You had the vision, the tech, and the promise of transformative automation—but instead of soaring, your project hit the dust. Maybe adoption is lagging, data inconsistencies are throwing off predictions, or your team isn’t using AI the way you expected. 

You’re not alone. Research from the Boston Consulting Group found that 74 percent of organizations struggle to identify tangible value from using AI. Only 26 percent of companies have developed essential AI capabilities to “move beyond proofs of concept and generate tangible value.” 

Launching AI-powered workflows like Salesforce Agentforce isn’t just about flipping a switch—it’s about precision, strategy, and avoiding the common pitfalls that can send your project into a tailspin. 

If your RevOps team struggles to get their AI-powered workflows, like those in Agentforce, off the ground, don’t worry. The Simplus Business Transformation Services crew is here to help. 

 

Pre-Launch Checklist: Setting Your AI Up for Success

Would you launch a rocket without checking the fuel, testing the systems, or briefing the crew? Of course not—yet many teams rush into AI implementation without laying the groundwork for success. 

The result? Confusion, miscommunication among crew members, and a project that never reaches liftoff. Remember that AI, especially within Agentforce, isn’t plug-and-play—it requires careful planning, clean data, controlled testing, and, most importantly, a team ready to embrace it. Before you engage those thrusters, run through this pre-launch checklist to ensure your AI is prepped for a smooth and successful mission.

Before you engage the AI-sourced thrusters, review this checklist to ensure your AI initiative is built on a solid foundation:

 

Establish Clear Mission Objectives 

Define what success looks like—whether it’s faster case resolutions, improved lead scoring, or more personalized customer interactions.

 

Fuel Your AI with Clean Data 

AI is only as good as the data it’s trained on. Ensure your Salesforce Data Cloud is clean, structured, and continuously updated.

Eighty-six percent of analytics and IT leaders say that AI’s outputs are only as good as its data inputs. “To get the most out of data for small businesses, it needs to be ‘clean,” said Brett Grossfeld, product marketing manager, Salesforce Growth Products. “A perfectly clean contact list has only one entry per contact, with all of the information consolidated under each entry—name, physical and email addresses, phone numbers, and so on—accurate and up-to-date. Any duplicate entries or inaccurate info are examples of unclean data.” 

 

Test in a Low-Gravity Environment 

Before full deployment, run controlled pilot programs to work out any kinks without disrupting daily operations.

 

Align Your Crew (aka, Your Team)

AI adoption isn’t just a tech challenge—it’s a people challenge. Train your sales and service teams to trust and use AI insights effectively.

 

Avoiding Black Holes: Common AI Pitfalls

Many RevOps teams launch their go-to-market with high hopes, only to watch their AI initiatives spiral into confusion and frustration, with no options but to abandon the mission altogether. The workflows may be so complex that no one actually follows them. Or worse, the AI was set up once and left to drift, running the risk of growing less accurate and useful over time. Without some strategic course corrections, your AI investment can become a costly galactic void. Even with the best prep, some projects drift off course. Here’s how to avoid the most common AI implementation failures:

 

AI Without Human Oversight  

AI should augment, not replace, human decision-making. AI has exciting capabilities but is still new, with plenty of limitations in maintaining non-biased accuracy. “People need to embrace new technology, but they also need to be motivated to upskill along with it so that the jobs of the future are filled and workers feel confident about using AI to grow their careers,” Sania Khan, an economist and head of market insights, said in a recent Inc. article. “As roles get automated and overall efficiency rises, there will be a constant pipeline of upskilling and reskilling needed to make the best use of the workforce.” Your strategy should keep a human in the loop to ensure quality control. 

 

Overcomplicated Workflows 

If AI recommendations are too complex to follow, your team won’t use them. Keep insights actionable and easy to understand. The root of success is simple: focus on the problem. That means decisions about AI-sourced technology should offer direct solutions for those problems. 

 

Failure to Continuously Optimize  

AI models need ongoing tuning. Monitor performance and make adjustments to keep your AI mission on track. Just like a spacecraft needs mid-course corrections to reach its destination, your AI models require continuous tuning to stay accurate and effective. 

If you’re not actively monitoring performance and making adjustments, your AI will drift off course—leading to shoddy predictions, frustrated users, and missed opportunities.

 

The Final Frontier: Achieving AI Takeoff

With the proper preparation and execution, your Agentforce AI implementation can soar. Stay agile, keep learning, and embrace AI as a co-pilot rather than an autopilot. 

Need help troubleshooting your AI project? Let’s talk about how to recalibrate your strategy and achieve liftoff.