While most of the attention for how artificial intelligence (AI), machine learning and big data can impact companies is focused on the business to consumer (B2C) space, business to business (B2B) companies need to pay attention or they risk their future success. Customers have the same expectations for a simple and easy buying experience whether it’s a B2C or B2B interaction. So, if you’re in the B2B space, I hope your organisation is beginning to explore and plan for, if not already implemented, big data and machine learning to your operations.

Since the buying cycle for B2B is usually significantly longer and more complex than B2C (Gartner found there is an average of 5.4 people on corporate buying teams), I would argue that it’s even more important for B2Bs to use machines in any way possible to quickly get to know customers and respond to decision-makers with the objective of closing sales as efficiently as possible.

According to Salesforce’s 2016 Connected Customer report, by 2020, 57% of business buyers will depend on companies to anticipate their needs and if they don’t, business buyers will have no problem switching brands. If that isn’t motivation to get things started, I don’t know what it is.

Here are some of the biggest ways artificial intelligence, machine learning and big data are facilitating operations for B2B companies.

Lead generation

It can take hundreds of hours of manpower to source new leads for B2B companies and to find the contact info you need from searching LinkedIn, company websites and more. Not only can machines help with the original gathering of information of lead generation, AI can analyse unstructured data such as emails, phone calls and social posts to then determine patterns and define who is a good prospect. This info is vital for effective marketing campaigns.

Predictive account management and sales

There’s no doubt about the fact that machines are powerful at analysing data to glean insights especially to inform the sales and marketing efforts. This power is put to work to figure out commonalities between customers and figure out what separates your best customers from the less desirable and can help focus the marketing efforts to be more effective within these categories of customers. When this learning is applied to your company’s prospecting efforts, it can definitely streamline lead scoring and can help you prioritise where you should focus the majority of your sales efforts. While this type of account-based marketing decision can and will still be made by humans, the machines facilitate the process and can identify patterns that would take much longer to identify using human effort alone.

Predictive sales efforts are already in place and we’re hit with it every day. Just check out your social media feeds to see what ads you’re being served. B2B businesses can offer their customers suggestions and ideas about what services or products would complement their business in much the same way.

Monitor customer behaviour and act based on the behaviour

Most B2B companies are actively monitoring web analytics, but that’s just the very minimum a company needs to do today. With the power of machine learning, the more effectively you use big data to learn about your customers and respond with what they need when they need it, the more successful you will be. Machines can and already are being programmed to respond immediately to the input from customers. Machines are better able to efficiently consolidate data points and analyse the data for meaning; something that would take humans exponentially more time. This has wide applications from improving content marketing to customer service to upsell opportunities.

Free up humans to do what they do best

There’s always a common theme when we talk about AI and machine learning in business no matter the industry. The more workload we can shift to machines—the mundane, repetitive and analytical tasks that machines are better at than most of us—the more humans are freed up to be more creative and effective in their tasks. So, while machines can identify patterns and process data efficiently to classify customers, recognise and alert humans that customers need something and more, humans can apply the learning we get from the machines to be more effective at our responsibilities.

There’s never been a better time for B2B companies to adopt AI, because they will be ahead of the competition and realise results right away. There are significant benefits that are capable today, and those benefits should be the focus when trying to build a business case for the technology and accompanying expense of AI.