Artificial intelligence (AI) is revolutionizing everything from customer service in banking to data privacy compliance to elevator maintenance. That’s why businesses and public sector organizations around the world have AI programs on their IT agenda. Yet in spite of the broad interest, only 20 percent of companies have actually implemented any sort of functioning AI in their business.

That’s not surprising, considering that the field of AI is relatively new and the process of developing and implementing an AI solution can be complicated. Therefore, in today’s blog post, I’d like to share some strategies for getting started with AI and executing a project that will deliver solid, repeatable value to your organization.

Take the agile approach to AI

Just as the journey of a thousand miles starts with a single step, a successful AI program can begin with a single sprint. The collaborative agile methodology breaks down large projects into small, manageable increments or “sprints,” typically of two weeks’ duration. It encourages experimentation and the use of small projects and quick iterations to facilitate fast-paced problem solving. In an agile process, you examine your progress frequently and can change direction quickly if a chosen course of action isn’t delivering results. Work quickly, show value, and move on.

Identify the business value in your AI project

The place to begin, of course, is with determining your business objectives. Do you want to streamline a business process? Reduce customer churn? Anticipate the maintenance needs of machinery and equipment? Whatever it is, once the business goals have been clearly identified, you need to evaluate the available data to see if it contains the information necessary to address the business need. The data will undoubtedly need some cleaning and preparation before it can be used in your model.

Perform a basic cost-benefit analysis to ensure that the result will deliver enough business value to justify the effort and take time to properly scope out your project requirements before beginning to build the solution. This might be an iterative process in itself. The data science team might want to explore the data first, determine what outcomes are needed, and then develop a proof-of-concept to outline the proposed plan.

After you have a good sense of what kind of work the project will require, and the data available to support it, take an inventory of the skills on your team. It’s important to understand that culture is an extremely important factor to consider here. Curiosity, creativity and open minds are essential ingredients in successful AI projects. And soft skills, such as communicating the business value of the project, can be just as important as the data science.

Broadly speaking, an AI project needs two primary categories of skills:

  • Domain expertise: You need subject matter experts (SMEs) who understand the business needs of the project and nuances of it. For example, regulatory compliance in the health care field.
  • Data science skills: Data scientists create models and write the algorithms used to train machine learning models. But prior to that, the data scientists need to clean and prepare the data and be able to work with a variety of data frameworks. Once the model is created and deployed, the data scientists will need to refresh the models on a regular basis, monitor the accuracy of predictions, and fine-tune the models to deliver the desired results.

Because AI projects are inherently interdisciplinary, people from a variety of backgrounds can bring complementary skills and insights to the table. An AI project may present an attractive opportunity for internal people who want to “reskill” and move into the field. Such people will already be familiar with your business and they can bring valuable domain expertise to the team. For example, someone with a background in human resources (HR) may be familiar with issues of bias in the data and with protocols for handling confidential data, both of which are rapidly becoming top concerns in AI deployment.

Time for a deeper dive into AI

Integrating AI into your business happens one use case at a time. That’s where the small scale of the agile approach can be invaluable. A growing record of small successes can be a persuasive argument for continuing or expanding your AI program.

To learn more about the skills, processes and tools that can help you succeed in your AI project, watch my on-demand webinar, Agile AI for Business. You can also download our free e-book, Agile AI: A Practical Guide to Building AI Applications and Teams. The race for AI advantage has already begun. Make sure you know what you need to compete.

Accelerate your journey to AI.

More from Analytics

How data stores and governance impact your AI initiatives

6 min read - Organizations with a firm grasp on how, where, and when to use artificial intelligence (AI) can take advantage of any number of AI-based capabilities such as: Content generation Task automation Code creation Large-scale classification Summarization of dense and/or complex documents Information extraction IT security optimization Be it healthcare, hospitality, finance, or manufacturing, the beneficial use cases of AI are virtually limitless in every industry. But the implementation of AI is only one piece of the puzzle. The tasks behind efficient,…

IBM and ESPN use AI models built with watsonx to transform fantasy football data into insight

4 min read - If you play fantasy football, you are no stranger to data-driven decision-making. Every week during football season, an estimated 60 million Americans pore over player statistics, point projections and trade proposals, looking for those elusive insights to guide their roster decisions and lead them to victory. But numbers only tell half the story. For the past seven years, ESPN has worked closely with IBM to help tell the whole tale. And this year, ESPN Fantasy Football is using AI models…

Data science vs data analytics: Unpacking the differences

5 min read - Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructured data for various academic and business applications. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to…

Financial planning & budgeting: Navigating the Budgeting Paradox

5 min read - Budgeting, an essential pillar of financial planning for organizations, often presents a unique dilemma known as the “Budgeting Paradox.” Ideally, a budget should give the most accurate and timely idea of anticipated revenues and expenses. However, the traditional budgeting process, in its pursuit of precision and consensus, can take several months. By the time the budget is finalized and approved, it might already be outdated.In today's rapid pace of change and unpredictability, the conventional budgeting process is coming under scrutiny.It's…

IBM Newsletters

Get our newsletters and topic updates that deliver the latest thought leadership and insights on emerging trends.
Subscribe now More newsletters