How to use AI
effectively

with sales & marketing

15 min read

You don’t have to look into the chapters of science fiction novels anymore to see a new player on the scene of business – that of Artificial Intelligence (AI). In truth, AI has been with us for quite a while now, but its impact on business processes in the last decade has been tremendous.

AI stands at the forefront of innovation, promising to revolutionise the way we live, work, and interact with the world around us. We’ve seen it at work in the areas of customer service, business planning, administrative support, reporting, copywriting, GPS navigation, and more, but what specifically does it mean for the world of sales and marketing? And, more specifically, how can it help your team close more business and increase revenue?

What AI capability are you most excited about exploring for your sales team?

A recent SBR Consulting survey of clients found most organisations were equal parts excited vs. uncertain about the use of AI with their teams, with not much in the middle.

What is AI, and how did we get here?

At its core, AI refers to the simulation of human intelligence processes by machines, particularly computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.

In simple terms, AI enables machines to mimic cognitive functions such as problem-solving, decision-making, and language understanding, that have been traditionally associated with human intelligence.

There are different types of AI, depending on how you define them. Narrow AI is programmed to conduct a specific task, or limited range of tasks, such as virtual personal assistants (Siri, Alexa, etc.) or chatbots who can recognise pre-programmed questions or keywords. General AI, the next evolution of AI, adds a layer capable of understanding, learning, reasoning, and adapting to diverse tasks and situations.

Within the broader categories of AI, there are several subsets or components:

Generative AI refers to a new class of AI that enables machines to generate new content, such as images, text, audio, or even video, that is similar to or indistinguishable from human-created content. Unlike traditional AI systems that are programmed to perform specific tasks or analyse data, generative AI models are trained on large datasets and learn to generate original content by understanding and mimicking patterns within the data.

Machine Learning (ML) is a subset of AI that focuses on developing algorithms capable of learning from data and making predictions or decisions. ML algorithms improve their performance over time, as they are exposed to more data.

Natural Language Processing (NLP) focuses on enabling computers to understand, interpret, and generate human language in a natural and meaningful way. NLP algorithms power applications such as language translation and chatbots, enabling seamless communication between humans and machines.

These three specifically make up the core of many of the AI tools that have quickly become popular in the corporate world, for all the reasons stated above. According to a 2023 article by the Harvard Business Review, generative AI has already helped writers draft content and programmers write code, boosting their productivity by 50% or more. It can do the same for salespeople, they argue. ¹

What are some of the limitations of AI?

While artificial intelligence can come close to simulating human thoughts and interactions, in the end, it’s only technology. There have been concerns about accuracy, data, privacy, copyright, and other infringements as AI has so quickly burst onto the scene.

“AI should only be used to save time” recommends SBR Consulting Managing Director Alan Morton. “It certainly should not write your proposals for you. It should reduce the time taken to get to where you would have got to without it.”

At SBR Consulting, some of the most common challenges heard from clients are:

Data Privacy and Security

By integrating AI into your CRM, safeguarding customer data and protecting it from data breaches becomes more important than ever.

Integration with Existing Infrastructure

AI integration can be complex and requires careful planning and execution.

Resistance to Change

Employees are hesitant to adopt new technologies, or unsure of the benefits of AI.

Data Quality

Poor-quality data or misinterpreted data can lead to inaccurate insights.

Cost and ROI

The cost of implementing AI and justifying the expense with a clear ROI can often be challenging.

In short, sales managers are likely thinking, Is AI really going to make things better, or is it going to make it worse? We don’t want to adopt something simply because it’s interesting or exciting. We want to invest because it’s going to make things better for our people.

All valid points, says Devang Agrawal, Founder of Glyphic, an AI tool for sales teams. “One of the biggest challenges we’ve seen with the sales teams we’ve worked with is that each [piece of technology] feels like an additive tool. They don’t integrate well with each other. And you’re context-switching between a lot of tools.”