Do you feel like AI is going to change everything, but you aren’t totally clear on how to actually get there in your company?
If it feels murky for you, don’t freak out, you are not alone. I am getting lots of semi-embarrassed questions on this. No need to feel that way! Here are a few simple steps to use as you consider how it might work for your company. Read time = five minutes, and you'll have a practical, beginner guide for simple AI deployments.
1/ Strategy. Deploying AI is a business transformation not a tech project. Define your business priorities (e.g., reduce opex, improve speed, etc) that AI needs to achieve
2/ Decide where. Audit data (quality, completeness, accessibility, etc) and map it to relevant business process. Assess your current tech stack and integration points. Map your processes that are high-volume and repetitive or decision-intensive based on clean data. AI will be very useful where it can use data to predict, generate, retrieve, or classify things, and as a result highly automate process flows. Prioritize use cases where you will have high business impact/ROI technically feasible (good data, technical fit, reasonable deployment effort). Integrate AI directly into your existing workflows and tools. Standalone tools just become a parallel processes, or worse, a more expensive google search. Embed it where your org already does work, like ticket classification, draft generation, predictive alerts in CRMs, etc.
3/ Get Ready. Lean out your process flow. Subtract before you automate. implement data governance (single source of truth with clear policies, quality rules, access controls, data lineage, and audit trails), otherwise garbage in/out. Build unified data pipelines. Your enterprise data unfortunately lives in silos (ERP, CRM, emails, docs, etc.) and is rarely clean. You’ll want a pipeline to automate ingestion, cleansing, transformation, and delivery. Choose your core AI platform. Usually either inside a CRM, low-code, in your CSP, or a full orchestration framework. Prioritize working within your existing tech stack and your team’s technical capabilities.
4/ Enable yourself for success. Establish basic MLOps/LLMOps. You Don’t need to go crazy here, but realize that LLMs are probabilistic not deterministic (aka same prompt -> different outputs) and context-dependent, so traditional software testing doesn’t hold up. You’ll need to keep an eye on data inputs, evaluation, and ongoing monitoring. Build in security to control access, prompting, output validation, data poisoning, or overly permissive agency. AI without any of your proprietary or contextual data is generic and will be low-value; but direct hookups without any controls will risk exposing your sensitive info. Also, give your people enough lightweight supporting artifacts that they can be successful. Think: prompt libraries and templates, usage guidelines, playbooks, skill docs, error-handling.
5/ Deploy. Run it as a controlled pilot in one team or department with a limited scope. Measure against your defined business KPIs Iterate quickly based on your actual usage and data. Establish baselines before the pilot so you can prove impact. Monitor prod (technical performance, cost, evaluations). Use dashboards and automated alerts. Don’t forget to enforce governance with ongoing risk reviews, audit trails, etc. Optimize costs and refine your approach as your maturity grows.