Blog Series: Taking the First Step with Artificial Intelligence & Machine Learning

Artificial Intelligence

Artificial Intelligence and Machine Learning (AI/ML) technology continue to be increasingly accessible with a lower barrier of entry to newcomers. In recent posts, Gartner identifies that one in ten enterprises now use ten or more Artificial Intelligence applications, with the top use cases being chatbots, process optimization, and fraud analysis. 1 Of the companies reporting Machine Learning usage, Algorithmia identified the following area benefits: reducing company costs, generating customer insights and intelligence, and improving customer experiences. 2

Have you been asked to lead an Artificial Intelligence and Machine Learning initiative to solve a business problem?

If so, you’re at the beginning of an exciting journey in leveraging transformational technology. To give you an idea of potential return, let’s looks at Highmark Inc.’s Financial Investigations and Provider Review Department. Highmark successfully applied AI to save over $260 million related to fraud, waste, and abuse in the year 2019 and continues to use AI to enhance fraud identification and prevention to catch potentially fraudulent activities today. 3

Because many have walked the path before you, successful approaches to incorporating AI and ML capabilities are readily available. This blog series will guide you through that process, while highlighting best practices and potential pitfalls. Our first article starts by walking through three key components that must be present for a successful AI/ML initiative.

The following items are considered as entrance criteria:

A specific business need is identified.

  • For the first AI/ML project, it is best to choose a well-defined bite-sized proof-of-concept (POC) along with a key metric to evaluate the level of success. A succinct problem statement and hypothesis on outcome(s) will align the team and set stakeholder expectations with each iteration of the POC. Success on a POC enables further endeavors of a more aggressive nature.
  • If a specific objective is not identified, the project has the potential to fail based on the growing or changing scope, or even worse, misaligned expectations.

Data exists to support the business objective.

  • A full data warehouse does not need to be present, but enough data of sufficient quality must be available to build the knowledge base needed for training the AI/ML capability. Be sure to consider any support data that may be needed to fully address the objective. If data gaps are identified, there may still be a chance to produce a meaningful result, but with a reduced scope of application. To reduce risk, the best first attempt for use cases have the required full dataset available within a single source of truth, or the data is available via existing pipelines.
  • Data volume is a primary requirement, closely followed by data quality. In a study that Researchscape International and Trifacta performed, one of the greatest challenges the respondents faced for AI implementation was data accuracy, with only 26% of respondents reporting that their data is completely accurate. Additionally, poor data quality negatively impacts AI/ML initiatives, with more than one-third reporting that projects took longer (38%), were more expensive (36%), or did not achieve anticipated results (33%). 4
  • If the current state of your data is not known, you may want to perform a data assessment before pursuing the AI/ML initiative further.

A culture of making data-driven decision making is present.

  • An AI/ML capability may have the potential to disrupt your business, but without an appetite to steer decisions based on the algorithm outcomes, the initiative will fall flat. If there is skepticism, show the estimated ROI and a plan to kick the tires with A/B testing. An adoption roadmap with milestones and corresponding results will ensure confidence that true diligence is being taken and result in reduced uncertainty.

If these criteria are met, the odds of a successful AI/ML implementation are tenfold.

Author
Maria Groszek Headshot
Maria Groszek
Manager, Strive Consulting LLC

 

 

Tune in next week!

Part two of this series will guide you through the steps of developing the AI/ML capability, from setting an objecting all the way through to managing and understanding your results. Make sure to check out Strive’s Insights page for additional postings and information.

Connect with Strive.

Here at Strive Consulting, our subject matter experts’ team up with you to understand your core business needs, while taking a deeper dive into your organization’s growth plan. Whether you’re interested in AI/ML implementation or a overall data assessment, Strive Consulting is dedicated to being your partner and committed to success.

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  1. Gartner 2019 CIO Survey: CIOs Have Awoken to the Importance of AI, January 3, 2019 
  2. Algorithmia 2020 state of enterprise machine learning
  3. Highmark Inc.’s Anti-Fraud Department
  4. Researchscape International and Trifacta Study: Obstacles to AI and Analytics Adoption in the Cloud, January 2020
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One response to “Blog Series: Taking the First Step with Artificial Intelligence & Machine Learning”

  1. […] Learning (AI/ML) capability for the first time. In case you missed our post last week, the first article covered three items required before beginning development on an AI/ML initiative. Moving forward, […]

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