The technical sales process is broken. Learn what it takes to 10x your Technical Sales.
Sales AI
August 15, 2024
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Venkat Tummalapalli is the VP of Customer Success & Services at Nesh.
40% of companies have failed to take any action on Generative AI, according to Boston Consulting Group. Reasons for inaction range from lacking a robust plan around enterprise data, no buy-in from leadership to guide the project forward, or even not knowing how to get value out of a use case or generative AI pilot.
One of the biggest challenges with adopting Generative AI is often where to start. This question is so profound that more often than not, companies stall at this stage, mired in questions of data, security, and who owns what, and should we build vs. buy. MIT Technology Review notes that 57% of manufacturers are concerned by weak data quality. More than that, 75% of chemical companies expressed concerns over improving data integration.
Today, only the top 10% of companies that are winning the generative AI race have completed pilots and are scaling use cases across their enterprises. These companies have figured out the best ways to capture value from specific use cases. They’ve proven value at the pilot stage and successfully positioned a GenAI solution to scale across use cases, teams, or business units.
Figuring out viable use cases that deliver value is the first step in ensuring your generative AI project is successful. If you’re still unsure what use case to pursue, don’t miss the first part of this series, Measuring the Value of Your Generative AI Implementation. It features a deep dive into generative AI use cases that deliver value.
If you’ve got a use case in mind, let’s dive into the next step: planning for your GenAI implementation and setting your pilot up for success.
Proper planning ensures you set yourself and your company up for an optimal experience that avoids time waste traps and delivers success. Keep reading for best practices on how to approach your generative AI pilot, key factors to consider when evaluating your pilot, and how to sustain your generative AI implementation.
Socializing the value of generative AI to your organization is a critical step in building momentum for your project. Consider opportunities with your team up to your company’s Board of Directors to demonstrate ways your Generative AI project does more than just enhance productivity but delivers new avenues of value like top-line revenue growth or preventing knowledge loss. Building trust with the company’s board of directors entails careful consideration of their priorities as they deliver accountable corporate performance.
Turn leaders into advocates who can speak on behalf of your project even when you’re not in the room. Find the balance between cultivating advocates and retaining your autonomy to move projects forward.
Exploring multiple tools helps ensure you find the right solution for your use case and for your company. Level setting internally with tools like CoPilot can help ensure you understand and can communicate why similar tools might not be sufficient for realizing your ultimate goals. One of our customers had an Azure-first technology mindset and a process that required careful consideration of timeline and tradeoffs before exploring market alternatives. Make sure you understand these constraints and how to move forward.
Explore multiple options and vendors. Demonstrate you are pursuing specific tools for achievable value instead of piloting GenAI for the sake of it. Documenting your decisions helps build confidence and credibility in your vision, project, and vendor as you move forward. Buying, partnering or building internally is a key decision and experimentation will be helpful.
Pilots should be focused and simple with a clear goal in mind. This ensures you can set objectives that justify value.
If capturing expert knowledge before SMEs retire is your use case, set objectives tied to capturing that knowledge and delivering it in ways that are useful to other team members. If improving safety is your goal, set objectives tied to better enforcement of policies and health and safety standards. If improving the consistency of your sales reps messaging in customer conversations is your goal, set objectives tied to the accuracy, completeness, and readiness of the GenAI answers.
For more on how to set objectives and metrics tied to value, check out part 1 of this series, Measuring the Value of Your Generative AI Pilot Implementation.
The data practices of the past do not work in the modern AI tech stack. Work with your IT team early to discuss an organizational structure that prioritizes a modern approach to data. Your data pipeline is more important than ever and is full of untapped value. Without proper planning for data access, your GenAI project will likely fall short of expectations.
This doesn’t mean you need access to all of your company’s data. Think through the data you need to make your use case successful. Is your project focused on ensuring your technical sales reps use consistent, updated product messaging? How can you work with marketing and IT to ensure your data corpus delivers the right information for project success?
(And don’t be afraid to lean on your GenAI solution provider for guidance – we’ve seen it all!)
Does your pilot test group have a big project deadline that might impact their willingness to test a new solution? Does IT have a big initiative that will hinder their ability to assist with provisioning tech, VPC deployment, or data assets? Do the security or privacy requirements of your tech stack require you to use a VPC instead of a SaaS solution?
Be sure to account for timing needed for IT provisioning as well. Considerations for VPC and SaaS implementations are often vastly different time investments for your IT team.
Successful pilots take anywhere from 3 months to a year to plan and execute depending on scope. Be sure you have an understanding of how the timing you select could be impacted by organizational factors outside of your control.
Rush or skip this step at your peril. After you’ve set your use case and tackled the above, take the time to document who owns what aspects of the projects, who your onboarding team is, project advocates, why you’ve chosen the GenAI solution vs. others for your pilot, the objectives you’ve set tied to clear value, and how your project will work with IT for data access and resources.
Resocialize what you’ve done, learned, and planned to ensure your onboarding team, IT, and company leaders are onboard and ready to support your vision, pilot project, and timeline. If stakeholders that were originally involved in the project have moved on, take the time to socialize the business case with the new stakeholders to sustain enthusiasm and interest in the initiative.
As you begin your pilot test, work with your GenAI partner to provide a starter pack or initial guidance for how your pilot users should use the solution and derive value. Don’t forget: The faster users are onboarded, the faster they’ll realize value.
Plan to gather feedback from your test users at regular intervals. Use feedback to improve your pilot overtime and work towards an end solution that meets your full requirements.
Once you’ve determined your GenAI pilot was a success, your next task is to sustain momentum, test more use cases, and scale your solution across other teams and business units. Here are a few things to consider as you move forward beyond your initial generative AI pilot stage.
Your use case and the solution work great, but none of that matters if no one uses your solution. Think through ways you can integrate your project into the user’s flow of work through familiar tools and processes. Do you have an internal solution or app employees access every day?
Look for ways to use APIs that integrate your solution into a familiar interface so users can seamlessly access your solution in their flow of work. Bringing your GenAI project into the critical path of the users for their day-to-day activities is central to adoption.
User feedback is a gold mine with emerging technologies like GenAI.Use feedback to drive iterative improvements and refinement that deliver even greater value.
Improvements can include adding new features, expanding knowledge bases, tailoring solutions for different risk tolerances, and improving model performance to enhance quality of responses based on user ratings.
The companies that succeed in deriving value from Generative AI are the companies that are open to rethinking their tech stack and their data management practices. This won’t happen overnight, but work to continuously improve data quality and data management over time.
Of course, work with what you have. Don’t let poor data fears prevent you from moving forward.
Starting these data improvement projects now will unlock even more opportunities down the road. Your initiative should be used to inform which data assets to focus on for improvement. Instead of taking the next 20 years working in silos, knowledge analytics can help prioritize the data quality initiatives and get the corpus to a reasonable state.
You can’t communicate success without measuring value. Keep monitoring your use cases and their value metrics over time to justify the resources dedicated to your GenAI solution. Communicating how your solution impacts topline and bottom-line metrics will help sustain long-term investment in your solution.
Don’t stop now! You’ve tackled the hardest part - getting started. You’ve proven it’s possible to successfully pilot and launch a new solution that delivers value. Take a moment to recognize that you’re among the few people at your company who understand the challenges of implementing GenAI.
Use your understanding of GenAI’s capabilities and your company’s pain points to keep generating more value. Work with your solution provider, leadership, or teams that you routinely work with to explore additional opportunities to add value. New use cases. New teams. New business units.
And don’t forget to occasionally revisit old ideas that didn’t work before. As your data capabilities improve and GenAI tech advances, new opportunities will arise.
Getting started with generative AI is often daunting for even the most sophisticated enterprises.
Launching a GenAI pilot requires tackling challenges like navigating data quality, determining valuable use cases, gaining buy-in, and setting aside resources.
But each of these challenges is surmountable with the right plan for your GenAI pilot and the right solution provider. Nesh works with companies across the chemicals and materials value chain to solve real challenges that deliver revenue growth — even in the most competitive markets. The toughest data challenges relating to AI in chemical companies can be overcome, and pragmatic approaches entail identifying usable data for AI to drive top and bottom-line growth.
Want to learn more? Get a personalized demo to see how Nesh can help your company today.
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