Despite the clear benefits of adopting artificial intelligence (AI) and machine learning solutions, many organizations have not yet begun their AI journey. In fact, more than two-thirds (68%) of organizations have been using AI for less than two years, and the ones that haven’t run the risk of being left behind the competition.

Why the hesitation to adopt AI widely when the ROI is so great? A recent report from Ventana Research, Unlocking the Promise of Artificial Intelligence and Machine Learning, dives into the risks, rewards, and returns that organizations are finding when they invest in AI. It looks closely at how companies are using the technology to apply it to their most complex question — from forecasting threats to reducing customer churn to managing the supply chain, and much more. Companies are finding that the returns on their investments in AI are enormous and pay dividends for every level of the organization.

When the business environment and economic conditions are unsteady as they are now, AI and machine learning processes should change accordingly. It is crucial especially during uncertain and unstable periods that all the data-related insights are communicated across all the departments in a clear way, and that analyses are organically integrated into an organization’s analytics processes.

Integrating AI/ML into Analytic Workflows

One of the most important parts of AI and machine learning analyses is collecting and preparing data. Organizations report it to be the most challenging task since it is time-consuming, and it requires discipline, repeatable workflow, and high-quality standards. In addition to collecting data, AI and machine learning analyses must become a natural part of the workflow, be treated equally, and be controlled by the same rules as other components and techniques.

Companies prefer delivering AI and machine learning via the business intelligence (BI) and analytics tools they already use in their organizations. As a result, not only data scientists can benefit from the analyses and new approaches but also other lines of business. This results in overall success across other departments and business units. Subject matter experts can use it for analysis and forecasting in sales processes, marketing, maintenance control, and numerous other areas.

Transparency and Governance

While AI and machine learning is an easy-to-trust component for data scientists and citizen data scientists, it can be a tricky thing for line-of-business personnel to get on board. In order to break the ice between AI and newbies, companies need to establish trust by explaining key features of how algorithms work, demonstrating successful practices, and comparing predictions with actual results.

As soon as models are developed, they start decaying, which can erode trust in AI across your organization. In order to avoid this scenario, both the model and its output should be regularly reviewed and certified to avoid faulty results and keep up with the original target. Irrelevant data can influence not only the accuracy of predictive models but also various regulations. Data for a loan-granting procedure, for example, might be impacted heavily, since it requires personally identifiable information (PII). To follow regulatory and governance requirements, BI and analytics tools also can be used.

The Clear Case for AI and Machine Learning Value

It is hard to overstate the value that AI and machine learning brings to organizations. Increasing an understanding of AI and machine learning among larger groups of workers will help companies boost their profits, save time on critical projects, and maximize resources. Over 70% of participants in this report note that they plan to increase AI and machine learning usage throughout their organizations while still utilizing familiar tools.