The Rise of Task-Specific Models
While AI text-generation solutions offer multiple benefits across multiple workflows and can be applied to different solution types, they are not always the optimal choice.
Beyond the AI Model
The model(s) used by an AI solution are a core component and should be managed accordingly, e.g. through versioning. However, there are some other components that integrate with the AI solution, contributing to its IP and the overall value. These components must also be treated as essential elements of the solution.
AI Implementation and User Experience
Many organisations have rushed into “integrating” AI into their processes and products. However the successful integration of AI into existing workflows requires careful planning and an intentional approach. Without this, organisations risking disrupting the user experience, potentially becoming an additional tool that users must manage, rather than genuinely helpful enhancement.
Embedding AI Into Enterprise
Embedding AI into enterprise workflows can be a complex and presents several unique challenges, with of them being AI’s non-deterministic nature. Unlike traditional systems that produce predictable, rule-based outputs, AI systems operate with a level of uncertainty and variability. This non-determinism can introduce issues when integrating AI into established workflows. The adoption approach and the leadership team play an important role in successfully adopting AI within the organisation.
The Dominant Strategy in Game Theory and AI
One of the most classic examples of Game Theory is the Prisoner’s Dilemma, which illustrates the conflict between individual rationality and collective rationality. In this article, we will explore what game theory acn teach us about AI adoption and how the dominant strategy may lead to a race to the bottom.
A.I. = Accelerate Inefficiencies
AI is transforming the business and technology landscape, promising efficiency, innovation and better decision-making. However, for organisations already struggling with technology-business misalignment, structural and operational inefficiencies and lack of governance, AI will accelerate and amplify these challenges.
Blending AI and Product Management
The introduction of AI into product development represents a transformative shift in how products are developed, managed and evolved. AI product phases are integrating into various stages of the product management lifecycle. Successfully aligning and integrating the AI product management phases into product management lifecycle, will accelerate the pace of innovation and introduce new capabilities, but may also introduce new challenges.
ML Model Metrics and Transformation
The concept of Satisfying and Optimising metrics in Machine Learning emerges as a powerful approach that is applicable to Enterprise and Digital Transformations. In this article, we explore how this approach can be applied to identify the appropriate metrics to satisfy or optimise in the context of Transformation and some risks the Organisations must be aware.
From Project to Product, from Product to Platform
There is not a linear connection between the models in the form of going from Project to Product, and from Product to Platform. The key idea is that the three models could run in parallel depending on the transformation initiatives, serving different purposes and focusing on achieving different goals and outcomes.
System Theory and Enterprise Transformation
Viewing the Enterprise as a System can help an organisation to understand their Transformation (e.g. Business, Digital etc.) better and adopt a more holistic and integrated approach to it. In this article I will expand on this approach and I will provide ideas on how specific frameworks and theories can be leveraged to help understand better the dynamics inside an organisation during a Transformational journey, always in alignment with the view of the Enterprise as a System.
Service Mesh at the Edge
As Enterprises are under pressure to increase agility and speed to delivery, microservices and containers adoption continues to grow rapidly and faster than expected. There are many articles about the benefits of containerization, e.g. portability (write once, run anywhere), resource efficiencies and faster application start-up times.