AZ: There is a lack of practical information on the market on how you — a business executive — can implement artificial intelligence and machine learning to achieve ROI positive results. We distilled our learnings working with numerous clients into this handbook. We share frameworks, best practices and pitfalls to avoid. Our goal is to empower businesses to use machine learning to increase revenue, decrease costs, drive productivity and build successful enterprises.
MY: We cut the noise. There’s a lot of junk out there about artificial intelligence. Our book is focused on being a down- to-earth, practical how-to guide on applying modern machine learning techniques to your organization to solve real problems.
MY: AI is a huge umbrella term, much abused in the media. That’s why we spend two chapters to clarify what is artificial intelligence. On a high-level, AI refers to a set of techniques within computer science that are used to either replicate or even exceed human decision-making and human perception. These can include techniques like data science, data mining, symbolic and expert systems, machine learning, deep learning, evolutionary strategies, and the list goes on. We dedicate another chapter to a friendly non-technical introduction so that a business executive can understand how these technologies differ and what they’re good for.
MY: There are two layers that need to happen if you want to transform into a fully AI-ready organization. The first layer is leadership. You need to educate your executives, bring together budgets and gain political buy-ins from stakeholders. The stakeholders can be anyone from your frontline employees to middle managers who may be resistant to the idea of AI “taking over their jobs.” We spend time in the book discussing, from a strategic level, how to prepare your organization from a leadership standpoint. The second layer is the technical layer. We find that most traditional non-technology companies often lack the requisite proprietary data to train useful computer models. Preparing and collecting this data can be a months-long, years-long procedure. You should get started now. Our book helps you figure out what are the right steps to get started.
AZ: One of the most common areas of application of AI is in customer service. I’m sure everyone has had a frustrating time when they called customer service and got stuck in an endless loop. With artificial intelligence, you’re able to now create virtual agents that are better suited to answering questions in natural language. Virtual agents can alleviate cost center pressures, especially during spikes in contact volume. This can help you achieve higher customer satisfaction and better customer service results.
MY: There are AI applications for every enterprise function as long as you have the data and the technology infrastructure. Applications fall into three different categories: The first is automation. Low-level that a human can do in one second can be automated in the near term. The second is augmentation. For example, some AI systems that can give very accurate medical diagnoses and support doctors in their decision-making capabilities for disease identification and treatment planning. The third is completely new functionality. We made a couple of major breakthroughs in AI in the last few years. We can train machine learning systems that can identify images and classify objects and images to about human parity. We can do the same for speech recognition and also text-to-speech. That enables a whole frontier of new functions that we weren’t able to do before.
MY: Absolutely. One of the reasons why technology companies have been at the forefront of AI is data tracking. When we use online platforms such as Facebook, they record every action you take. Whereas, if you are selling consumer packaged goods, you are not putting sensors in shampoo. You don’t know what the user is doing at that granular level. Companies naturally collect a lot of data then feed this data into unique machine learning models. However, less tech savvy companies are even missing the data capture and the data collection procedures. Those types of companies tend to be further behind.
AZ: It’s not too late for them to start. If you are aware of your data problems now, then you can start the processes to collect, process, and store the data today. Data is the new oil.
MY: The biggest misconception is that we’re really close to superhuman AI and so people are often afraid, “Am I going to lose my job if we adopt this AI system?” Don’t worry about it. Systems that we can use right now can improve your predictive abilities, but today we simply don’t have artificial intelligence that’s at the human level that can actually replace the kind of strategic and creative thinking that you need an executive or an expert for.
AZ: Many times, we find that when businesses begin to use machine learning, they don’t replace their workers. Instead, they realign workers to higher level strategic types of work. For example, rather than have a contact center representative reset passwords all day, the agent can now help customers with banking finance or consultative advice which is higher value add to the business.
MY: If you ask experts any questions about AI predictions, the guesses will be all over the place. What I will say is if you don’t start adopting, if you don’t already have a big data strategy, if you don’t already have centralized data infra- structure, you’re already behind the curve. In five to 10 years, companies that have not made a sufficient technical transformation at the enterprise level to start adopting machine intelligence and more modern techniques are going to be behind.
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