Consistency beats intensity any day. If you're just chasing the next big news or the next update or the next tool, that will give you incremental gains. But if you actually treat AI as a long-term opportunity and less of an efficiency play, that's where the real value is.
New AI tools are dropping every week and it's easy to get caught chasing headlines. But the AI experimentation window is slamming shut, and quick wins can’t compete with companies embedding AI with long-term purpose across core systems and daily decisions.
*Rahul Singh, Data Science Manager at Adobe, sees AI "evolving like crazy." Instead of chasing every shiny new object, he values consistency over chaos.
Marathon, not sprint: "Consistency beats intensity any day," Singh says. "If you're just chasing the next big news or the next update or the next tool, that will give you incremental gains. But if you actually treat AI as a long-term opportunity and less of an efficiency play, that's where the real value is." His point hits home as more companies realize the "phase of experimentation is already coming to a close" and start treating AI as a core part of how they operate.
Pilot purgatory: Plenty of companies are using AI, but far fewer have figured out how to scale it meaningfully. That gap backs up Singh’s point that those not thinking strategically are "already pretty behind." His advice: "Start narrow" with real use cases, prove the value, then "scale those learnings."
Everybody is back to being a student. Everyone has to play around with these tools to understand the pros, the cons, the implications, because even leaders are getting hands-on now, and that shows just how much the old playbook has changed.
Learner’s permit: Scaling, Singh says, takes a hands-on, student mindset. "Everybody is back to being a student. Everyone has to play around with these tools to understand the pros, the cons, the implications," he says, "because even leaders are getting hands-on now, and that shows just how much the old playbook has changed." To cut through the noise, Singh asks: "Can I apply this to my workflow? Will it shift how I think about what I do?" That kind of practical filter is key—especially with big shifts like the rise of agentic workflows, where AI can manage complex tasks on its own.
Tools for everyone: The "democratization of AI tools" is making advanced tech more accessible, and that means companies need to upskill beyond just their technical teams. Singh believes "staying grounded on use cases, applications and impact" is what helps teams unlock real value, no matter how fast the tools evolve. It’s not about chasing every new feature; it’s about building the muscle to turn AI into impact again and again.
*The views expressed in this story are Rahul Singh's personal opinions and do not reflect or represent those of Adobe.
