Getting Started with AI: Four Things to Consider

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So you’re thinking that 2021 is the year to infuse Artificial Intelligence/Machine Learning (AI/ML) into your business. You’ve read about the difference it’s making in other organizations. You want to beat — or keep pace with — your competitors. But where should you begin? 

Should you license AI/ML software? How do you find the right business problem to solve? And if you’re like most organizations, your data is imperfect. Should you focus there first? 

Ironside can help. We’re a data and analytics consulting firm with a track record of helping companies get started with AI.

Let’s start with four things we think every organization should consider on their AI journey. They’re not the only four things you need to know, but we know your time is valuable, so let’s start here:

  1. Develop an AI Use Case Catalog – One of the first places you’ll start is to develop a list  of possible business problems, opportunities or challenges that AI might improve. We assist our customers in building that list by talking to executives and functional area leaders and understanding the organization’s strategic goals, how they are measured and then considering challenges/pain and what information is missing that would improve decision making. The catalog of use cases should be enhanced with information from a thorough data analysis. Is there the data to support the use case? Is there enough of it? What’s the data quality? Can you forecast improvement of an important metric? What’s the return on investment? 
  2. Involve a variety of stakeholders. Executive sponsorship in some form is critical in funding and executing an AI project. But building a culture of AI across an organization starts by involving as many stakeholders as possible across functional areas. Even if the use cases surfaced by some stakeholders are not immediately pursued, people want to be included and to have a voice. Broader involvement will avoid roadblocks and seed a culture of AI. Organizational success will grow over time. 
  3. Start small – Rank the use case catalog and find one or two to test. Identify the relevant business sponsor and data and prepare a limited set of data, or features. Build a simple machine learning algorithm to see if there are results that show that AI could improve a desired outcome. If not, move on to the next use case in the catalog (see, that’s why we need a catalog). If the early results are promising, move on to building a more advanced machine learning model.  
  4. Limit your investment – For as low a cost as possible, get a model deployed and start using it in the business to begin to get the benefit. You’ll inevitably iterate on that model but expediting that process and limiting the investment — and the risk — is the goal. Now here’s where we answer the questions about hiring a data scientist or buying software. 

Sometimes the answer is yes but for many organizations the answer is “no.” They’re just not sophisticated enough. And big costly failures could sour your organization on pursuing AI and set you back years

Ascend AI 

So what should you do? One option to get started is Ascend AI, a data science as-a-service solution that Ironside developed. Ascend AI lowers the risk out of diving into AI on your own. It is  underpinned by a custom configured and scripted cloud-based architecture as well as our highly skilled data scientists. 

We bring the data scientists and engineers and the technology. You provide the data and the business problems. 

We start with your leading use cases or help you develop them in a use case catalog. Then we perform rapid viability assessments on the leading use cases selected and if signs are good we would then build out full machine learning algorithms. Finally, we could deploy and host and manage the algorithms. At any point, depending on customer preference and maturity, we would hand the IP back to our customers and help them develop AI competency in house. We’re not a black box. 

Of course there’s more to getting started with AI than these four points and data science as a service might not always be the answer. The thing to remember is that AI should be consumed in bite sized-chunks and is attainable to even the most technologically immature organizations.