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        About David Menninger

        Executive Director, Technology Research

        David manages the software research and advisory for IT and leads the expertise in AI. Dave oversees the team and expertise areas for software used by IT and for what is used by business. Dave leads the software research efforts in AI for IT and AI infused software building on over three decades of experience in data and analytics. For decades, David has brought to market leading-edge analytics and data products where he has held marketing and product leadership positions at Pivotal a division of EMC, Vertica Systems, Oracle, Applix, InforSense, and IRI Software. David earned his MS in Business from Bentley University and a BS in Economics from the University of Pennsylvania.

        David Menninger's Presentations

        David Menninger's Market Assertions

        Through 2026, conversational experiences involving analytics will improve enterprises’ use of data, but for three-quarters these experiences will remain primarily text-based rather than voice-based.
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        By 2026, more than one-half of enterprises will adopt natural language processing as a component of improving digital experiences for business professionals who desire conversational methods of interaction. 
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        By 2026, one-half of conversational analytics will incorporate multilingual capabilities to support a larger portion of enterprises’ employees. 
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        By 2026, more than one-half of enterprises will use search and natural language generation to increase the reach and consistency of their analytics.
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        By 2028, natural language processing will replace search as the preferred way to access information in the majority of enterprises.
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        By 2027, 2 in 5 natural language processing applications will incorporate multilingual capabilities based on GenAI.
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        Through 2026, AI and machine learning approaches will remain largely independent of business intelligence approaches, requiring three-quarters of enterprises to maintain multiple, separate skill sets.
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        Through 2026, model governance will remain a significant concern for more than one-half of enterprises, limiting the deployment, and therefore the realized value, of AI and machine learning models. 
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        Through 2026, almost all software providers will expand the reach of analytics with further investment in both GenAI and machine learning capabilities. 
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        Through 2026, more than one-half of enterprises will realize their AI competencies and skills are insufficient and will require new investments to avoid being at a competitive disadvantage.
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        Through 2026, one-half of enterprises will unify their AI and ML teams with data and analytics professionals to ensure centralization of the disciplines and achieve the desired organizational impact. 
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        Through 2026, one-third of enterprises will realize that a lack of AI and ML governance has resulted in biased and ethically questionable decisions. 
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        By 2026, 9 in 10 AI and ML tools will include bias detection and mitigation capabilities, providing better assurance and governance to enterprises using these products. 
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        By 2026, 9 in 10 AI platforms will incorporate LLM and LLMOps capabilities to support GenAI.
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        Through 2026, almost all enterprises developing applications based on GenAI will use vector search and Retrieval Augmented Generation to complement foundation models with proprietary data and content.
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        By 2026, one-half of enterprises will use multiple LLMs to fine tune results of their GenAI efforts.
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        By 2026, more than one-half of enterprises will use governance tools to ensure their GenAI efforts are in compliance with regulations and internal policies.
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        Through 2026, GenAI will require specialized skills that are in short supply, making it difficult for one-half of enterprises to hire and retain the necessary staff.
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        By 2026, three-quarters of GenAI tools will support the use of multiple LLMs.
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        Through 2026, 1 in 5 enterprises will adopt computer vision applications to monitor and improve their operations.
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        Through 2026, advances in AI processing will result in 1 in 10 computer vision applications running at the edge rather than in the cloud.
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        Through 2027, the majority of enterprises will lack the skills needed to adopt and deploy computer vision in operational systems.
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        Through 2027, software providers will be challenged to provide explainability behind computer vision, limiting adoption in regulated industries.
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        Through 2027, deep learning models will be developed primarily in the cloud due to the large amount of computing resources required.
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        Through 2026, deep learning algorithms will continue to drive advances in natural language processing and image recognition, enabling new use cases.
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        Through 2026, detecting and eliminating bias from deep learning training data sets will be a challenge for the majority of enterprises.
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        By 2026, one-quarter of enterprises will have deep learning applications in production.
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        By 2027, more than three-quarters of enterprises using AI and machine learning will have adopted MLOps to improve success with the operationalization of machine learning models.
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        By 2026, nearly all MLOps software providers will expand their offerings to include LLMOps.
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        By 2026, LLMOps will be as common as MLOps to put LLMs into production effectively and with proper governance.
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        By 2026, 4 in 5 enterprises will use MLOps and LLMOps tools to improve the quality and governance of their AI/ML efforts.
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        By 2026, the majority of enterprises will prefer AI/ML platforms tools that support multiple LLMs.
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        Through 2026, three-quarters of enterprises will use third-party LLMs rather than developing and maintaining their own.
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        By 2027, one-half of enterprises will combine data science and data operations teams to improve the efficiency and governance of data preparation pipelines.
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        By 2027, one-third of model building platforms will use GenAI to assist in training and tuning predictive models.
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        By 2027, the majority of AI/ML software providers will incorporate feature store capabilities to increase reusability and consistency in data science pipelines.
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        By 2027, AI chatbots will be the default mode of starting service interactions, making it easier for CX teams to derive value from analytics. 
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        By 2026, 6 in 10 enterprises will include guided analytics processes and storytelling capabilities to help create and deliver insights for customer experience teams across departments. 
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        David Menninger's Perspectives

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