Welcome, executives and professionals.

We identify and breakdown the top 1% of Generative AI for enterprises.

This week:

• Deloitte research: 4 scenarios to sharpen your GenAI strategy

• AWS best practices for building GenAI Agents

• Accenture on data readiness for GenAI

• Fast Fives: Transformation and technology in the news this week.

• Career opportunities & events.

Read time: 4 minutes.

STRATEGY INSIGHT

Deloitte research: 4 scenarios to sharpen your GenAI strategy

Brief: New Deloitte research outlines four possible futures for generative AI's impact on enterprises by 2027, offering scenarios to help enterprises pressure-test and strengthen their AI strategies.

Breakdown:

• Scenario 1 (Growth with Costs): Adoption advances across the enterprise. While some workers see this as intrusive, enterprises increasingly leverage agents that automate and amplify complex, open-ended work.

• Scenario 2 (The Bubble Bursts): Generative AI underdelivers amid high expectations. Inaccuracy and workforce cuts lead to frustration, as misleading outputs complicate decision-making and lower than anticipated efficiency gains.

• Scenario 3 (Advancement Depends on Humans): Success favors strategy over speed as premature scaling of GenAI proves challenging. While early gains are promising, organizations struggle to replicate success across their full scale.

• Scenario 4 (All Systems Go): By 2027, GenAI fuels a creative boom, combining with robotics, biology, to other branches of machine learning, unleashing a wave of innovation and growth that spans across the economy.

• For each of these scenarios, the research explores opportunities and risks for people, technology, and culture, and offers steps you can take to maximize investments.

• These scenarios don’t predict the probability of outcomes but challenge organizations to anticipate and manage GenAI opportunties and risks of each scenario in the context of their strategies and projects.

Why it’s important: These scenarios highlight the transformative potential of GenAI. As industries adapt, enterprises that build resilient, forward-thinking AI strategies can lead the next wave of industry innovation, or risk being outpaced.

BEST PRACTICE INSIGHT, ACCELERATOR

AWS best practices for building GenAI Agents

Brief: AWS released a two-part series (part 1, part 2, summary of both, code) sharing best practices for creating generative AI applications using Amazon Bedrock Agents. It covers aspects of solution architecture, evaluation, reusability, scaling, and more, with many aspects being platform agnostic.

Breakdown:

• Gather accurate ground truth data (including expected API usage, knowledge base access and guardrails) and clearly define the scope with sample interactions to align agent capabilities with specific business needs.

• Architect collaborative AI agents as small, focused units that interact with one another, maximizing modularity, scalability, maintainability, and ease of testing. Craft the user experience with clarity, focusing on clear instructions and integrate knowledge bases through indexed documents and citation configuration.

• Establish evaluation criteria such as response accuracy, task completion rate, and latency. Employ human evaluators and diverse perspectives for continuous refinement. Implement logging, observability, and session state management to help improve performance.

• Use infrastructure as code for consistency and reusability, optimize models for both cost and performance, and implement robust testing frameworks, including test case generation with large language models (LLMs).

• Build in security measures such as confirmation mechanisms, flexible authorization, encryption, and broader responsible AI practices. Develop a reusable actions catalog and follow a 'crawl-walk-run' approach to scale agent usage gradually.

Why it’s important: AWS’s best practices can help enterprises develop and deploy efficient, scalable, and secure generative AI agentic applications for production. AWS’s repository contains examples and use-cases to get you started.

BEST PRACTICE INSIGHT

Accenture on data readiness for GenAI

Brief: Accenture’s new report, drawing from insights on 1,800+ GenAI projects, outlines data essentials with real-world enterprise examples, and key actions that data-focused executives can consider to improve readiness.

Breakdown:

• Nearly half (48%) of 2,000 CXOs surveyed in 2024 indicated their organizations lack the high-quality data needed to operationalize generative AI effectively. Generative AI yields the greatest impact when powered by a company’s proprietary data, as seen in BBVA’s new digital sales model.

• Unstructured formats like text, images, audio, and video hold significant untapped potential for AI, as demonstrated by Fortune’s improvements in complex data analysis.

• Synthetic data can help fill gaps in real-world data cost-effectively and reduce biases. An end-to-end data foundation breaks down silos, like Accenture’s GenAI platform for BMW, managing data from collection through post-use.

• Generative AI accelerates data-related risks (legal, reputational or both). For mitigation, enterprises can establish Responsible AI principles, conduct risk assessments, and other data governance strategies.

• Applying Generative AI to a firm’s data can improve data readiness (for instance, summarizing and classifying business data requirements, and generating test cases/synthetic data)

• Accenture's data readiness recommendations include identifying unique data for differentiated value (e.g. McDonald's), evolving architecture (e.g. Databricks and Snowflake), and using generative AI to streamline data management, as Cencora has done.

Why it’s important: Generative AI is changing data as we know it, increasing its importance and creating new requirements for companies. These recommendations can help as a starting point for achieving data readiness.

Transformation

In the news this week:

1. IBM's new report discusses the new era of GenAI-powered consulting, enabling digital labor and assets for improved quality and reduced costs. They also released a video with code on building full-stack GenAI applications.

2. Gartner and Forrester shared their top predictions for 2025 and beyond. Gartner predicts that by 2026, 20% of organizations will use AI to streamline their structure, cutting more than half of middle management positions.

3. DHL Supply Chain is leveraging Generative AI to improve data management and customer support. Its data solution helps engineers design logistics quickly and efficiently.

4. Deloitte's research explores how unique government circumstances may lead to a more bottom-up approach for scaling Generative AI, promoting widespread tool access within established guardrails.

5. McKinsey’s new article on Generative AI in corporate functions shows IT leading adoption, while finance may be in "pilot purgatory". They also offer recommendations to move beyond efficiency gains.

Technology

In the news this week:

1. Google is developing an AI agent codenamed ‘Project Jarvis’ to control web browsers for everyday tasks, with a preview expected in December. DeepMind also unveiled SynthID, a watermarking system for AI-generated content.

2. OpenAI CEO Sam Altman refuted claims of a new AI model called ‘Orion’ launching in December, calling the reports "fake news". OpenAI Scientist Noam Brown noted that giving AI models 20 seconds to "think" can match a 100,000x training data boost.

3. Microsoft announced new agentic capabilities for Copilot and Dynamics 365, enabling users to create or use pre-built agents that enhance processes across the platforms.

4. IBM released Granite 3.0, a new suite of open-source enterprise AI models, providing improved performance and safety features for businesses.

5. Anthropic introduced ‘computer use’, allowing Claude to interact with computers by viewing screens, typing, moving cursors, and executing commands.

Career opportunities

• Adobe - Director, Product Management, Firefly AI Platform

• Barclays - Conversation AI & Gen AI Product Director

• AWS - Principal Specialist SA, Generative AI and ML

Events

• Microsoft - Explore cutting-edge models: LLMs, SLMs, and more - October 31, 2024 (Virtual, 1:25 hours)

• Generative AI Summit, London - November 07, 2024

• The AI:ROI Conference - November 14, 2024

Whenever you’re ready, here’s how we can help you:

1. Get 12 proven Generative AI case studies from industry leading enterprises for free. In-depth analysis and tangible value delivered.

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All the best for the week ahead,

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