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The moving parts of a production deployment, from channels and orchestration to systems of record.
An AI Readiness Assessment determines whether an enterprise has the right use cases, data access, platform choices, economics, governance and operating model to move AI from pilots into production. Pronix runs the assessment as a practitioner-led engagement and turns the findings into an executive roadmap and implementation-ready backlog.
The output is not a maturity label. It is a funding and delivery decision: what to build first, what to fix before building, which platform path fits the estate, and how leadership should govern value, risk and adoption after launch.
Assessment findings and economics are based on the client's supplied baselines and operating constraints; no outcome or funding level is guaranteed.
Executives are asked to fund AI before the organization has proven which use cases matter, which platforms fit, what data is usable, who owns risk and how the economics will hold after go-live. The assessment closes that evidence gap before money is committed to the wrong pilot.
Each workstream produces a decision artifact that can be used by business, finance, architecture, security and delivery teams.
Workshops and evidence review to identify, cluster and score AI opportunities by business value, feasibility, risk and time-to-impact.
Vendor-neutral fit scoring across cloud, model, agent, retrieval, CRM and CCaaS options based on your existing estate and constraints.
Baseline, target and sensitivity models using volumes, cycle times, cost per case, license economics, token or minute costs and run-state assumptions.
Decision rights, intake, responsible AI controls, human oversight, evaluation evidence, escalation paths and RACI for production ownership.
Assessment of source systems, APIs, identity, entitlements, data quality, privacy, audit logging and release constraints.
A sequenced plan for quick wins, foundation remediation, POCs, production releases, operating-model changes and budget gates.
Each dimension connects strategy to delivery evidence, so the assessment can recommend a practical next step rather than a generic maturity score.
Identify AI opportunities across CX, operations, employee experience and back-office work; score each candidate by value, feasibility, integration effort, risk and time-to-impact.
Output: Prioritized use-case matrix with owners and decision gates
Compare model, agent, retrieval, cloud, CRM and CCaaS options against the current estate, identity model, data location, security controls and total cost assumptions.
Output: Platform scorecard and recommended architecture path
Translate the shortlist into CFO-ready economics using your own baselines wherever available: volume, cycle time, cost per case, license cost, run cost and adoption assumptions.
Output: Business case model with sensitivity ranges
Define decision rights, intake, risk tiers, approval gates, responsible AI controls, evaluation evidence, human oversight and post-launch ownership.
Output: Governance model, RACI and operating cadence
Review source systems, APIs, data quality, entitlements, audit logging, privacy controls and operational constraints that determine whether the chosen AI workflow can ship safely.
Output: Readiness gap map and remediation backlog
Sequence quick wins, foundation work, POCs and production releases into a roadmap leaders can fund, track and communicate across the enterprise.
Output: 6/12/18-month roadmap with next-step recommendation
Every engagement follows the same rhythm — so business, IT and delivery stay aligned from opportunity to outcome.
Define sponsor, scope, decisions needed and evidence standard.
Run stakeholder workshops and map use cases, systems and constraints.
Score value, feasibility, data, risk, governance and adoption readiness.
Build platform fit, business case and unit-economics views.
Sequence remediation, POCs, implementation and governance gates.
Present executive findings and the recommended next-step motion.
Prioritize which AI use cases deserve budget now, which need remediation and which should be stopped before spend grows.
Choose between AWS Bedrock, Azure AI, Google Gemini Enterprise, OpenAI, Anthropic, IBM watsonx, Salesforce, Kore.ai or existing CCaaS-native AI based on fit, not hype.
Assess containment, agent assist, knowledge AI and automated quality opportunities against current CCaaS, CRM, data and workforce constraints.
Confirm whether tool-calling agents can safely act in systems of record, and what approval gates, test sets and observability must exist first.
Every platform we implement is only as good as the retrieval, connectors and controls behind it. These are the horizontal solutions we ship with every engagement.
An AI readiness assessment delivers the evidence executives need before funding AI: a prioritized use-case portfolio, platform selection guidance, data and integration readiness findings, unit-economics model, governance and operating-model recommendations, and an executive roadmap. Pronix runs it as a practitioner-led assessment that converts readiness findings into a POC or production implementation path.
Each opportunity is scored by value, feasibility, risk, integration effort, ownership and time-to-impact, so leaders can fund the right sequence rather than a disconnected set of pilots.
The assessment compares cloud, model, agent, retrieval, CRM and CCaaS choices against data location, identity, security, integration and operating constraints.
Business cases use client baselines where available, while governance recommendations define decision rights, risk tiers, evaluation evidence and production ownership.
The assessment is designed to support an executive funding decision and a clean handoff into assessment, POC, implementation or managed run.
One business domain, one platform decision or one prioritized portfolio. Best when leadership already knows the function or outcome in scope.
2–4 weeks typical
Multiple business units, shared platform decisions and operating-model design. Best when the board needs a cross-enterprise AI roadmap.
4–6 weeks typical
Readiness assessment followed by a fixed-scope POC or production pilot, using the same evidence, acceptance criteria and delivery team.
Assessment plus 8–14 week build path
Most enterprises start with an assessment, move into a fixed-scope build, keep it running under managed support, and add AI readiness consultants where their own team is short. All four can run together under one commercial agreement.
A bounded AI Readiness Assessment assessment: current-state review, prioritized use cases, target architecture, business case and a sequenced delivery roadmap.
Fixed price · 2–4 weeks typical
A defined AI Readiness Assessment implementation — architecture, build, integration, testing, evaluation and a documented production release against agreed acceptance criteria.
Fixed price · 8–16 weeks typical
Monthly operations for AI Readiness Assessment in production: release management, integration monitoring, configuration changes, model and agent evaluation and incident response under one SLA.
Monthly service tier · 24×7 coverage available
AI readiness consultants, solution architects and delivery leads embedded in your team, reporting to your delivery manager.
Monthly per person · typically live in 2–4 weeks
Programs are led from our Plainsboro, New Jersey headquarters and delivered with our Hyderabad global delivery center, plus London and Dubai for EMEA and Middle East clients.
Business-hours support in your time zone as standard, follow-the-sun 24×7 for production contact center and agentic workloads, with named escalation and monthly service reviews.
Bring your use-case backlog, platform questions and funding goals. You leave with the assessment scope, stakeholder map and evidence needed for an executive roadmap.
See the executive-ready scorecard, use-case portfolio, platform-fit view, unit-economics model, governance plan, roadmap structure and CFO budget-approval one-pager Pronix uses to turn AI ambition into a fundable delivery path.
Sample report · Illustrative enterprise data · PDF · Built for internal leadership review
How we work
Engagement models that fit your program — advisory, build, run, or embedded pods.
One accountable delivery model: US-based architecture and program leadership with global engineering pods running 24×7 build, cutover and hypercare.
Security questionnaires, controls documentation and named client references are available under NDA. More about Pronix Inc →
30 minutes. Your architecture, your data, your KPIs. You leave with a concrete pilot outline and a business case worth defending.
