“AI could contribute up to $15.7 trillion to the global economy by 2030, more than the current output of China and India combined,” according to PwC. The same report estimates that in 2018 alone, AI contributed $2 trillion to the global GDP. Despite the enormous rewards of implementing AI solutions, becoming an AI-driven organization is still a challenge. 

In our ebook, 10 Keys to AI Success in 2021, we look at how to meet these challenges. Drawing from our More Intelligent Tomorrow podcast series, we hear from thought leaders in the fields of AI, finance, mathematics, and the military who offer their insights about AI and the opportunities ahead. We have collected highlights of our interviews on topics that range from trusted AI to democratization of AI to whether you should build or buy an AI platform. The ebook looks at how these challenges will impact companies that are implementing AI in 2021 and in the decade to come. 

Here we take a look at three of the ten topics featured in the ebook: 

• AI with ROI: Delivering results with value and urgency
• Democratization of AI across your organization
• Deciding whether or not to build or buy an AI solution 

AI with ROI: Delivering Results with Value and Urgency

The days of experimenting with AI without a plan for obtaining value are over. While it has been fun for data scientists to test what machine learning can do, companies that invest huge resources into their AI solutions want to see results. AI must find its purpose, and it must deliver ROI.

As our guests point out, you can come up with great solutions, but if they don’t fix your problems, then what’s the point? It’s incredibly important to build a bridge between what your data scientists are working on and the business outcome. By making that connection, you initiate a value-first discussion that connects your work to the investment of resources that you are making. 

In addition, you need to find the right problems to solve. This starts by beginning with the end in mind. If you can’t find the right problem, then start over and find something that truly brings value to your organization. 

Think of AI as instituting a fundamental change in your organization. Find ways to optimize, automate, and deliver value for the business now and deliver AI success as quickly as possible. 

Democratization of AI Across Your Organization

In business environments, there are an average of 30x more business analysts than there are data scientists. When you engage those business analysts in your AI effort, you can create citizen data scientists and train them to identify ways that AI might help solve the business problems they grapple with everyday. Capitalizing on AI means having a coherent AI strategy that you can operationalize within your existing processes and with your existing teams. 

Our guests note that democratization starts with building a solid educational program, one that demystifies AI so that everyone understands what it is about. This can help your workforce to get inspired to use AI in their individual practices and find new problems to solve. 

From there, someone has to be in charge — a sponsor who knows about the advancements in AI and also has a voice in the boardroom to advocate for those advancements. Support from leadership is the essential ingredient. A leader who endorses what you’re doing and who gives you the time and resources to do it is invaluable to any AI-driven organization. 

When building your AI initiative, be sure to put thought into the composition of your end-to-end AI team. It runs much deeper than just hiring a bunch of people who understand machine learning. You need someone who understands the problems you are trying to solve. 

Build or Buy an AI Solution?

When it comes to deciding whether to buy or build an AI platform, our guests have several recommendations. No matter what, they all agree: don’t reinvent the wheel. That’s a waste of time. Instead, your organization should focus on building what it can and buying the rest. 

You should start with an assessment of your whole organization and find opportunities for repeatability. Learn what other groups are doing and whether you can build on that knowledge or need to fill in with automation. There is so much to learn by going through this process and pulling knowledge from other areas in your organization. 

Our guests note that rarely is a vendor product usable off the shelf. Usually, you need to develop the algorithm jointly and use that algorithm in a way that actually delivers business value. Keep in mind that buying is the preferred option when you need to scale, outpace competitors, and free up internal human resources.

Ten Keys to AI Success

For a closer look at the rest of the ten keys to AI success, read our ebook where we cover seven other topics, including: 

• Transparent storytelling
• Governance
• Trusted AI
• Hyperscaling your AI
• Working with different types of data, such as data in visual or geospatial formats
• MLOps
• Predictions for what’s next in AI and technology