AI is transforming virtually every aspect of business operations. It's tempting to think AI will simply "figure it out" on its own, but the reality is this: time-tested fundamentals still matter — perhaps now more than ever. When guided properly and paired with human intelligence, AI can amplify your efforts with speed, scale, and fresh insights.
Ready to make sure your customer-focused AI deployments deliver real impact for your customers and your bottom line? Here are seven practical considerations:
1. Clarify Your Use Case and Goals
AI can support nearly every stage of strategy and execution — but only if you know exactly what you aim to achieve. Spend time up front refining your use case. Identify your objectives, define success metrics, and ensure alignment with your business strategy.
Involve cross-functional stakeholders early. You'll refine requirements, gain buy-in, and set the groundwork for impactful AI deployment.
2. Generate and Prioritize Hypotheses
AI shines as a thought partner — with the ability to consider voice-of-the-customer data, support tickets, product engagement, competitor activities, cross-industry best practices, and more. Use this power to uncover, help score, and prioritize hypotheses. Just remember:
- AI-driven hypotheses are invaluable for surfacing possibilities, but human judgment is still critical for vetting and deciding why something might or might not work.
- Hypothesis generation ≠ ROI measurement. It's a starting point, not the finish line.
3. Don't Let Imperfect Data Hold You Back
Waiting on better data is a common stumbling block. While data quality is important, you can likely yield invaluable learnings, insights, and positive ROI from what you have right now. Today's AI algorithms are very adept at handling gaps and inconsistencies. If you wait, you risk losing ground to more agile competitors.
Specialized AI tools can automate the data-prep drudgery. Let these tools handle data gathering, transformation, and evaluation so your team can focus on higher-value analysis and insights.
4. Connect the Dots Between Cause, Effect, and ROI
Too many companies flip the AI switch without a clear, financially-oriented compass to guide their path. Although traditional metrics like NPS and customer satisfaction are important, leadership is demanding a link to financial impact and ROI.
Just remember: this can't simply be a cost reduction and/or efficiency focused exercise. The largest, most enduring benefit of customer-impacting AI is its ability to guide and deliver better, more personalized experiences, leading to higher-value customer relationships. Make sure your ROI analytics incorporate these value levers as well.
A customer value-based ROI measurement platform, powered by specialized AI, has the unique ability to unify objectives, measurement, and decision-making across your organization.
5. Make Activation Matter
Activation — where strategy meets execution — is often the most exciting stage of AI. Whether automating digital engagement, enhancing person-to-person interactions, or informing product development, properly guided AI can transform and elevate value driven by engagement with customers.
But first, be sure to:
- Ensure your efforts are guided by clear goals (see #1)
- Implement a measurement system tied to ROI (see #4)
- Adopt an ongoing test-and-learn mindset (see #6)
Skipping these steps risks both subpar outcomes and a loss of leadership support and buy-in just when you need it most.
6. Embrace Test & Learn
Initial AI implementation is just the tip of the value iceberg. A deliberate test-and-learn program — coupled with a strong, AI-powered measurement program — ensures constant learning from the good, the bad, the new, and the old. This continuous cycle of experimentation ensures AI's true potential is unleashed, as innovation, performance improvements, and ROI gains continue indefinitely.
7. Lay the Groundwork Early to Reap Wide-Ranging Benefits
A benefit of early diligence that should not be overlooked is the foundation built for scaling the effective deployment of AI going forward. You'll gain rich learnings, a strong understanding of your data, and develop assets that can be repurposed across the enterprise. Not to mention, an advantage over your competition.
The bottom line? Go all in with AI — this is not another buzzword destined for the cutting-room floor. Just remember not to leave business fundamentals behind in the process.
What do you think is most critical? The biggest risk(s)? Something here? Something else? Reach out — we'd love to hear your thoughts.