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Developing an AI Strategy for Your Sales Team

Artificial Intelligence (AI) has the potential to transform the productivity and output of your sales team. That said, AI is a means to an end and not the end itself.  So, when you think about an AI-first strategy you need to start with your strategy and then identify the use cases where AI could have a transformative effect. I say “could” since even though AI has the potential to transform your team’s productivity, you need to systematically evaluate this across multiple criteria, track results, and iterate toward your desired outcome.  

AI models ultimately get really good at solving for a particular objective that they are trained for. So, it’s critical to start with a set of objectives that you have for your Sales organization. These objectives relate to something that you need to get better at, for example prioritizing your most valuable accounts. Alternatively, the objective could relate to something you are not able to do today but really need to do, for example personalizing emails at scale. Or it could be repetitive tasks that if automated, will free up your team for higher-value tasks like making more calls.

Let’s look at the steps that you can take to develop an AI strategy for your Sales team. 

Start with the Objectives: AI is not a monolithic technology but a collective term for a set of technologies that can each be really good at separate objective functions if properly trained. AI can solve a variety of use cases based on classification, optimization, prediction, natural language processing and understanding, and many more. By starting with the problems that your team is struggling with, you can arrive at the most important objectives that move your business forward. Then you can map each of these objectives to the matching AI solution to evaluate whether this approach will move the needle for that objective. Moreover, objectives provide a framework for evaluating the performance and effectiveness of the AI system, enabling stakeholders to assess whether it is achieving its intended outcomes. Objectives also play a crucial role in ensuring the ethical and responsible use of AI, by setting boundaries on the system’s behavior and preventing it from engaging in harmful or unethical activities. 

Identify the Right Data: The phrase very commonly used with AI is “Garbage in Garbage Out”. While that may seem negative, it’s actually quite accurate. AI gets really good at what you train it to do. This is done by training AI on examples, also known as sample data, which is used to teach the AI model how to recognize patterns and make predictions. The positive spin on this is that if you have good quality data and ideally lots of it, AI will get very good at solving for your objective. In fact, the more diverse and representative the training data is, the more robust and effective the model will be in real-world applications. 

So after concluding that AI can move the needle for your objectives, you need to outline the data that would be required to train AI for your use cases and ensure that you have access to the training data. Ultimately, the quality of the training data is a critical factor in the success of any AI project, and investing in relevant, accurate, and unbiased training data can yield significant returns in terms of accuracy, reliability, and business impact.

Experiment and Iterate: While it’s tempting to go “all in” if you have a fit and have access to the training data, a crawl, walk, run approach would be prudent for most. You should run some experiments at a smaller scale to sanity check whether potential translates to results and scale out from there. One example is A/B testing messaging, where one group of customers receives the communication without AI assistance, and the other group receives the same communication with AI-powered personalization. The results can be analyzed to see which version of the communication was more effective in generating a response. Another example is validating the use of AI in sales is lead scoring where the sales team can be split into two groups, one group receiving leads scored by AI and the other group receiving leads scored manually. The results can be analyzed to see if the AI scoring is more accurate and effective in generating sales.

Pick the Right Infrastructure: Training and running AI at scale is a complex and expensive endeavor. You could choose to set up the infrastructure and operations in-house and maintain custom models from the ground up. This would not be the right solution for everyone. At a minimum, AI infrastructure typically requires high-performance computing resources, such as GPUs, to accelerate training and inference tasks. Storage and memory are also critical, as AI models can require vast amounts of data to be stored and accessed quickly. In addition, specialized software frameworks and libraries, such as TensorFlow, PyTorch, and sci-kit-learn, are essential for developing and deploying AI models. Cloud-based services, such as AWS, Google Cloud, and Microsoft Azure, provide access to these resources and offer scalable infrastructure that can be provisioned and de-provisioned as needed. Further vendors such as Open AI, Google Cloud AI, etc. provide LLMs as a service that you can “rent” without taking on the operational overhead. 

Develop or Obtain the Required Expertise:  Another big factor to consider would be the level of expertise that you have within your team to do all of the above. You can always hire and train your team to build greater expertise. Alternatively, you can choose to work with vendors with this expertise that provide solutions that integrate with your existing stack and workflow. The latter can be the fastest time to value for you and you can build expertise over time and re-evaluate at a later time. Do note that whichever approach you take, you will have to train your team on using the solution effectively while continuously monitoring and fine-tuning the solution.  See our post on B2B Sales use cases for some examples.   

Based on these criteria you can systematically determine where and why AI can help your sales team and arrive at the best way to light this up for your current situation to drive business impact. 

Looking to apply AI to identify the most valuable and buying-ready prospects in your TAM? Relevvo can help. Drop us a line here and we’d love to chat.


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