rather than selling a product. The work spans strategy, platform selection, data and retrieval plumbing, integration with core systems, evaluation, governance and the run state. Pronix.ai is a specialized AI and CX systems integrator working across Enterprise AI, Contact Center AI and AI Business Automation.
How long does an agentic AI pilot take?
A production-intent agentic AI pilot takes eight to twelve weeks from kickoff to live traffic. That covers one workflow, the integrations it touches, tool permissions and guardrails, an evaluation harness and human-in-the-loop review. Longer timelines usually signal missing data access or an undefined baseline rather than model difficulty. Scale decisions follow the measured result.
What does enterprise AI cost to run?
Model tokens are a minority of enterprise AI cost. Most spend sits in retrieval, integration, evaluation and the engineering that maintains them, plus the run state after go-live. Budget in three buckets — build, inference and operate — and instrument unit cost per task from day one, because routing, caching and context discipline move the run bill far more than model price lists.
Should we build or buy enterprise AI?
Buy the commodity layer — models, CCaaS, orchestration platforms — and build only what encodes your differentiation: retrieval over your content, integrations with your systems of record, and evaluation of your outcomes. The real comparison is not licence versus engineering; it is total cost of ownership including integration, evaluation and the run state on both sides.
What is enterprise RAG and when do you need it?
Enterprise RAG grounds model answers in your own governed content: documents are chunked, embedded and retrieved at query time, then cited in the answer, with the user's permissions enforced on retrieval. You need it whenever answers must reflect current, access-controlled company knowledge — policies, product data, contracts — which is most enterprise use cases.
How do you govern enterprise AI?
Workable AI governance has four parts: an inventory of every AI use case, a risk tier per use case, evaluation gates that a release must pass, and monitored controls in production. Map those to NIST AI RMF and ISO/IEC 42001 so audit and procurement recognise the artefacts, and keep the gates automated so governance speeds delivery instead of blocking it.
7 answers · CX & Contact Center AI
What does contact center AI cost?
Contact center AI cost has three parts: the build for each automated intent, per-interaction platform and model charges once live, and the run state that keeps containment from decaying. Judge it on cost per contact rather than licence price, because a contained interaction is dramatically cheaper than a handled one and that delta is where the business case lives.
How do you measure contact center AI ROI?
Measure contact center AI ROI against a pre-pilot baseline on four numbers: containment on the automated intents, handle-time change on assisted contacts, escalation quality, and blended cost per contact. Capture the baseline by intent before launch — without it, savings cannot be attributed and finance will discount the business case entirely.
Agent assist or virtual agent — which should you deploy first?
Deploy agent assist first when your intent mix is complex or your content is uneven: it lowers handle time and improves consistency without customer-facing risk. Deploy a virtual agent first when a few high-volume transactional intents dominate, because containment removes those contacts entirely. Most mature estates run both, on the same knowledge and the same evaluation harness.
What containment rate should we expect from conversational AI?
Containment targets should be set per intent, not per programme. Simple transactional intents such as balance, hours, status and appointments can sustain high containment; complex informational intents land far lower; anything requiring discretion or empathy should escalate immediately. A blended target set without that split is the most common cause of a missed business case.
How do you migrate an IVR to conversational AI?
Migrate in stages rather than replacing the IVR wholesale. Baseline call reasons and containment, fix the prerequisites — authentication, back-end APIs, routing data — then cut over the highest-volume intents behind a fallback to the existing flow, and tune weekly on real transcripts. Skipping the prerequisite work is what costs containment in the first six months.
How do you choose a CCaaS platform?
Choose a CCaaS platform on four things: the intent mix you must automate, the integration surface into your CRM and core systems, the maturity of the AI and orchestration layer you will actually use, and total cost across licences, telephony and build. Feature-grid comparisons rarely predict outcomes; a scripted proof on your own two hardest intents does.
How much does voice AI cost per minute?
Voice AI cost per minute is the sum of telephony, speech recognition, model inference and speech synthesis, plus the platform charge for the interaction. Comparing those components to each other is unhelpful; compare the fully loaded cost of a contained call to the loaded cost of a handled call, which is where the published savings range comes from.
5 answers · AI Business Automation
What is AI business automation?
AI business automation applies models, retrieval and agents to end-to-end operational workflows — claims, invoices, onboarding, records, case handling — so documents are read, decisions are drafted and systems are updated without a person touching every step. People handle exceptions and approvals. The measurable outcome is straight-through processing rate and cost per transaction, not tasks automated.
RPA or agentic automation — what is the difference?
RPA executes deterministic, pre-scripted steps and breaks when screens or formats change. Agentic automation reasons over unstructured input, chooses tools and adapts, but needs guardrails, permissions and evaluation. Use RPA for stable, high-volume mechanics and agents for judgement and variation — most production workflows combine both, with agents deciding and RPA or APIs executing.
How do you automate insurance claims processing with AI?
Automate claims in four steps: ingest and classify the submission, extract structured data from documents and images, triage by complexity and fraud signal, and settle low-complexity claims straight through while routing the rest to adjusters with a drafted summary. Accuracy gates and sampled human review protect leakage while the straight-through share increases.
How do BPOs protect margin with AI?
BPOs protect margin by automating the work that clients already expect to cost less, consolidating onto fewer platforms, and using AI quality assurance to cover every interaction instead of a sample. As pricing shifts from seats to outcomes, margin follows automation share and cost per transaction rather than headcount utilisation.
How do you get 100% quality assurance coverage in a contact center?
AI quality assurance scores every interaction against your existing scorecard instead of the small manual sample most centres review. Calibrate the model against human scores on a labelled set, publish agreement rates, then move QA analysts from listening to coaching and dispute review. Full coverage removes sampling bias and surfaces compliance risk that sampling misses.
Want these numbers applied to your estate?
Bring your containment, handle-time and cost baseline. We will model the opportunity against the same benchmarks used on these pages.