AI RISK IN SUPPORT: A FIELD GUIDE TO CUSTOMER OUTCOMES Recorded Syaxis example Created and revised: 31 October to 1 November 2026 WHAT THIS EXAMPLE SHOWS A complete eight-slide presentation created in Syaxis from a short brief and the inputs below. Syaxis handled the research or source reading, writing and slide design. The operator answered its setup questions and used the same chat for the revisions recorded here. All slide creation and edits were made in Syaxis; the exported deck was not rebuilt or manually redrawn elsewhere. This is a selected, reviewed and revised product demonstration, not a first-pass accuracy benchmark. The final PDF, PowerPoint export and preview come from the same approved presentation. INPUTS nist-ai-rmf-source.pdf SETUP ANSWERS Summary of the selected answers to Syaxis questions, not a verbatim chat transcript: 8 slides; AI risk basics, support workflows, risk identification/assessment, mitigations/safeguards. ACTUAL SUBMITTED PROMPTS Initial brief Turn this report into a practical introduction to AI risk for support leaders. Use a calm, light visual style with a simple support-workflow illustration. Revision 1 Improve the cover and closing compositions with clear spacing; the final headline currently crowds the workflow diagram. Keep the rest unchanged. Revision 2 On slide 8, replace the three cramped condition labels with one concise line about workflow, owner, and launch/pause criteria above the diagram. Leave everything else unchanged. Revision 3 Make this a more engaging illustrated field guide for support leaders. Give it a distinctive cover and closing, with illustrations instead of repeated boxes. Keep the source meaning. Revision 4 Change only the cover and closer images to illustrations without any people. Keep an image on both slides. Preserve all text and every other slide. OUTPUT AND REVIEW Eight slides. The final native slide images were visually reviewed, and the actual PowerPoint export was rendered in LibreOffice and checked. Source review used AI-assisted checks against the sources below. The example was selected for this showcase after visual review. PowerPoint includes editable text and supported shapes alongside raster visuals. These files do not contain native PowerPoint chart or table objects. Speaker notes contain slide numbers, not a bibliography or substantive presenter notes. Microsoft PowerPoint application editing was not tested. Use the PDF for the closest representation of the reviewed slide design, and this record for detailed source provenance. nist-ai-rmf.pdf SHA-256: b6b2673a0a31d508d7d83ffa702d0bdc9152d1599c34d132358f2106e1758abe nist-ai-rmf.pptx SHA-256: 8dbec68e9636606e0cf30b627d44652d919237a4b553a481bbfe49f56047225f SOURCES AND EVIDENCE AI risk briefing source and illustration notes The eight-slide “AI risk in support. A field guide to customer outcomes” briefing draws on NIST's AI Risk Management Framework. The routing and refund cases and operational recommendations illustrate how to apply the ideas; they are not NIST-reported incidents or evidence of Syaxis performance. Source National Institute of Standards and Technology (2023), Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1, January 2023, 48 PDF pages. https://doi.org/10.6028/NIST.AI.100-1 https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf Original source file: NIST.AI.100-1.pdf. Printed page numbers differ from PDF viewer numbers by five pages. Source grounding - Printed page 2 (PDF page 7): the framework is voluntary and adaptable across sectors and uses. - Section 1: risk involves potential impacts and likelihood; uncertainty and context matter. Section 1.2.2, printed pages 7-8 (PDF pages 12-13), addresses contextual risk tolerance. The framework does not prescribe an organization's tolerance or eliminate responsibility for residual-risk decisions. - Section 2 and Appendix A describe AI actors; Appendix C discusses human-AI interaction. Human review alone is not proof of adequate risk control. - Section 5, printed pages 20-21 (PDF pages 25-26): GOVERN, MAP, MEASURE and MANAGE are four iterative functions. GOVERN spans the others; they are not a one-way checklist. The following function tables develop governance, context, testing, monitoring and risk treatment. Illustrative content and recommendations The fixed-rule versus AI-classifier routing comparison is a conceptual example of possible failure modes, not a measured comparison or a claim that every classifier changes its routing when wording changes. The customer-support workflow, refund request, unsupported refund promise and alternative escalation response are invented examples. The rule requiring billing review before a refund promise is a proposed scenario rule, not a rule prescribed by NIST. “Safer response” means avoiding that unsupported promise in the example; it is not a guarantee that the response or workflow is safe. Suggested ownership, permission boundaries, current-policy grounding, limited customer data, informed review, handoffs, exception tests, monitoring and disabling automation are operational recommendations. So are starting with one workflow and setting launch/pause criteria. They require local design, testing and accountable approval. They are not a NIST certification checklist or evidence that risks have already been controlled. A bounded pilot is not proof of safety, legal compliance, measured ROI or guaranteed risk reduction. Record remaining uncertainty and residual risk, and consider people and outcomes beyond the model's immediate response. Attribution Framework concepts are adapted from National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework (AI RMF 1.0), 2023. Original report republished courtesy of NIST. Attribution does not imply endorsement of Syaxis, the presentation or its recommendations. NIST publication licensing information: https://www.nist.gov/open/copyright-fair-use-and-licensing-statements-srd-data-software-and-technical-series-publications