January 11th, 2019
What Business Leaders Need to Know About AIRSS Share Category: Beginners, Community, Data Journalism, Data Science
By: Ingrid Burton
The interest around artificial intelligence (AI) is at an all-time fevered pitch right now, and it’s important to understand why.
AI can solve real business problems and address very complex situations. Organizations and business leaders should start with the idea of how AI can help by identifying a business problem or use case that they can address with the goal of creating better business outcomes. Essentially, creating an AI strategy that will lead to results and success.
Let’s walk through the basic foundational aspects of any strong AI strategy and understand the key challenges that they will need to address––talent, time and trust–– the best practices needed for AI success and what business and IT leaders should know in order to achieve an AI Transformation across their organization.
#1: Talent: Data is a Team Sport
Because AI can drive better business results, there are fundamental aspects of an organization that must be considered. Ultimately, the success of any AI strategy will hinge largely on the people and culture inside of each individual company.
First, we must face the current technical skills gap and lack of AI talent. Research from EY shows that 56 percent of tech professionals dealing with AI say a lack of talent is the biggest barrier to more AI adoption in businesses. In addition, IDC predicts that by 2020, 85 percent of new operation-based technical hires will be screened for analytical and AI skills. We’ve heard the stories about expert data scientists being in such high demand that they are quickly snapped up by tech companies and the internet giants, leaving most companies with a small talent pool to hire from. Quite simply, there are not enough AI experts in the workplace. We need more people to pursue data science and AI as a career choice, and can start solving for this with STEM programs at the elementary, high school and university levels. In addition, machine learning should be fast, accurate and available to everyone. At H2O.ai we believe that it is inevitable that by democratizing AI we can achieve a higher level of understanding and awareness around data and decision-making for all businesses. To achieve this, businesses should look for a solution that make machine learning and data science problems simple, even if an organization doesn’t have a dedicated data science expert on staff.
However, when a company has data scientists, they need to understand that data is a team sport. Getting people with different skill sets to work together productively, enabling teamwork across an organization and working well enough together to make the data work for them is crucial to building a successful data-driven business. Everyone from the functional business leader to devops professionals and analysts, to data engineers and data scientists are on the “data team.” Culturally, this team must be collaborative in order to be transformative. Usually while working within the existing culture of a company to bring change that is lasting.
#2: Time: Getting to Results Faster
Using data is a great way to make decisions. But how do you glean insights from data that enable more efficient and effective decisions? Business leaders are inundated with data from all areas of their organization and need to address a range of use cases that are primed for AI, including how attract the next new customer, make a credit-scoring decision, detect fraud or pinpoint the right treatment for a patient.
Essentially what many businesses are trying to do is extract real insights from data. To make the best possible decisions requires not just data, but also time. AI can help make the correct decisions, more easily, for less money and in less time. By building AI models, data science teams illustrate each scenario based on the data a company already has. Future data can then be used to re-train the model, allowing it to continuously improve, learn and correct. IT and business leaders should look for a solution that can help speed time to insights and time to better results.
#3: Trust: Explain the AI
Perhaps the biggest hurdle to AI success is trust. As organizations build a strong data and AI team, trust in the AI is one of the most critical ingredients to the successful incorporation of AI into a company’s culture and processes. For example, how are the people in an organization going to trust an algorithm more than the decades of existing human intuition and experience?
Part of overcoming this challenge is to provide more meaningful explanations of AI to people within the organization about what it is, how it will be used and how it will ultimately help people by enabling them to get more done, faster, in order to complete the larger, more creative projects and solve critical, complex problems. The goal here is to have AI running in the background all the time – it’s the permanent Plan B. Plan A is still to use your manual tool base, namely the humans who work at the company. There are new technologies on the market today that can address the explainability and interpretability of a model. Ask for that when considering a solution.
Influencing Change via a Maker Culture
From an organizational and cultural perspective, it’s important to remember that AI is not just a new technology. It is the catalyst for a chain reaction in the direction of change. With AI, organizations can instill a maker culture where learning is best done through doing. It can instill a product culture that continues its life cycle inside the businesses processes. As companies adopt an AI strategy, they become makers and can influence change. It’s fascinating and exciting to see all the development in AI over the last few years as companies adopt an AI strategy. It will only get more interesting and rewarding for every business as they as they move forward on their AI Transformation journey.