Q: What is the simplest rule to begin when trying to scale AI in my company? A: A simple rule for how to scale AI in enterprise is do less, better: pick one problem, one metric, and run a small pilot before expanding. Baseline the metric for two weeks and estimate value per unit so you can prove the after against the before. Q: How should we pick and structure a pilot use case? A: Choose a painful task like support backlogs, slow proposals, invoice matching, or QA steps and define a single success metric such as tickets per agent or days sales outstanding. Run a time-boxed pilot with 10–30 real users, include a control group, and instrument usage, task time, error rate, and handoffs weekly. Q: What data and access preparations are necessary before launching an AI pilot? A: Map the model’s inputs and outputs, clean obvious issues like duplicates and missing fields, and set role-based access so sensitive fields are masked or off-limits. Use connectors to your docs, CRM, ticketing system, and data lake to avoid copy-paste sprawl. Q: How can we prove ROI in a straightforward way during pilots? A: Calculate time saved (hours saved × loaded hourly rate × adoption rate), track a clear impact metric for the use case, and estimate revenue lift or risk reduction where relevant. Report monthly with three numbers: adoption, the impact metric, and dollars realized. Q: Why do many AI projects stall after deploying chatbots? A: The survey found about 90% have adopted text-based chatbots, but many teams don’t tie those tools into daily workflows or establish metrics. The bigger blockers are people-related—time to learn tools, lack of skills, and unclear measurement—rather than the technology itself. Q: What governance and safety steps should be put in place for enterprise AI? A: Write simple rules about what data is allowed, when human review is required, and what to log, and add guardrails like citation checks, PII redaction, and domain-restricted retrieval. Track errors and hallucinations, escalate high-risk outputs to experts, and keep audit logs for prompts, versions, and outcomes. Q: How do we embed AI into everyday workflows so adoption sticks? A: Embed AI where work happens—inside email, docs, tickets, chat, and CRM—to avoid context switching and update SOPs to show when the AI starts and when humans review. Use sidecar assistants for drafting and agents for repeat steps, and connect actions to systems so AI can create Jira issues or update Salesforce fields. Q: What should a company do to scale beyond a successful pilot? A: Package what worked—prompts, templates, connectors, and SOPs—in a shared library and create a light Center of Enablement to coach teams rather than block them. Standardize vendor checks for security, latency, and cost controls, set budgets, monitor usage, and trim unused seats and calls.