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Beyond the Hype — Realigning Customer Service Strategies for Real CX Impact

    Summary

    The customer service technology landscape is undergoing a significant transformation as vendors aim to offer more comprehensive customer experience (CX) solutions. However, despite the promise of an enhanced service value proposition, the actual customer experience often falls short due to the scope, complexities, and immaturity of current offerings. This research examines the shifting dynamics in the customer service vendor market, identifies the challenges faced by vendors and organizations, and provides actionable recommendations for adopting a more integrated and future-ready customer service strategy. 

    Key Take: Customer experience success requires recognizing the limitations of current offerings and strategically preparing for a future where comprehensive customer service solutions are the norm. Without careful internal alignment and strategic planning, organizations risk under-delivering on the promise of enhanced customer experience.

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    Cloud Is 28 Years Old — Catch up Now to Be Able to Compete in the AI Innovation Race

      By Simon Harrison
      Analyst and Client Executive Partner

      Simon is an industry analyst who has authored over 30 Gartner Magic Quadrant notes as lead and with colleagues. He’s written important research as the Chief of Research advisor for Gartner. He continues to provide deep research and insights as an Executive Partner for Actionary clients and the industry.

      Summary

      Cloud computing has come a long way since it was first envisioned 28 years ago, transforming from a revolutionary concept to an essential driver of business innovation. From its origins in a 1996 Compaq business plan, cloud computing has evolved into a critical enabler of AI, offering unprecedented growth, efficiency, and scalability. This research revisits the original cloud vision and how dramatically cloud has evolved since. It covers what you need to know to realize the pivotal benefits of cloud computing, that is driving unprecedented growth and retention increases in today’s digital economy.

      Key Take: Companies relying on outdated on-premises technology in their innovation strategy will fall behind competitors who are realizing cloud-powered and AI-related growth and customer retention.

      Read MoreCloud Is 28 Years Old — Catch up Now to Be Able to Compete in the AI Innovation Race

      Don’t Drift Away from Reality — Ensure Vendors Offer AI Model Longevity

        By Simon Harrison
        Analyst and Client Executive Partner

        Simon is an industry analyst who has authored over 30 Gartner Magic Quadrant notes as lead and with colleagues. He’s written important research as the Chief of Research advisor for Gartner. He continues to provide deep research and insights as an Executive Partner for Actionary clients and the industry.

        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.

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

          By Chris Marron
          Analyst and Distinguished Advisor

          Chris is an industry analyst who leverages the breadth and depth of his background to provide a unique view on industry trends and dynamics, as well as specific actionable advice tailored to the exact needs of customers. As a distinguished advisor for Actionary, he continues to expand his wealth of knowledge and insight.

          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

          Maximize AI With Clear Understanding of Fine-Tuning vs. Retrieval-Augmented Generation (RAG)

            By Simon Harrison
            Analyst and Client Executive Partner

            Simon is an industry analyst who has authored over 30 Gartner Magic Quadrant notes as lead and with colleagues. He’s written important research as the Chief of Research advisor for Gartner. He continues to provide deep research and insights as an Executive Partner for Actionary clients and the industry.

            By Simon Harrison

            Summary

            Fine-Tuning and RAG (Retrieval-Augmented Generation) are essential AI model enhancement methods, each offering distinct advantages and challenges. Implementing these approaches strategically, based on specific AI application needs, can significantly enhance the performance and outcomes of pre-trained models. Conversely, choosing the wrong approach can undermine the benefits of an AI solution. This research explores the benefits of both Fine-Tuning and RAG, emphasizing the importance of selecting and combining these methods appropriately to achieve optimal results.

            Key Take: Companies that fail to align Fine-Tuning and RAG with the appropriate use cases will encounter inefficiencies, diminished performance, and missed opportunities to fully leverage AI’s potential.

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            Navigating the Disruption: Balancing AI Integration and Workforce Engagement Management

              By Jim Davies
              Analyst and Client 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.

              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.

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              B2B CMOs Should Strategically Apply AI in Marketing to Drive Growth

                By Simon Harrison
                Analyst and Client Executive Partner

                Simon is an industry analyst who has authored over 30 Gartner Magic Quadrant notes as lead and with colleagues. He’s written important research as the Chief of Research advisor for Gartner. He continues to provide deep research and insights as an Executive Partner for Actionary clients and the industry.

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

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