The sales process involves sales functions converting leads or opportunities to customers and selling more products to existing customers. The optimization of the sales process thus is key to success of any B2B business. In this work, we introduce a principled approach to sales optimization and business AI, namely the Causal Predictive Optimization and Generation, which includes three layers: 1) prediction layer with causal ML 2) optimization layer with constraint optimization and contextual bandit 3) serving layer with Generative AI and feedback-loop for system enhancement. We detail the implementation and deployment of the system in LinkedIn, showcasing significant wins over legacy systems and sharing learning and insight broadly applicable to this field.

If you thought that Artificial Intelligence was only used for playing chess or analyzing data, think again. AI is quickly becoming a staple in sales teams across the globe as companies attempt to increase efficiency and close more deals. This blog post will explore a few ways companies use AI for more efficient sales. From lead generation to customer segmentation, AI is changing the sales landscape as we know it. So if you're curious about how AI can help your sales team, read on! Sales intelligence is the term given to the data and information gathered about potential customers during the sales process.

Predictive machine learning models often lack interpretability, resulting in low trust from model end users despite having high predictive performance. While many model interpretation approaches return top important features to help interpret model predictions, these top features may not be well-organized or intuitive to end users, which limits model adoption rates. In this paper, we propose Intellige, a user-facing model explainer that creates user-digestible interpretations and insights reflecting the rationale behind model predictions. Intellige builds an end-to-end pipeline from machine learning platforms to end user platforms, and provides users with an interface for implementing model interpretation approaches and for customizing narrative insights. Intellige is a platform consisting of four components: Model Importer, Model Interpreter, Narrative Generator, and Narrative Exporter. We describe these components, and then demonstrate the effectiveness of Intellige through use cases at LinkedIn. Quantitative performance analyses indicate that Intellige's narrative insights lead to lifts in adoption rates of predictive model recommendations, as well as to increases in downstream key metrics such as revenue when compared to previous approaches, while qualitative analyses indicate positive feedback from end users.

AI is taking over industries. If you are running a large-scale business in 2021, it must be having some degree of automation or AI as a functionality. AI's role in your business can range from the usage of chatbots to helping you close a sale. Given how the disruptive technology is advancing, you may also be used to optimize sales and cut down costs. Whatever your strategy to use AI is, it is important to keep yourself aware that AI is like a puppet and it is only as good as its puppeteer.

More than thirty years ago, Fred Davis developed the Technology Acceptance Model (TAM) as part of his dissertation at MIT. It's one of the most widely cited papers in the field of technology acceptance (a.k.a. Since 1989, it's spawned an entire field of research that extends and adds to it. What does TAM convey and how might today's AI benefit from it? TAM is an intuitive framework.

The COVID-19 pandemic has increased the focus on the use of artificial intelligence (AI) across the life sciences organization, from R&D to manufacturing, supply chain, and commercial functions. During the pandemic, company leadership and management realized that they could run many aspects of their business remotely and with digital solutions. This experience has transformed mindsets; leaders are more likely to lean into a future that lies in digital investments, data, and AI because of this experience. At present, the life sciences industry has only begun to scratch the surface of AI's potential, primarily applying it to automate existing processes. By melding AI with rigorous medical and scientific knowledge, companies can do even more to leverage this technology to transform processes and achieve a competitive edge. AI has the potential to identify and validate genetic targets for drug development, design novel compounds, expedite drug development, make supply chains smarter and more responsive, and help launch and market products. We will highlight a number of these use cases in this report.

With a million things to do, meetings can sometimes seem like an intrusion on your workday. You want to get as much done as possible. Meetings are often inefficient, redundant, and...well...boring. Whether your team is three people in a co-working space or thousands of people spread across the globe, it's important to get everyone on the same page. Meetings help ensure that work doesn't get duplicated or fall through the cracks. Getting the team together is important. You need to make sure everyone understands your organization's goals and challenges.

Tech savvy sales and marketing folks are always looking for an edge over their competition. The past few years have been filled with lots of IT promises whereby AI, machine learning and predictive data analytics will replace the salesperson as a fate de complete. With this in mind will the venerated salesperson ever be replaced by the multi-functional, AI, machine learning toaster? I think not, or at least not in the near future! Think about all the time we waste with companies automated, fun and responsive customer service chatbot "robot" sales rep! Companies claim that these automated systems are efficient and save customers time – but we all know the only thing they save is the company's money.

News headlines everywhere scream fears of robots taking over jobs, creating an ever-growing pool of unemployable humans who cannot compete with machines. This concern, while understandable, is unfounded. In truth, Artificial Intelligence (AI) will arguably be the greatest job engine the world has ever seen. Both automation and AI underscore how central and critical human insight and expertise are to business success. And nowhere is this more evident than when we look at the impact of AI in the sales process.

Most organizations have begun to invest in AI to guide their sales representatives as it helps organizations stay adaptable to changing customer needs and evolving markets. AI guided selling usually takes the form of machine learning generated advice offered to reps on their CRM or other software. It's primarily designed to help salespeople stay organized, prioritize leads, choose the customer most likely to buy for their next sales call, and so on. When its impact is fully realized, it gives salespeople more time to sell and information that they leverage to sell more effectively. I've seen solutions like this work, even quite well.