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As a leader or technology decision-maker in your organization, you’re tasked with making decisions about how to implement generative AI (genAI) - or if you even should. Though it can be tempting to dismiss genAI as another over-hyped technology, the reality is that it’s here to stay - and your competitors are already using it. The key to genAI’s value is the fact that it makes data easy to understand and use in real-time, and at scale, thanks to its ability to share insights in plain language.
Organizations around the world and across every industry are using genAI to make themselves smarter and faster - and capture market share. By developing your generative AI roadmap now, you can position yourself as the genAI hero in your company.
The key lies in identifying the right tools to address the right use cases that offer the most value with minimal time and effort. This also means selecting a tool that's user-friendly and non-technical enough for a wide array of employees. This may sound like an impossible task but there is one overwhelming requirement for a valuable genAI solution that outweighs the rest - the quality, relevance, and breadth of the data on which it is trained and the real-time data sources it has the ability to analyze. These various data sources together fuel the tool and are the foundation on which it is built. A generative AI tool’s ability to give you highly-relevant recommendations that are also true (and not hallucinations) is entirely dependent on its’ data foundation. I’ll explain more below.
How do I find a generative AI solution that will tell me the truth about my business?
There are so many genAI solutions out there right now that it can feel like it’s raining chatbots. Technology vendors in every industry are assuring you that their solution is the one that will make your business fastest, smartest, and most competitive. But not all genAI tools are created equal. The biggest differentiator lies in a solution’s data foundation - which isn’t always immediately apparent when you’re getting a great demo showing off amazing UI and capabilities.
Many solutions are only capable of revealing partial truths about an organization because they are restricted to data from a limited number of sources. Or a genAI solution that wasn’t trained on or doesn’t have access to enough data may generate hallucinations. A genAI hallucination is when a tool fabricates an output that isn’t based on reality. For organizations that are relying on a genAI tool for critical functions such as revenue-generation or HR, hallucinations can be detrimental to the business and harmful to employees and customers.
The best (and in my opinion, only) way to get consistently true insights from genAI is to select a vendor that has purpose-built their tool for a specific function and has the right business function-specific data foundation in place. If you were selecting a genAI sales tool, you would want to find a vendor that has been aggregating, cleaning, and organizing activity data from a large number of anonymized GTM teams for many, many years - not just in the last few months since genAI became a hot topic. And that tool must have the inherent ability to collect and analyze a vast amount of real-time, organization-specific data from many different sources. Again, if you were selecting a genAI sales tool, you would want it to be able to use your CRM data, your team’s captured GTM activity data, other relevant company data, and the aggregated data already mentioned for deeper relationship insights.
“The best way to get consistently true insights from genAI is to select a vendor that has purpose-built their tool for a specific function and has the right business function-specific data foundation in place.”
How do I identify the right generative AI use cases?
Generative AI can support nearly every team in an organization with an incredible array of use cases. From streamlining recruitment processes in HR to creating synthetic data copies of actual sensitive data for IT teams. Marketing teams can use it for developing landing page copy and sales teams can leverage genAI for automated completion of account plans.
Explore more generative AI use cases for sales teams in this brief
When developing your generative AI strategy and roadmap, focus on the use cases that can offer the most immediate value. In a recent report, McKinsey analysts said, "Generative AI could have an impact on most business functions; however, a few stand out when measured by the technology’s impact as a share of functional cost. Our analysis of 16 business functions identified just four—customer operations, marketing and sales, software engineering, and research and development—that could account for approximately 75 percent of the total annual value from generative AI use cases."
Should I use a public generative AI tool, buy a private genAI tool, or build a custom one in-house?
Public genAI tools like ChatGPT can be useful for answering very general, high-level questions but they lack the specialized data and functionality to be truly helpful. They also pose security and privacy data exposure risks, leading many organizations to restrict their use, especially in highly regulated industries like finance and insurance. In order to use generative AI technology at scale, organizations need to adopt private tools.
Private genAI tools are purpose-built to seamlessly integrate with your existing technology stack and working style. Your end-users are far more likely to use a tool that offers helpful, data-driven insights and is embedded within the workflows and tools they’re accustomed to. For instance, sales teams can benefit from specialized genAI tools embedded within the organization’s CRM.
If you have the time and resources to build an in-house generative AI tool, that’s a great option from a security and privacy point of view. But it’s a long process - at least 6-12 months - and ultimately will likely solve a limited number of use cases. There are high-quality, out of the box solutions that deliver near-instant value.
How do I find the right generative AI vendor?
When deciding on a genAI vendor, select one that adds immediate value and is easy to use. Here are a few key considerations:
- Data is the differentiator: Data is truly the differentiator between mediocre generative AI tools and exceptional ones (see section on data above).
- Leverages both public and private large language models (LLMs): Most private genAI tools start with a public LLM foundation and then layer a private LLM on top of it. By using a public model like Azure’s version of OpenAI, a tool gains all the expansive knowledge benefits of the public OpenAI model but doesn’t push data back to the public model. The private LLM layer sits on top of the public one and acts as an extra security layer with minimized risk. The private layer also gets trained on data specialized for its’ use case, making it far more useful than a public tool (see previous section).
- They should take security as seriously as you do: As with all new technologies, a vendor’s security policy should be one of your top considerations. Your company’s infosec team will make sure of it! There are some things you can look for to make sure your new genAI technology partner is as serious about security as you are. First, pick vendors that have large enterprise customers. Chances are their security requirements are even more strict than yours. You should also select vendors that put security and trust as their top priorities for genAI and have documented how they’re going to protect your data.
- Specialized for your industry and use cases: Public genAI tools are like Swiss army knives. They’re useful for a wide variety of things but not great at any one thing. The generative AI tool you choose for your organization should specialize in the industry and use cases you’ve identified as top priority. Chances are you may end up adopting multiple different tools for different teams in your organization.
By focusing on these key areas, you can provide immediate value to your organization using genAI. With the right implementation and vendor partner, generative AI can automate mundane tasks, allowing your teams to focus on strategic, value-generating activities.
Ready to learn about how generative AI is going to impact sales teams? Read more here.