About - 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

Who We Are

We’re operators first, technologists second. Stratalith helps organisations cut through AI hype with evidence-based audits and pragmatic change support. We only recommend workflows that deliver measurable business value.

Andrew Parrington

CEO & Founder of Stratalith™

Roman Vonbun

CCO & Co-Founder of Stratalith™

Damian Thompson

Director at Stratalith™

Andrew Poole

CIO of Stratalith™

Philosophy

90% operational optimisation.
10% AI enhancement.
100% business value.