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Don’t Drift Away from Reality — The Importance of AI Model Longevity

    Author’s

    George Harrison

    George Harrison

    Analyst and AI Consultant

    George is an Oxford alumni mathematician. His research is deeply rooted in an understanding of how AI works and how to apply it in real-world working environments. His specialist coverage is AI in CX.
    Simon Harrison

    Simon Harrison

    Analyst and Executive Partner

    Simon Harrison is an accomplished analyst and technology strategist with over 30 years of experience spanning systems engineering, technical consulting, product innovation, and global senior leadership. He began his career as a UNIX systems engineer and consultant before advancing to senior roles, including SVP of Product Marketing and award-winning Chief Marketing Officer, driving growth for a multibillion-dollar company. A former Gartner analyst and Magic Quadrant author, Simon remains an active industry analyst and executive advisor, helping companies sharpen their strategy, messaging, and go-to-market performance. Today, as founder of Actionary, he delivers board-level insight on AI, customer engagement, and platform innovation, drawing on deep technical roots and a proven track record of helping companies achieve their goals at scale.

    Summary

    In the ever-evolving landscape of machine learning (ML), winning the battle against model drift and decay is pivotal to maintaining a competitive edge and achieving sustainable success. Model drift and decay refer to the degradation in the performance of machine learning models that happens over time. This research delves into the critical importance of addressing these issues head-on and offers actionable insights to support verifying investment in the right Artificial Intelligence (AI) vendor solutions. Continuous monitoring, implementing automated retraining pipelines, and ensuring robust data management safeguard the integrity of AI systems and unlocks their full potential. Discover how proactive measures can ensure long-term value for your organization and what to be asking AI solution providers.

    Key Take: Effective management of model drift and decay is essential for sustaining the value of ML systems. Without these measures, models can become obsolete, leading to inaccurate predictions and reduced ROI.

    Read MoreDon’t Drift Away from Reality — The Importance of AI Model Longevity

    Get Ahead and Equip Your Team for AI Agency in Customer Service

      Author’s

      Jim Davies

      Jim Davies

      Analyst and Executive Partner

      With over 20 years of experience, Jim is a visionary analyst who has shaped markets and provided strategic advice to thousands of organizations. As the founder of groundbreaking frameworks such as the VoC and Workforce Engagement magic quadrants, and through his role as agenda manager for Gartner’s customer service research team, Jim has championed the elevation of customer experience and employee engagement. As an Executive Partner for Actionary, he continues his mission of driving impactful change in the industry.
      Simon Harrison

      Simon Harrison

      Analyst and Executive Partner

      Simon Harrison is an accomplished analyst and technology strategist with over 30 years of experience spanning systems engineering, technical consulting, product innovation, and global senior leadership. He began his career as a UNIX systems engineer and consultant before advancing to senior roles, including SVP of Product Marketing and award-winning Chief Marketing Officer, driving growth for a multibillion-dollar company. A former Gartner analyst and Magic Quadrant author, Simon remains an active industry analyst and executive advisor, helping companies sharpen their strategy, messaging, and go-to-market performance. Today, as founder of Actionary, he delivers board-level insight on AI, customer engagement, and platform innovation, drawing on deep technical roots and a proven track record of helping companies achieve their goals at scale.

      Summary

      Google, OpenAI and others have stated that they will imminently release bots that have agency — acting on behalf of humans to book flights, manage subscriptions, and gather information. Unlike humans, AI can juggle many interactions at the same time without fatigue, leading to a huge surge in automated queries for businesses. These bots will have a multitude of webchats and calls with brands simultaneously without any concern for the length of engagements. Impacts on revenue and customer service will be significant for businesses that are not prepared.

      Actionary Take: Brands that are not prepared for bots with agency will struggle to provide effective customer service, and will face new sales and marketing challenges.

      Read MoreGet Ahead and Equip Your Team for AI Agency in Customer Service

      Navigating the Disruption: Balancing AI Integration and Workforce Engagement Management

        Summary

        Workforce Engagement Management (WEM) is fundamental for managing employee productivity and satisfaction. WEM evolved from its predecessor, Workforce Optimization (WFO) and represented a shift in focus from purely operational efficiency to a more human-centered approach, emphasizing employee engagement and work-life balance. However, the advent of Artificial Intelligence (AI) is disrupting this model, posing new challenges and opportunities for the WEM market. Landscape confusion and competing value propositions will significantly complicate procurement decision making.

        Actionary Take: Failure to balance AI benefit with WEM principles will harm employee satisfaction and productivity for the next three years.

        Read MoreNavigating the Disruption: Balancing AI Integration and Workforce Engagement Management

        B2B CMOs Should Strategically Apply AI in Marketing to Drive Growth

          Summary

          Using Artificial Intelligence (AI) to improve personalization and customer journeys are leading priorities to drive revenue growth for B2B CMOs. However, the most widely available applications of AI in marketing are for more tactical and operational use cases such as lead scoring and propensity to buy. Access to data of suitable quality and a more holistic approach to AI infused in the marketing technology stack is key to realizing revenue growth. This research details three key success factors and how to achieve them to enable AI-powered revenue growth in Marketing.

          Actionary Take: Marketing leaders must change their approach to martech stack innovation, and address the fragmented data reality, else they will fail to realize AI-empowered growth.

          Read MoreB2B CMOs Should Strategically Apply AI in Marketing to Drive Growth

          Getting Gartner to Play for Pay  

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            The Cognitive Cost of Poor Leadership

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