The Concept of AI/CRM Hyper-Specificity

Introducing AI into an organization's processes requires careful consideration and planning. Unfortunately, many organizations overlook a fundamental step, leading to disappointment and inefficiencies. This step, which I have coined in my upcoming book as "AI/CRM Hyper-Specificity" (AI/CRM HS), emphasizes precisely defining the problem or objective AI will address.? AI/CRM HS requires more identification and a more rigorous effort than other technologies that have come down the path over the past 10 years (and before) because the AI ecosystem is still forming, amazingly dynamic, and highly fluid.? AI/CRM HS requires a deeper level of identification and a more rigorous effort compared to other technologies that have emerged in the past decade.

The AI landscape is still in its formative stages, with new tools, directions, capabilities, and vendors being introduced daily. This makes it imperative for businesses to stay agile and adaptable in their approach to AI implementation.

To successfully navigate this complex landscape, organizations must invest significant effort in identifying the most suitable AI technologies for their specific needs. This entails conducting thorough research, evaluating the capabilities and limitations of different tools, and understanding the implications and potential risks associated with each option.

Furthermore, the evolving nature of the AI ecosystem requires a continuous learning mindset. As new advancements and breakthroughs occur, organizations must be ready to adapt their strategies, re-evaluate their technology choices, and explore new directions for maximizing the value of AI in their operations.

AI/CRM HS stands out as a disciplined approach to AI adoption due to the unique challenges posed by the constantly evolving AI ecosystem. Organizations can effectively leverage AHP to make informed decisions and drive successful AI initiatives by dedicating the necessary effort and staying updated with the latest developments.

AI/CRM Hyper-Specificity involves:

• Identifying specific use cases.

• Setting clear goals.

• Ensuring that AI algorithms are tailored to address those particular needs.
• Establishing measured goals for the AI effort/tool to specifically maximize ROI

By taking a focused approach, organizations can avoid the common pitfall of implementing AI without a clear direction, leading to wasted resources and unsatisfactory outcomes.

When optimizing lead scoring through AI, there's more to it than simply choosing an off-the-shelf solution. Instead, companies should take a more tailored approach to AI/CRM HS by analyzing their unique business requirements, defining ideal lead-scoring criteria, and developing a custom AI model that aligns with their specific needs. This way, they can maximize their ROI by ensuring their AI tool perfectly fits their operations.

This may involve selecting a generic solution or a highly niche tool provided by a third party or even building a custom in-house - whichever approach best suits the company's unique needs. By taking the time to go through this process, companies can bring significant value to their operations and stay ahead of the curve in their industry.