AI results, your CEO demands them yesterday. The fear of missing out is real, but most organizations are stuck between pilot and proof of concept. That’s normal. IDC forecasts that worldwide spending on AI will exceed half a trillion dollars in 2027, but most companies are still trying to figure out how much the latest AI buzzword is moving the needle. The smartest play: choose several high‑conviction bets, wire them to measurable results and scale thoughtfully.
Here are three bold, yet realistic actions any company can try this quarter — all based on actual implementations and about creating value, not headlines.
Bet 1: Agentic sales and service that builds trust
“Outgrow static chatbots to agentic AI—systems that do more than just tell you things, they do things in your stack.” Think: refund an order, recommend a bundle, reschedule a delivery, issue a ticket. Begin with the 15–20 most common intents that make up the majority of your contact volume and wire the agent into your CRM, order system, knowledge base and policy library.
Use retrieval‑augmented generation so the responses cite your current policies and product details. Gate high‑risk actions behind human approvals, and craft graceful hand‑offs to live agents. Center metrics around containment rate, first‑contact resolution, average handle time, escalation rate and revenue per chat — then run a duplicate to A/B test against your current flow.
This isn’t theoretical. An international jewelry retailer has trialed agentic shopping capabilities built on a leading CRM platform to move from service-only chat to consultative selling by retraining the model with transcripts from top performers. Klarna noted that its AI assistant is now responsible for the majority of customer chats, performing hundreds of agents’ worth of work as effectively while delivering similar satisfaction scores and faster resolution. The playbook is effective because it combines data grounding, stiff guardrails and human-in-the-loop approvals.
Pro tip: construct an “emotions and events” schema (birthdays, likes, previous purchases) that the agent can legally draw from. That is the world in which upsell and loyalty exist, and it’s a place where “built-in” chatbots just plain suck.
Bet 2: AI‑powered product development to speed decisions
Apply AI in the product decisions where you bottleneck — cost, manufacturability, demand and design feedback. Given CAD features, bill of materials and historical routings, cost and cycle time can be forecast using probabilistic models. Language models can sift through thousands (or millions) of customer reviews and service file logs to identify failure modes, feature requests or quality signals your teams have overlooked.
One consumer brand employs a review synthesis model to group recurring themes — fit, finish, materials — into design sprints and supplier briefs. A top shop estimates production effort and feasibility based on design attributes before the concept leaves the studio. Industrial firms are combining generative design software with rule-based safeguards to reduce iterations while preserving safety and compliance.
It’s a trade‑off for speed and hit rate. Generative AI could add trillions in annual value globally, according to McKinsey, with significant lift in customer and product operations. Start with a “concept-to-shelf” cockpit: manufacturability scoring models; cost out projected demand across price points and lead times; and draft a PRD automatically that references source data. Measure time-to-decision, ECOs and margin at launch.
Pro tip: service notes and reviews are a proprietary asset. Clean them, tag them and apply similar taxonomies. The quality of your product insights will escalate as quickly as your data hygiene improves.
Bet 3: Copilots in the back office to reduce busywork
Knowledge work is full of repetitive tasks: invoice triage, policy lookups, vendor onboarding, and “where do I find…?” questions. These steps can be collapsed if the copilots are built into and learn from everyday tools. Early users of Microsoft 365 Copilot, Microsoft reports, can accomplish common tasks more quickly and feel more satisfied with their work; there are similar gains among the teams testing Google’s Workspace assistants and Salesforce’s Einstein Copilot.
Begin with one internal assistant in Teams or Slack that can respond to HR and IT inquiries, draft form emails, brief on meetings, and prepare reports from live data. Use regulated build environments — like Copilot Studio — to prescribe actions, enforce role-based access and automatically log every decision for audit. Then graduate to “straight-through” workflows like expense audits or vendor form validation.
Report cycle time, touchless rate, exception rate and rework. Establish service-level objectives so the copilot understands when to pass ownership to humans. The result isn’t fewer people, but fewer busywork loops and quicker time to the right answer.
Getting going in 90 days: a practical starter plan
Weeks 1–2: Choose one use case for every bet. Name an accountable product owner. Stand up a secure sandbox connected to your CRM, knowledge base and data warehouse. Apply the NIST AI Risk Management Framework and map obligations from the EU AI Act if you are in Europe. Agree on success metrics and red‑lines.
Weeks 3–6: Develop thin‑slice prototypes with retrieval‑augmented generation, action plugins and human‑in‑the‑loop checkpoints. Red‑team quickly for fraud, leak and bias. Develop model cards and decision logs. Bring legal and security in early; ISO/IEC 42001 provides a model for AI management systems.
Weeks 7–12: Conduct a staged pilot with live users. Publish a live dashboard of results and errors. Compare business metrics versus your benchmark. Scale if the bet does; kill or pivot if it doesn’t. One way or the other, you’ve turned FOMO into a repeatable operating model.
Bold isn’t the same as reckless. Agentic service, AI‑assisted product development and back‑office copilots are all possible today, subject to guardrails and data discipline. Choose your edge, instrument your results and let the evidence, not FOMO, determine what you scale.
