TLDR: AI Webinar Episode 3
This 40-minute webinar, "Changing Gear, Changing Lanes, and Reaching Your Destination," provides strategic guidance for CTOs and IT leaders navigating the AI journey. Experts discuss critical decision points on AI strategy, including whether to build your own (bespoke) or buy (off-the-shelf) AI models, using a data maturity model to assess readiness, and establishing the long-term governance and maintenance plans necessary to ensure model longevity and protect investment.
Experts on the Webinar Panel
This session is presented by experienced leaders from Agile, ensuring the insights are grounded in strategic implementation and real-world data governance:
- Ben Howarth, Head of Practice for AI: Ben provides deep technical and strategic insight into the AI model lifecycle, focusing on the hidden costs of retraining and the necessity of proper maintenance schedules (MOTs) for living, breathing AI applications.
- Mel Hodge, Head of Practice for Data Strategy and Governance: Mel offers crucial perspectives on AI governance and compliance, using the "car vs. taxi" analogy to clarify responsibility. Her expertise in defining and tracking data maturity (the Agile Data Mountain) is vital for organizations seeking to accelerate their AI adoption safely.
- Paul Dewar, Head of Practice for Delivery: Paul chairs the discussion, ensuring the focus remained on practical delivery and providing clear takeaways for technology and business decision-makers.
Changing Gear, changing lanes, and reaching your destination
There will be countless businesses trying to sell you their AI model, but which is the one to get you where you need to go? Or, do you need to create your own?
When you are on the journey, how do you know when it is time to ramp up and accelerate your strategy? Then, how do you monitor and maintain the AI, and do you have the skills in your team to do it?
Watch this 40-minute session where experienced leaders in AI strategy and implementation will share insights on how enterprise organisations can approach questions of bespoke vs off-the-shelf AI, when to step up a gear with AI, and how to extend the life of the AI model that you invested in. This webinar is tailored for CTOs, IT leaders, and decision-makers who are tasked with leading digital transformation. You will learn:
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- The essentials of AI monitoring, maintenance, and oversight
- The advantages, disadvantages, and responsibilities with bespoke AI models
- How to assess your position and readiness relation to AI (and your competition)
Should You Build Your Own AI Model vs. Buy Off-the-Shelf?
When an organisation decides to deploy an AI agent, the first major choice is whether to build a bespoke model or utilise a Commercial Off-the-Shelf (COTS) solution. This is akin to choosing between owning a car or taking a taxi. The seemingly cheaper option of buying an agent often carries hidden technical and business costs with off-the-shelf agents that may not accept or process your existing data.
This forces a costly and time-consuming process of rebuilding and validating training data, often involving valuable time from business-facing teams. While a COTS solution looks cheaper upfront, it may not meet all the 'must-have' requirements (Moscow scale) essential to the business use case, requiring a critical decision on which functionality the business is willing to lose.
The AI Governance Difference: Own a Car (Bespoke) vs. Take a Taxi (COTS)
The choice profoundly impacts your AI governance model:
- Owning a Car (Bespoke): You control everything including the build, the maintenance schedule, and the speed making you are fully responsible for the outcomes.
- Taking a Taxi (COTS): You outsource maintenance, but you still need to ensure the driver (the vendor) is operating in a way you approve of. You remain responsible for your customers' treatment and must still adhere to the five pillars of responsible AI (fairness, accountability, transparency, explainability, and safety). You are responsible for closing the gap between the bought solution and your specific needs. No matter the choice, compliance has no shortcuts.
Accelerating Your AI Journey: When to Change Gear
Moving from the testing (second) phase to the operational (third) phase of an AI deployment is "changing gear." This requires more than just testing the model; it means establishing the crucial processes that safeguard the business when things inevitably go wrong.
