NWoS 41: AI for Sales, 5 principles to use
AI is taking the world by storm: large enterprise solutions and providers are looking to disrupt entire industries, developers are releasing tools built upon chatGPT, and suddenly consultants position themselves as AI specialists, and frankly, good for them: they see and seize the opportunity!
What will be the impact on b2b sales?
Big.
It can be tempting to jump in and see how to use it in sales, specifically in b2b sales.
Nevertheless, I think it is important to use some basic principles to stick to in order to implement AI successfully.
And those are:
Let's go:
1. AI is not intelligence, just like vegetarian meat is no meat.
It is a technology that can be used to augment human capabilities. It will definitely replace sales activities, and it will replace people whose job is tied to activities.
Just like robotics did with millions of factory workers, but at the same time improved the productivity of higher skilled factory workers.
AI can automate repetitive tasks, analyse vast amounts of data, and make predictions, but it does not possess the same level of critical thinking, creativity, and empathy as human beings….
2. AI will not replace sales people, but it will polarise b2b sales.
The trend to automate low value, high transaction sales requiring low friction will accelerate.
This means a direct challenge for sales teams working on the SMB segment.
At the same time, sales is an emotional journey, and this is where the human factor will play a crucial role.
3. Understand the context of your AI use case
As with everything. Context is key.
In order to help you, I created this matrix designed around 2 axis:
Looking at the matrix then we get to following cells:
How does this look like?
For example, while in transactional sales speed and zero friction is crucial, these can be high level requirements to implement AI. At the same time, complex sales is by it nature slower and as such enriching will be far more critical.
And here to illustrate you have it in a couple of use cases from each cell:
Low Speed, Enriching
Use Case: Hyper verticalisation
Definitely in sales operations or sales strategy, AI-driven data analysis can identify patterns and trends in customer behaviour, enabling salespeople to tailor their approach to specific customer segments.
This approach enables highly verticalised approaches, building stronger customer relationships and increases the likelihood of closing deals.
Context: Sales teams can leverage AI tools to analyse vast amounts of customer data, including purchase history, social media interactions, and website behaviour. By identifying key insights, salespeople can personalize their messaging, product recommendations, and marketing strategies to resonate with each customer more effectively.
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High Speed, Enriching
Use Case: Quick customer resolution
AI-powered chatbots can provide initial customer support, answering frequently asked questions and resolving common issues quickly. The value is here to recreate conversations with digital twins, more interactive and realistic than the current generation of chatbots.
Context: Businesses can deploy chatbots to handle routine and more complex customer queries, such as checking order status, providing product information, or resolving technical issues. This streamlined approach ensures prompt customer support.
Low Speed, Automating
Use Case: Lead scoring
AI-powered lead scoring systems can automatically evaluate leads based on various criteria, such as their company size, website traffic, and social media engagement. This categorization helps prioritize leads and focus sales efforts on the most promising opportunities.
Context: Businesses can implement AI-driven lead scoring solutions to streamline their sales qualification process. This automation reduces the manual effort involved in evaluating leads, allowing sales teams to focus on engaging with the most qualified prospects.
High Speed, Automating
Use Case: Sales onboarding
AI-enabled scheduling tools will enable automatically booked meetings and appointments with new clients, saving salespeople time and reducing the risk of scheduling conflicts. In addition, onboarding documents, access to content, manuals and enablement videos will happen automatically.
Context: Client experience is crucial the first weeks after sign up. Clients want to maintain the same attention and brand experience they had during the sales process, and you want to have them start using your solution as soon as possible.
4. The texture and flavour of AI
One AI is not the other.
The effectiveness of implementing AI will be impacted by the type of AI you implement.
AI is not AI.
I was talking earlier this year with a client who insisted if our technologies use ‘AI’.
AI is an umbrella for various technologies including machine learning, natural language processing and its subset LLM, computer vision, … For many use cases, you 'don't need' AI, you need automation.
In addition, many startups that claim to offer AI, either only offer a presenation layer (lets call it an app) with some prompts on top of openAI, or they hard code prompts they have adjust for every individual client (which will create a huge mess when -if- they add more clients and want to get version control).
On the content creation side, Neil Patel, one of the best marketeers and SEO experts in the world, confirmed that context created with the help of Bard perform better than ChatGPT, in the sense that they resonated better to human readers (based on sharing the generated content to 200 readers).
5. Caution: Unintended consequences
I am adding one more principle here: are you taking into account potentially unintended consequences of implementing AI, like in creating artifacts or AI hallucinations that can have dramatically negative consequences.
The intelligence lies in the ‘training’ it gets, in the information and dataset it receives.. and more.
I remember a story where a civilisation got wiped out by AI.
The reason? One of the AI clusters got tasked by calculating PI.
The AI diligently executed its mission, directing all the resources of the civilisation to calculate PI.
Not that I think the unintended consequences of your implementation would be as far reaching as in the story, at the same time, it is good to do a thorough risk analysis.
What is your PI?
And this was it for the week. What AI solutions are you working on in your org? Share in the comments, or send me a DM.
Do you have questions? feel free to reach out to me to: [email protected]
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