Discover best practices for measuring the value of your generative AI pilot implementation and sustaining its value into the future.
Sales AI
February 6, 2025
Generative AI (GenAI) remains a beacon of innovation across industries. While GenAI promises to simplify and support complex technical processes for high-performing corporate teams, generic GenAI implementation has fallen short of expectations for many.
The reality is that many companies are frustrated with artificial intelligence initiatives that promise gains only to provide little value.
If you’re disappointed in the reality of generative AI, there are a few reasons why the technology may be failing you. More importantly, there may be alternative approaches that can turn the tide for your organization and help you realize ROI. Let's dive in.
GenAI initiatives often fail when they are not piloted properly or lack the support of a cohesive corporate strategy. Even if these two steps are achieved, using the wrong GenAI platform for your company can limit its benefits to users and provide lackluster results.
At Nesh, we talk to technical commercial teams looking for an AI solution that truly performs every day. Let’s look at some common reasons why the wrong system or incorrect AI implementation can fail.
One of the biggest reasons GenAI projects fall short is that companies lack clear, industry-specific use cases. Many jump on the AI bandwagon without realizing there must first be a concrete purpose and plan to use it effectively.
Any company can implement generic GenAI like ChatGPT. But if there’s no workflow, training, or clear objective, using these tools becomes a directionless experiment. And such experiments do not deliver tangible or measurable business results.
Even when experimenting with industry-specific AI services, the right metrics are needed to guide pilots that achieve GenAI’s potential.
Take this example. A chemical manufacturer might implement a general-use AI chatbot for customer service only to discover the chatbot can’t handle the data texture and technical specificities of product inquiries. The misalignment between the GenAI tool and its use case fails to improve the chemical company’s operations and risks eroding customer trust.
In structured settings like chemical manufacturing sales where precision is paramount, aimless GenAI adoption can be a recipe for chaos. Companies that allow ad-hoc adoption without clear guidelines or strategies end up with scattered, inconsistent usage across departments. This fragmented approach diminishes efficiency and makes it impossible to leverage AI in a way that benefits the organization.
Consider a scenario where the technical services team uses one GenAI tool for understanding product applications in a specific use case and the sales team uses a different tool to analyze which products are the best fit for a customer's use case. The lack of integration is bound to end in missed opportunities for cross-functional insights that could have driven innovation and sales.
Too often, GenAI is treated as an exciting novelty or an interesting “toy” instead of a sophisticated business tool. Adopting generative AI with this popular mindset leads to mistakenly adding the technology into core business processes. Without meaningful, thought-out integration, measuring ROI becomes challenging, often leaving executives wondering why they invested in the first place.
In other words, using AI to generate social media content might seem cutting-edge. But if that content is not driving engagement or supporting broader marketing goals, it's little more than a costly distraction.
GenAI programs don’t serve every industry in the same way. Instead, GenAI should be used to address industry-specific challenges and opportunities and necessary use cases.
Marc Benioff, CEO at Salesforce, points out that GenAI tools such as Microsoft’s CoPilot may be causing more setbacks than they’re solving.
“When you look at how CoPilot has been delivered to customers, it's disappointing,” he shares. “It just doesn't work… it's spilling data everywhere, and customers are left cleaning up the mess.”
To avoid this mess, some companies turn to build their own custom LLM. As Benioff’s points out, AI can be a nuisance even then. He critiques, “I have yet to find anyone who's had a transformational experience with Microsoft CoPilot or the pursuit of training and retraining custom LLMs. CoPilot is more like Clippy 2.0.”
Companies need GenAI systems that understand the intricacies of their operation. Chemical companies especially need solutions that can handle complex product portfolios, formulations, regulatory compliance requirements, and global supply chain logistics.
Learn how to implement a GenAI strategy and pilot program that works with this free step-by-step guide from Nesh.
To avoid the risk of failed initiatives and loss of investment, first consider the specific needs of your industry, company, and team.
In complex industries like chemicals, successful GenAI implementation requires choosing the right tools for the job. This means looking beyond popular, general-purpose AI and focusing on solutions tailored to specific domains, users, and objectives.
When evaluating solutions, consider:
By prioritizing industry-specific capabilities, you can make a more informed decision that addresses real challenges.
Successful GenAI adoption starts with well-designed pilot programs. Here’s an example of what that could look like:
Even the most sophisticated AI tool is only as good as the connected data and how user interact with that data. Having a comprehensive training program that goes beyond basic functionality can ensure that your team understands not just how to use the AI, but how to integrate it into their daily workflows for maximum impact.
Create a culture of continuous learning and improvement around your GenAI tool. Encourage users to share their discoveries and creative applications. Effective user guidance and training not only improves adoption rates but can also promote innovation from within.
Even with the right tools and training, successful GenAI implementation requires more. Effective GenAI programs require a firm leadership commitment and a clear vision. To be succesful, chemical company leaders must start with a top-down strategy that aligns GenAI initiatives with their company's overall business objectives. This strategy should:
Embed GenAI into your corporate strategy to ensure it becomes a core driver of value rather than a peripheral experiment.
The journey toward useful GenAI in the chemical industry may be challenging, but its potential rewards are immense.
By avoiding common pitfalls, focusing on industry-specific solutions, and going for strategic alignment, GenAI becomes a competitive advantage instead of a source of frustration.
Don't let GenAI continue to fail you. Instead, test a different solution that better aligns with your chemical company's unique operations and goals.
Contact Nesh to learn how our industry-specific sales AI platform can take you from being frustrated by AI to a huge proponent of its capabilities.
Generative AI is a game-changer, but achieving real value requires the right strategy from the start. Download our Generative AI Playbook to learn our insights from helping companies across the advanced manufacturing and chemical manufacturing value chain achieve ROI from GenAI implementations.
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