Start
Introductions: (Please review here). Speed Icebreaker Question:
- In the last six months, what is the one biggest insight you have gathered from something you heard someone else say?
Reflections: True innovation comes not from pilots and POCs but instead from industrialization at scale.
Open Room Discussion
1. Reflections from scaling AI inside a hyperscaler
- What’s the learning in applying AI at Microsoft ?
- Which areas are highest capital returning areas in the application of AI?
- How do you see the next set of AI embedded products to be different?
2. The future of embedded AI in SaaS Platforms
- Data is foundational for AI – how to get it right?
- Why Embedded vs Assembled AI?
- How do you architect for composability?
Closed Door Discussion
1. Architecting AI for the Enterprise
AI - and Generative AI - is becoming a larger focus for our organizations, across the board. And Enterprise Architecture is emerging as one of the critical success factors in running AI at scale. Specifically, getting a data foundation is essential - and yet Data continues to be last unsolved problem in the enterprise. Model Accuracy is an evolving science and picking the right language model for the long term is almost a non-starter - but equally implementing a model-switchable backend to accommodate the latest models is also hard. Through all this we will explore some of the key questions that need to be resolved, the best practices that we are realizing, and the lessons have we learnt and can share.
- The Search for Value
It has been easy to get AI pilots up and running and there are a thousand things we can do with GenAI but there is a balance between AI projects we can do and the ones we should do. One of the largest questions on the table in AI and GenAI is where to invest, specifically across industries, where the use cases have shown proven value in this group and what are the considerations that drive success.
- ROI and Value Assessment: How can we effectively measure and quantify the ROI of AI investments, especially in the context of rapidly evolving technology and data architecture?
- Pilot Success and Scalability: What are best practices for conducting successful AI pilots that minimize technical debt and facilitate efficient scaling of successful projects?
- AI Agent Implementation and Business Impact: How can AI agents be effectively leveraged to automate specific tasks and drive significant business value, while considering factors like co-invention effort and economic benefits?
b. Architecting and building the data foundation for scale
As AI continues to evolve rapidly, enterprises must carefully consider their foundational model strategy, balancing the benefits of model agnosticism with the advantages of aligning with specific models. Designing robust AI architectures is essential, taking into account data management, MLDevOps, and security. In this context, data is the most unsolved problem in enterprises today – requiring a special focus on data preparation, including data cleanup, architecture changes, and acquisition of new data, to ensure AI initiatives are fueled by high-quality information.
- Foundational Model Strategy and Customization: How should organizations balance the benefits of foundational model agnosticism with the advantages of aligning with a specific model, considering factors like customization, performance, and maturity?
- AI Architecture and Infrastructure: What are the critical architectural and infrastructure considerations for deploying AI technologies, including data management, MLDevOps, and security, in a rapidly evolving landscape?
- Data Strategy and Preparation: How can organizations effectively prepare and manage their data to support AI initiatives, including data cleanup, data architecture changes, and acquisition of new data?
c. Operating Model and Governance
Beyond technical considerations, the success of AI initiatives hinges on effective governance and organizational structure. As AI models become increasingly sophisticated, questions arise about ownership, scaling ML expertise, and ensuring ethical and responsible AI development. The challenge is to create an environment that fosters innovation while mitigating risks and ensuring accountability. The ability to evaluate and monitor AI performance is key. Organizations must develop robust metrics and frameworks to assess the accuracy, security, and ROI of their AI solutions. This is particularly critical in the context of LLMs, where the complexity and potential biases of these models require careful oversight.
- Integration and Business Impact: How can AI models be effectively integrated into existing business processes and workflows to drive significant and measurable impact?
- Organizational Structure and Governance: What is the optimal organizational structure and operating model for AI initiatives, considering factors like ownership, governance, and scaling of ML expertise?
- Evaluation and Monitoring: How can organizations effectively evaluate and monitor the performance of AI models, particularly in the context of LLMs, to ensure accuracy, security, and ROI justification?
2. Practical Frameworks for Scaling Innovation
Sometimes piloting innovation can be scaringly easy - but scaling to production is almost always incredibly tough. This journey of Pilot to Production - going from experiments, incubation and POCs to scaling innovation in the enterprise - requires significant attention across the tech and business teams. We will explore some of the best frameworks and working models around organizational design and championship, strategy and capital allocation, program and portfolio management that drive success.
a. Innovation by Design
The journey of innovation begins with idea generation. Organizations must develop effective processes for capturing and prioritizing these ideas, striking a balance between centralized and decentralized approaches. Once ideas are identified, careful evaluation is crucial to separate the hype from the potential. The key is to focus on the real-world impact of innovative solutions and their ability to scale.
- Defining and Measuring Innovation: How do you define innovation in the context of IT, and how do you evaluate the potential for scaling and business impact of innovative ideas?
- Innovation Process and Methodology: What is the most effective process for generating, capturing, and prioritizing innovation ideas in IT, and how do you balance centralized and decentralized approaches?
- Innovation and Scaling: How can organizations effectively select innovative ideas that will have the highest business impact and the ability to scale?
b. Optimizing Innovation: Balancing Model and Governance
One of the key challenges in scaling innovations is establishing the right organizational structure and governance framework to support innovation. This involves determining the most effective approach for managing innovation teams, ensuring a smooth transition from incubation to scale, and avoiding the pitfalls of innovation silos. Additionally, organizations must develop robust strategies for managing risks associated with AI deployment and ensuring the long-term sustainability of innovative projects. Finally, measuring and monitoring the progress and impact of innovation initiatives is essential for continuous improvement. This requires establishing clear goals, KPIs, and feedback loops, as well as implementing effective governance mechanisms to prevent the proliferation of unsuccessful projects.
- Innovation Operating Models and Governance: What are the most effective organizational structures and governance models for managing innovation, from incubation to scale, and how can they be adapted to different organizational contexts?
- Managing Risk and Ensuring Long-term sustainability of innovation: How can organizations effectively manage risks associated with AI deployment, and ensure the long-term sustainability of innovative projects?
- Measuring and Monitoring Innovation: How can organizations effectively measure and monitor the progress and impact of innovation initiatives, from initial pilots to large-scale deployment?
c. Change Management/Culture/Mindset Change
One of the key challenges in scaling innovation is overcoming cultural barriers and implementing effective change management strategies. This involves shifting mindsets, fostering collaboration, and creating an environment where experimentation and risk-taking are encouraged. Additionally, organizations must develop a well-defined innovation strategy that balances the need for exploration with the pursuit of scalable, high-impact projects – and ensure they have the right talent in place.]
- Organizational Culture and Change Management: How can organizations overcome cultural barriers and implement effective change management strategies to foster a culture of innovation and facilitate the adoption of new technologies?
- Cultural Framework for scaling innovation: How can organizations develop effective innovation strategies that balance the need for experimentation and exploration with the pursuit of scalable, high-impact projects?
- Talent and Capability Development: What are the key talent and capability gaps that organizations need to address to accelerate technology deployment and drive innovation?
Open Room Discussion
A fast look at quantum computing
- What’s the big deal?
- What can it do?
- What are other enterprises doing with this today?
A lightning round on intelligent application startups
- What are the big themes in venture investing?
- What can we learn from (success and failure) of startups
- What should we see in coming year ?
Key Takeaways
- What’s the one thing that you heard today you will take back to the office next week?
- Other board work …
Executive Technology Board (c) | North America & Europe