Services for Organizations

Using our research, best practices and expertise, we help you understand how to optimize your business processes using applications, information and technology. We provide advisory, education, and assessment services to rapidly identify and prioritize areas for improvement and perform vendor selection

Consulting & Strategy Sessions

Ventana On Demand

    Services for Investment Firms

    We provide guidance using our market research and expertise to significantly improve your marketing, sales and product efforts. We offer a portfolio of advisory, research, thought leadership and digital education services to help optimize market strategy, planning and execution.

    Consulting & Strategy Sessions

    Ventana On Demand

      Services for Technology Vendors

      We provide guidance using our market research and expertise to significantly improve your marketing, sales and product efforts. We offer a portfolio of advisory, research, thought leadership and digital education services to help optimize market strategy, planning and execution.

      Analyst Relations

      Demand Generation

      Product Marketing

      Market Coverage

      Request a Briefing

        David Menninger's Analyst Perspectives

        << Back to Blog Index

        Augmented Intelligence Reduces Dependency on AI/ML Skill Sets

        Business intelligence has evolved. It now includes a spectrum of analytics, one of the most promising of which has been described as augmented intelligence. Some organizations have used the term to describe the practical reality that artificial intelligence with machine learning is not replacing human intelligence, but augmenting it. The term also represents the application of AI/ML to make business intelligence and analytics tools more powerful and easier to use. It’s this latter usage that I prefer and I’d like to explore in this perspective.

        Ventana_Research_Benchmark_Research_Analytics_39_AI_ML_Skills_20220627As I’ve written previously, analytics are not deployed as widely as they could be. Sure, analysts can do amazing things with the tools that are available today, but not all line-of-business workers are analysts or have deep analytical skills. In addition, many analysts will not have the skills needed to be proficient in applying AI/ML. Our Analytics and Data Benchmark Research shows AI/ML skills are the least common skills in an organization, and the ones most needed to successfully use data. Only 23% of organizations have the skill sets they need, while 65% claim they need more skills. Augmented intelligence can help by reducing the skills needed to benefit from AI/ML.

        I see three main types of augmented intelligence:

        • Automated AI/ML analyses.
        • Natural language processing.
        • Guided or assisted features within data and analytics products.

        Automated AI/ML analyses are becoming more common among analytics vendors. Many AI/ML vendors tout these capabilities, and some have built businesses around automated or assisted AI/ML model development. While these capabilities are valuable, I remain a skeptic as to whether an organization could completely automate the development and deployment of models without any further oversight or governance by highly trained AI/ML specialists. We’re getting closer, but I would still want a review of any models developed automatically before they were deployed into production.

        However, automated “insights” can be quite valuable. BI vendors are now running automated AI/ML analyses to help guide individuals as they examine the data in their organizations. No special expertise is required. The analyses offer an assessment of which metrics may be most significant or which factors are causing the largest changes in performance. In addition to performing AI/ML-based analyses that many individuals wouldn’t have the skills to perform, these automated analyses also introduce some consistency across the organization. The same type of analyses will be conducted and available to all.

        Natural language processing also employs AI/ML on behalf of individuals using the software. AI/ML is used to interpret queries and translate them into SQL or other computer languages as necessary to retrieve the information for which an individual is searching. Similarly, AI/ML is used as part of the natural language generation process to produce sentences or paragraphs of text describing what was found in the data. Many people do not know how to interpret a table of numbers or a chart of data. NLG helps draw their attention to critical pieces of information driving the organization’s performance. Again, this technique introduces more consistency in the organization, rather than leaving to chance how a set of data might be interpreted. Organizations using NLP are more likely to report having more than half the workforce using analytics (31%) compared to those relying only on reports, dashboard and visualizations (18%).

        Finally, AI/ML can be used to make data and analytics software products easier to use. Many of the automated insights referenced above produce visualizations as part of the analyses. Those visualizations can form the basis of a new dashboard. AI/ML can make suggestions on what data to analyze. For instance, an individual’s department or location might be used to suggest the filtering to be applied to data. More sophisticated assistance might include considering what others in a similar role are analyzing. Many analyses require data to be joined together, and AI/ML can assist with that process as well.

        VR_2022_AI&ML_Assertion_1_SquareBI technologies have advanced significantly, but there are still not enough people using analytics. Through 2024, artificial intelligence and machine learning applications will remain largely independent of business intelligence solutions, requiring three-quarters of organizations to maintain multiple, separate skill sets. Yet both types of analyses are valuable to organizations. As you evaluate analytics and BI vendors, or as you work with your existing vendor, make sure you understand the augmented intelligence capabilities offered. Stay informed about investments the vendor is making to develop and deliver more augmented intelligence. Using those AI/ML capabilities will help you get more out of your analytics processes and improve your organization’s overall performance. 


        David Menninger


        David Menninger
        Executive Director, Technology Research

        David Menninger leads technology software research and advisory for Ventana Research, now part of ISG. Building on over three decades of enterprise software leadership experience, he guides the team responsible for a wide range of technology-focused data and analytics topics, including AI for IT and AI-infused software.


        Our Analyst Perspective Policy

        • Ventana Research’s Analyst Perspectives are fact-based analysis and guidance on business, industry and technology vendor trends. Each Analyst Perspective presents the view of the analyst who is an established subject matter expert on new developments, business and technology trends, findings from our research, or best practice insights.

          Each is prepared and reviewed in accordance with Ventana Research’s strict standards for accuracy and objectivity and reviewed to ensure it delivers reliable and actionable insights. It is reviewed and edited by research management and is approved by the Chief Research Officer; no individual or organization outside of Ventana Research reviews any Analyst Perspective before it is published. If you have any issue with an Analyst Perspective, please email them to

        View Policy

        Subscribe to Email Updates

        Posts by Month

        see all

        Posts by Topic

        see all

        Analyst Perspectives Archive

        See All