Quick bits

• Strategy: Harnessing generative AI can skyrocket productivity. Dive into our synopsis of the McKinsey Quarterly article exploring the crucial skills for its deployment across enterprises.

• Trends: "Unveil the Future of AI Integration: To Bolt or Go Native?" - a pivotal choice for modern organizations.

• Tools: Don't miss this FREE educational gem offering an LLM Bootcamp and Deep Learning course—too good not to share!

  • Key Takeaways from AI Summit II: Insightful discussions on "LLM Application Testing & Evaluation: Process, Tools, SDLC."

• Prompt: With ChatGPT's new memory feature, expect more relevant and personalized interactions over time.

Strategy

Strategy Insight: The McKinsey Quarterly highlights "The human side of generative AI: Creating a path to productivity," with the following key insights:

• Flexibility and relational dynamics trump compensation for AI talent.

• Supportive environments and meaningful work are magnets for top talent.

• Generative AI could automate up to 30% of tasks by 2030, boosting productivity.

As jobs change, companies will need to look at worker outcomes according to the results achieved, not by hours spent. The benchmark for output will have to shift.

Trends

Trends Analysis: The choice between AI-native applications and bolt-on AI solutions shapes how organizations evolve. AI-native options promise seamless integration and scalability, ideal for long-term growth, while bolt-on solutions offer immediate, cost-effective enhancements with minimal disruption.

Deep Dive

In today's fast-paced technological world, organizations are at a pivotal junction in choosing how to integrate Artificial Intelligence (AI) into their systems—opt for ground-up AI-native applications or enhance existing infrastructures with bolt-on (internal)/in (external vendor) AI solutions. AI-native applications are inherently built with AI from inception, designed to leverage machine learning and advanced data analytics at their core. This approach not only enhances operational efficiency but also ensures a high degree of scalability and adaptability, aligning perfectly with future tech advancements and evolving business needs.

However, not every company is prepared to leap into AI-native development due to various constraints, ranging from budget limitations to the risk of disrupting established systems. This is where bolt-on AI solutions come into play. They allow businesses / vendors to attach AI functionalities to their existing applications, providing a significant boost in performance and capabilities without the need for extensive overhauls. This method can be particularly appealing for organizations seeking immediate improvements and ROI, as it taps into the power of AI with minimal disruption and investment.

Understanding the distinct paths to AI integration—either through AI-native or bolt-on/in solutions—is crucial for any tech leader or business strategist. Each approach offers unique advantages and can be tailored to meet specific organizational goals and industry demands. By diving deep into these options, companies can make informed decisions that not only optimize their current operations but also strategically position them for future technological landscapes. Discover which AI integration strategy best aligns with your business objectives and technological readiness to fully harness the potential of AI in transforming your operations.

AI-Native Solutions

Pros:

• Fully Integrated AI Capabilities: Designed from the ground up with AI, ensuring seamless integration and operation.

• Optimized for Efficiency: Each layer of the application can fully leverage AI, resulting in potentially greater operational efficiency and effectiveness.

• Future-Proof: Typically more scalable and adaptable to future technological advancements, making it suitable for long-term strategic planning.

Cons:

• Higher Initial Investment: Often requires more resources and time to develop, leading to higher initial costs.

• Complex Implementation: Might involve significant changes or complete overhauls of existing systems, which can be disruptive.

• Longer Time to Market: The development cycle can be lengthy as everything is built from scratch, possibly delaying ROI.

Bolt-On AI Solutions

Pros:

• Cost-Effective: Allows for the addition of AI functionalities without the need to rebuild or replace existing systems, making it more budget-friendly.

• Quick Deployment: Faster to implement as it involves adding to existing structures rather than building anew.

• Less Disruptive: Minimizes disruption to current operations, as it enhances systems that teams are already familiar with.

Cons:

• Potential Integration Issues: May face compatibility and efficiency challenges with the existing system architecture.

• Limited Capability: The scope and performance of AI functionalities might be constrained by the limitations of the original system.

• Short-Term Focus: While effective for immediate needs, bolt-on solutions may not be as scalable or adaptable to future changes as AI-native applications.

Understanding the strategic implications of each approach, empowers enterprises to make informed decisions that align with their specific needs and readiness for AI integration. IMHO it is not if but when Native AI Applications eat the world. At this point, startups are in the gold rush to deliver the first killer apps while enterprises are trying to figure out how to best implement AI to maximize ROI. Soon enterprises won’t have a choice because all applications will have AI bolted on and then over the next five years or so AI will be natively built in all applications. The question is how long can enterprises wait to implement AI without being made obsolescent?

Svitlana Rahimova had an excellent post highlighting the enormous potential of AI solutions for enterprises to help streamline any process. However, projecting, development, implementation, deployment, and scaling remain challenging. Many pilot projects finish at the pilot stage and never go to production. Deriving value from AI is still not easy and requires working with experienced experts.

Tools

Tool Spotlight: The Full Stack connects enthusiasts to master AI product lifecycles, from inception to deployment, and it's free!

Stefan Krawczyk's Insights: Learn from his Lightning Session on Building a Document Processing Pipeline for RAG Systems (it's free, they just want an email for it).  I owe Stefans a cup of Joe for all the knowledge he dropped on me this week!

Key Insights from Stefan Krawczyk’s Newsletter on the HeavyBit DevGuild: AI Summit II - "LLM Application Testing & Evaluation: Process, Tools, SDLC":

Tools providers listen up, your niche is calling:

• Still early days for people getting to production. So lots of challenges, no consensus on the best way to do things yet.

• Nobody likes their tools.

Things that are hard and people are dealing with:

• Curation of evaluation data is hard

• Things can take a long time, and could be costly, e.g. evaluating agents thoroughly:

• Choosing the right metrics can confusing. What do you focus on: model metrics or business metrics?

• No consensus on tooling. Lots of vibe checks, etc.

LLM Evaluation Tips: Challenges remain in production scaling and tool satisfaction. Strategies to consider include using domain experts for data labeling and incorporating synthetic data.

Training

Step into the Future with Hyperdrive! Are you a Product Manager looking to master AI or earn your AI Certified Scrum Product Owner license? Hyperdrive offers top-tier Agile training, consulting, and staffing—power up your career in AI today!

Choose From Two Classes Below:

1. May 16-17, 9am-5pm Eastern time

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Prompts

Read the Memory FAQ to learn about the exciting new ChatGPT 4 Memory feature. Previously, I often wished ChatGPT Plus could remember details without needing to manually update Custom instructions. Now, Memory functions similarly but with a significant enhancement: ChatGPT has trained its models to manage memory updates automatically, freeing users from manual adjustments.

As you interact with ChatGPT, it will become increasingly helpful by recalling details and preferences from your discussions. You'll notice ChatGPT's memory improving the more you use it. Remember, you are in complete control of ChatGPT’s memory.

You have the option to reset it, clear specific entries or all memories, or disable this feature entirely in your settings. If you prefer a session without memory, simply switch to Temporary Chat.

To understand what ChatGPT remembers, just ask it. You can view specific memories by selecting 'Manage.' If you need to delete a memory, click the trash can icon next to the memory in question.