What powers your AI also powers your performance.
AI is everywhere, and most organizations know they need to leverage it somehow. The truth is simple: AI only creates value when the right foundations are in place. The challenge now is figuring out where it fits best and ensuring it’s solving real business problems, not just adding more noise.
This technology isn’t a magic fix; it’s a tool for leverage that only delivers value when strategy, data, workflows, and culture are aligned. When applied with purpose, it can unlock efficiency and speed across your go-to-market organization. But too often, companies chase AI for AI’s sake.
Before making big investments or rolling out AI pilots, pressure-test your readiness by asking these five critical questions:
1.What GTM problem are we solving?
As with any product, before building a solution, you must identify the business hurdle you’re addressing. Maybe your forecasting is unreliable, or your incentive plans aren’t driving the right behaviors. When you focus AI on specific, measurable problems, you can design solutions that drive tangible outcomes.
2. Is our compensation data clean, current, and connected?
AI is only as smart as the data you feed it. Inaccurate or siloed compensation data doesn’t just reduce the value of your models – it can lead to flawed insights and erode trust in the system. You should always ensure your compensation data is complete, continuously updated, and seamlessly integrated across platforms. Connected data gives AI the context it needs to spot patterns, predict outcomes, and recommend meaningful actions that your team can take.
3. Can our workflows act on AI outputs?
Insights are only valuable if you can act on them. It’s one thing for AI to tell you your sales quota is lagging in a specific region or for a specific product – it’s another for your workflows to automatically alert leadership and suggest next steps or strategies. Embedding AI into existing sales and compensation workflows ensures insights translate into outcomes.
4. Will this scale beyond a pilot?
When looking to start your AI journey, you want to have the end goal in mind as well. A successful pilot is a start, not the finish line. To make AI sustainable, think ahead: How will models evolve as your business grows? What governance frameworks will you need to ensure data quality, security, and compliance? Who will own ongoing maintenance? Broad scalability with AI requires infrastructure and a long-term operational mindset.
5. Are we ready – organizationally and culturally?
AI success depends as much on people and culture as it does on the actual technology. Teams need to understand why AI matters, how it fits into their daily work, and essentially what it means for their roles and for the company. Ensure you are investing in a thorough change management strategy during your AI implementation process. When users feel empowered by AI – not replaced by – adoption and impact follow naturally.
AI can transform revenue performance, but only if it’s built on solid ground. By asking the right questions upfront, organizations can move from experimenting with AI to realizing real, measurable impact.
At Xactly, we’ve seen the best results when strategy, data, workflows, and culture align. AI doesn’t replace strong business fundamentals – it amplifies them, giving organizations an unfair advantage.
Ready to turn AI from hype to real impact?
Discover how Xactly’s AI-powered revenue performance solutions help organizations turn data into action, align teams, and drive sustainable growth with the Unfair Advantage: https://ai.xactlycorp.com/