How Is AI Cost Reduction Enabling Sales Transformation?

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The decreasing cost of AI technologies is revolutionising various industries, with sales being a prime beneficiary. This can be seen both in open-source models that are increasingly capable with reduced sizes, such as diffusion models or small LLMs, or in the closed-source industry with GPT-4o mini from OpenAI driving down prices.

The evolution from simple AI use cases to more complex and integrated solutions is reshaping how businesses approach sales, making it imperative to adopt new strategies. A recent article from a16z “Death of a Salesforce”: Why AI Will Transform the Next Generation of Sales Tech has dissected new market organisations. The key for these is the reduced cost of AI. In this article, we will explore why and how it can be exploited to reshape the sales landscape. 

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Simple AI is not enough anymore

The decreasing cost of AI technologies has opened the door for more advanced applications, making basic AI models insufficient to match and redefine the sales landscape.

Limitations of simple predictions and classifications

Simple AI models, such as those used for basic predictions and classifications, are now very precise for simple applications. However, redefining the sales landscape as described in Death of a Salesforce requires handling context, omnicanal sources and performing actions.

For example, these models might predict which customers are likely to purchase based on past sales data, but they cannot autonomously sell a product in an end to end pipeline.  

Moreover, these simple AI models often provide generic insights that do not cater to specific needs or dynamic changes within the sales process. A basic predictive model might suggest potential leads based on past data but fail to identify new patterns or emerging trends that a more advanced model could detect leveraging non numeric data sources. As a result, sales teams might spend time on leads that are less likely to convert while overlooking more promising opportunities. This inefficiency underscores the limitations of relying solely on basic AI models for sales predictions and classifications.

LLM prompting limitations

In addition to the limitations of simple predictive models, the use of Large Language Models (LLMs) for prompting also falls short in complex sales scenarios. While LLMs, such as those used in chatbots, can generate human-like text based on prompts, they often lack the ability to understand deeper contextual nuances.

For instance, an LLM might be able to answer common customer questions but struggle to design a plan to satisfy a customer task or perform actions on its own. Adding context can increasingly avoid interactions that feel static and impersonal, failing to engage customers effectively.

Furthermore, relying solely on LLMs does not leverage the full potential of AI, as these models operate in isolation without integrating broader data inputs and contextual nuances. This isolation can lead to fragmented customer experiences, where interactions feel disjointed and fail to address specific pain points. 

Simple LLM use lacks the ability to be “intelligent”. However in this new sales landscape, AI systems will need to be more and more “intelligent” to help people or to perform tasks autonomously.

For more detailed information you can read our previous article “Is Generative AI Truly the Solution for Modern Sales?”

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Cost reduction leads to more performant AI systems

As the sales landscape evolves, businesses must adopt new AI applications that go beyond basic functionalities. Fortunately, the decreasing cost of AI technologies is enabling new opportunities, from the integration of various AI models, creating sophisticated systems that can handle complex tasks and provide deeper insights to autonomous machines.

Combination of models

One of the key advancements enabled by the reduced cost of AI is the combination of different AI models to enhance overall capabilities. By integrating predictive analytics, natural language processing (NLP), and machine learning (ML), businesses delve into a mass of new opportunities leveraging multiple data sources.

An integrated approach allows companies to analyse customer data more accurately, leading to better decision-making and strategic planning. For instance, combining NLP with predictive analytics can improve customer sentiment analysis, enabling sales teams to tailor their approach based on real-time feedback. This integration ensures that businesses are not just reacting to past data but are proactively adapting to current and future trends.

Moreover, using vision models in conjunction with predictive analytics helps identify hidden patterns and correlations in data that might otherwise go unnoticed. This synergy can significantly enhance the precision of sales forecasts and the effectiveness of marketing campaigns. For example, while a predictive model might highlight potential leads, an ML model could further analyse these leads to determine the most effective engagement strategy, thereby increasing conversion rates and getting feedback from textual or visual data.

AI agents and numerous AI model calls

Utilising AI agents to manage different aspects of the sales process can streamline operations and improve efficiency. These agents can handle tasks ranging from lead generation and customer inquiries to follow-ups, significantly reducing the manual workload on sales teams. AI agents are capable of learning and adapting over time, which enhances their efficiency and effectiveness. For instance, AI agents can analyse customer interactions to identify the most effective communication strategies, improving engagement and conversion rates.

Implementing numerous AI model calls allows for real-time data processing and updating, ensuring that sales strategies are always based on the most current and relevant information. Frequent model calls enable dynamic adjustments to sales tactics based on real-time data, such as changing market conditions or customer preferences. This real-time responsiveness ensures that sales teams are always equipped with the latest insights, allowing them to make informed decisions quickly.

For example, during a sales campaign, multiple AI models can work in tandem to analyse customer responses, predict behaviour, and suggest the next best action. One model might assess the sentiment of customer emails, another could evaluate purchasing patterns, and a third might suggest personalised offers based on this combined analysis. This multi-model approach ensures a more nuanced and effective sales strategy, leading to higher customer satisfaction and increased sales. 

This is all enabled by cheaper use of AI to increase the amount of AI use.

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AI everywhere using less expensive models

Utilisation of cost-effective expert models

Deploying less expensive, scalable models such as Small Language Models (SLMs) that are specialised experts in particular tasks can handle smaller but crucial jobs efficiently. These expert SLMs can be integrated at various points in the sales process, providing continuous support and automation.

For instance, specialised SLMs can be used to pre-qualify leads by analysing customer inquiries and categorising them based on relevance and potential value. This initial filtering allows sales representatives to concentrate on high-priority leads, thereby increasing efficiency and conversion rates. The cost efficiency of these expert SLMs allows businesses to deploy multiple instances without incurring significant expenses, making advanced AI accessible even to smaller companies.

Moreover, the scalability of these models ensures they can be easily adjusted to meet the growing demands of the business. As the volume of customer interactions increases, additional instances of SLMs can be deployed to maintain performance and responsiveness. This scalability ensures that businesses can continue to deliver high-quality customer experiences without compromising on efficiency or cost.

One can also read “How far are we from AI on the edge?” for further developments.

Collaborative AI systems

Establishing a network of interconnected AI systems allows for seamless collaboration between different models and agents. Each AI component can focus on specific tasks, while collectively contributing to the overall sales strategy. For instance, one AI system might handle lead scoring, another focuses on customer engagement, and a third manages follow-up communications. This division of labour ensures that each task is performed by the most suitable AI model, enhancing efficiency and effectiveness.

Interconnected AI systems can share data and insights, allowing them to learn from each other’s outputs and refine their algorithms. This collaboration leads to continuous improvement in accuracy and efficiency. By sharing data and insights, interconnected AI systems can adapt to changes in customer behaviour or market conditions more quickly than isolated models.

For example, during a marketing campaign, an AI system responsible for analysing customer interactions might identify a common question or concern among potential buyers. This insight can be immediately shared with another AI system responsible for generating marketing content, enabling it to create targeted responses that address these concerns. This real-time collaboration enhances the overall effectiveness of the campaign, leading to higher engagement and conversion rates.

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Conclusion

The decreasing cost of AI technologies is revolutionising the sales landscape by enabling sophisticated, integrated AI applications. As we look ahead, businesses must embrace these advancements and explore new AI-driven strategies to rethink the sales market described by a16z in “Death of a Salesforce”.