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Solve The Deep-Rooted Strategy Problem Affecting Marketing and Sales

    Summary

    When companies struggle to realize growth ambitions or revenues plateau, the natural ambition is to revisit Marketing and Sales practices to find out what’s wrong. The reality is that when a company decides to dig a little deeper, they often find there’s a deep-rooted strategy problem. There are good reasons for this — markets change shape, competitive products evolve, and founders realize that the original reason for existing isn’t as relevant anymore. In this research we dive into what’s likely to be happening and why, then provide some solutions on how to move forward.

    Key Take: Companies must regularly reassess their purpose, align their value proposition with market shifts, and ensure resources are strategically positioned to drive sustained, scalable growth. Without this discipline, businesses risk losing their competitive edge and no longer being relevant.

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    Create an Architecture of Trust with the Latest in AI-Related Data Security

      Summary

      Data security and privacy have long been essential in technology. Artificial Intelligence (AI) introduces new, often overlooked risks that require an understanding of new security concepts and a modern approach. Traditional security practices won’t address AI-specific challenges such as adversarial attacks, new data extraction techniques, and regulatory compliance under new laws like the EU AI Act. This research provides insight on the nuances of these issues buyers should be aware of and provides actionable strategies for AI solution providers to mitigate risks and maintain client trust.

      Key Take: AI introduces new security risks. Providers must demonstrate a clear understanding and evidence of how AI-specific security measures have been implemented to stay compliant and secure to maintain client trust.

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

        Summary

        The customer service technology landscape continues to significantly transform 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

          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.

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

            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

              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.

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

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