Skip to content

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.

    Read MoreSolve The Deep-Rooted Strategy Problem Affecting Marketing and Sales

    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.

      Read MoreBeyond the Hype — Realigning Customer Service Strategies for Real CX Impact

      How CCaaS Vendors Can Remain Relevant in a Shifting CX Landscape

        Summary

        The customer experience (CX) landscape is evolving as diverse vendors, including CRM, Customer Engagement Centers (CEC), and Unified Communications as a Service (UCaaS) providers, increasingly encroach on the Contact Center as a Service (CCaaS) market. This shift is driven by AI advancements and the growing value of data integration across CX functions. Traditional CCaaS players face significant challenges as competitors leverage AI and data to offer more integrated and holistic solutions. This note aims to provide buyers with insights on what to look for in leading CCaaS vendors, and their solutions, based on areas they should be focusing on to deliver true CX capabilities.

        Key Take: CCaaS vendors need to focus on forming broader partnerships and enhancing data integration to achieve meaningful advancements in personalization, employee engagement, and AI-driven capabilities. Failure to do so may risk their relevance in an evolving CX landscape.

        Read MoreHow CCaaS Vendors Can Remain Relevant in a Shifting CX Landscape

        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.

          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 — 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.

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

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

              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.

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