Contact - Stratalith

Turn AI Workflows Into ROI

Whether you’re just exploring AI workflows or already testing them, the challenge is the same: proving measurable business value. Our free AI Workflow Readiness Assessment highlights ROI opportunities, pinpoints risks, and shows where to focus for quick wins.

1. Business Identity & Context

Full Name *

Email Address *

Company Name *

Company Website *

Industry *

Where do you want to implement AI Workflows? *

Department Size *50<50>

Company Size *5050-250250-500500+

Have you trialed or abandoned AI Workflows before? *(if yes please explain why)YesNo

Reason for trialing or abandoning AI Workflows *

2. Purpose and Outcomes (Your AI Workflow North Star)

What is the primary objective for AI Workflows in the next 6–12 months? *(select one)Improve operational efficiencyEnhance customer experienceReduce risk/compliance exposureDrive revenue growth/new productsOptimize cost-to-serveRegulatory alignment/ethical assuranceOther (please specify)

When do you expect to see first meaningful results from AI Workflows? *(select one)<3 months (quick wins)3-6 months (mid-term)6-12 months (strategic initiatives)Flexible

What level of accuracy do you expect from AI Workflows to feel useful? *(select one)100% accuracy only90-95% accuracy if it drives major productivity70-80% accuracy if it drives productivityFlexible, depends on the use case

Which Metrics will prove success? *(select up to 3)Cycle time / throughputCSAT, NPS, or first-contact resolutionQuality, error rate, or accuracyCost per transaction / caseRevenue lift or conversion rateRegulatory findings / compliance incidentsTime-to-market / launch cadenceemployee adoption / enablementFairness or complaints rate

In one sentence, what would make AI Workflows a success for your business in the next 12 months? *

3. Current Stage of Adoption (Where You’re Starting From)

Where are LLM Workflow initiatives today? *(select one)No activityExploration/pilot designPilots in progressLimited productionScaled operations across multiple use cases

Please indicate: *

Which technical level are you operating at? *(select one)Level 1: prompting only (LLMs like ChatGPT, Claude)Level 2: No-code workflows (Zapier, Make, n8n)Level 3: Custom integrations / APIs / AI agents

Have previous AI Workflow initiatives stalled at the pilot stage?If yes, please explain why?YesNo

reason for stalled AI Workflow initiative *

4. Business Functions & Process Readiness (Where AI Workflows can help most)

For each stage of your value chain, select whether it is mostly digital, mostly manual, or a mix.

Market → Lead *(select one)Mostly digitalMostly ManualMixed

Lead → Sale *(select one)Mostly digitalMostly ManualMixed

Sale → Delivery *(select one)Mostly digitalMostly ManualMixed

Delivery → Success *(select one)Mostly digitalMostly ManualMixed

Success → Market *(select one)Mostly digitalMostly ManualMixed

How well documented are your key processes? *(select one)Only in people's headsSome checklists / tribal knowledgeBasic SOPs documentedFully documented and usedContinuously measured & improved

Which teams are most open to AI Workflows, and which are most resistant? *

How integrated are your current systems (CRM, ERP, data warehouses, etc.)? *(select one)Highly integratedSomewhat siloedVery siloedDon't know

5. Leadership & Governance (Setting the Rules of the Game)

Which best describes your AI Workflow governance maturity? *(select one)No AI Workflow policy / no defined rolesDraft policy; informal practices; no approvalsPolicy and named owners; basic approvalsPolicy, approvals, decision logs, and evidence capturedPolicy, approvals, evidence + periodic internal audits/reviews

Do you have defined roles for AI Workflows? *(select all that apply)Product ownersData governancePlatform operationsRisk / Security roles

Which of the following do you currently require from your AI Workflow vendors? *(select all that apply)AI risk assessment completedTraining data disclosuresEthics, privacy, or security clausesAudit rights / compliance evidenceNone

Who in leadership is accountable for AI Workflow security and risk decisions? *

6. Data, Privacy & Security (Protecting the Crown Jewels)

Which AI security measures are in place? *(select all that apply)Role-based access control (RBAC) for AI systemsAudit logging of prompts/outputsModel input/output monitoring (detect leakage/injection)API security standards (OAuth2, mTLS, gateways)Incident response plan for AI misuse/breachPenetration testing / red-team assessmentsAlignment with NIST, ISO 27001, SOC2 security frameworksNone

Which stage best describes your AI security posture? *(select one)No AI-specific measuresSome ad hoc measuresDedicated AI controls in placeFormal AI security testing + monitoring (e.g., adversarial testing, anomaly detection)

Is poor data quality, access, or security currently blocking AI Workflows progress? *(select one)YesNoNot Sure

What security concerns keep you most awake about AI Workflow adoption? *

7. Evaluation & Monitoring (Building Trust in AI Workflow Outputs)

Which best describes your evaluation & monitoring approach? *(select one)No formal tests or metricsPre‑release tests onlyPre‑release tests + limited post‑deployment monitoringMonitoring with rollback / kill switchFull monitoring + bias / drift checks + incident playbooks

Is bias monitoring active on your AI Workflow systems? *(select one)YesNo

Is there a human-in-the-loop review or escalation process for critical AI Workflow decisions? *(select one)YesNo

Do you monitor AI Workflows for bias? *(select one)YesNo

Do you require human-in-the-loop for critical AI Workflow decisions? *(select one)YesNo

Which additional monitoring/security measures do you use? *(Select all that apply. If none, select only ‘None’.)Prompt injection detectionAnomaly detectionModel drift monitoringNone

Do staff trust AI Workflow outputs without excessive rewriting/double-checking? *(select one)YesNoVaries by team

What new standard of performance would you like AI Workflows to set? *

8. AI Workflow Attitudes & Concerns (The Human Factor)

What concerns you most about AI Workflow adoption? *(select all that apply)High cost / ROI doubtsImperfect accuracy / errorsJob disruption / role changesStaff resistance / cultural pushbackPoor data / integration challengesCompliance / regulatory exposure

How confident are employees in adapting workflows to LLMs (without expecting perfection)? *(select one)Very confidentSomewhat confidentNot confidentNot discussed

What would make your staff more confident using AI Workflows daily? *

9. People and Change Management (Bringing the Team with You)

Which best describes your AI Workflow talent & change maturity? *(select one)No assigned owners; no trainingPart‑time owners; ad hoc trainingClear owners + structured trainingChange plan with role-based commsEmbedded change office, ongoing training + metrics

Are non-technical staff trained in AI Workflow ethics and compliance? *(select one)YesNo

Do you have a communication plan for AI Workflow adoption? *(select one)YesNo

How do you expect your staff to respond to AI Workflow adoption? *(select one)ExcitedOpen but cautiousResistantNot sure

Do you track and communicate ROI of AI Workflow initiatives to staff/leadership? *(select one)YesNoPlanned

10. Standards & Certifications (External Proof & Compliance)

Do you have any ISO certifications currently inside your organisation in review, in development, or certified? *(select all that apply)ISO 27001 Information Security ManagmentISO 27701 Privacy Information ManagementISO 9001 Quality ManagementISO 42001 AI ManagementNone (Please explain why)

ISO 27001 level of maturity *(select one)in reviewin developmentcertified

ISO 27701 level of maturity *(select one)in reviewin developmentcertified

ISO 9001 level of maturity *(select one)in reviewin developmentcertified

ISO 42001 level of maturity *(select one)in reviewin developmentcertified

11. Technical Foundations (Your AI Workflow Landing Zone)

Do you track the cost of AI workloads (LLM/API usage, compute, storage)? *(select one)YesNoPlanned

12. Risk & Oversight (Keeping AI Workflows Accountable)

Which best describes your risk classification & oversight maturity? *(select one)No classification or oversight definedBasic register of use cases with risk notesFormal risk classification with human oversight on high-impact decisionsFormal classification + transparency/explanation + user redress channelsFormal classification + redress + periodic bias/impact reviews and documentation

Do classifications cover third-party/vendor LLM models? *(select one)YesNo

How often are AI Workflow risks reviewed? *(select one)MonthlyQuarterlySemi-annualAnnual

13. Prioritization & Roadmap (Where to Start, What to Stop)

Which prioritisation method do you use? *(select one)None (ad hoc)Effort vs value heatmapROI threshold matrixMatrix + formal review & retirement criteria

For each AI Workflow use case, which delivery mode fits best? *(select all that apply)Level 1: Prompting (no code)Level 2: No-code workflows (Zapier, Make, n8n)Level 3: Custom integrations / APIs / AgentsEliminate task (not worth doing)

Do you track the total cost of ownership (TCO) for AI Workflow initiatives? *(select one)YesNoPlanned

If yes, which cost components do you currently track? *(select all that apply)LicensesInfrastructure / compute usageMonitoring / evaluation costsHuman-in-the-loop time (manual oversight)Vendor fees (SaaS, APIs, consulting)FinOps alerts / optimization toolsOther

Does your vendor policy include the following? *(select all that apply)IP ownership / indemnityTraining data disclosuresData handling / residencySecurity posture (SOC2, ISO certs)Service uptime SLOsPortability / exit rightsModel update cadence transparency

How do you decide when to retire or abandon an AI Workflow initiative? *(select one)No criteriaBased on ROI thresholdsBased on risk incidentsFormal review process

Let’s Start With Clarity

Every journey begins with our free AI Workflow Readiness Assessment. From there, we’ll show you if a paid audit makes sense — and whether workflow implementation is right for your business.

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