WELCOME, EXECUTIVES AND PROFESSIONALS.

Since the last edition, we've reviewed hundreds of the latest insights on best practices, case studies, and innovation. Here’s the top 1%...

In today’s edition:

• BCG AI playbooks for senior executives.

• McKinsey’s AI transformation blueprint.

• Deloitte Legal on contracting for GenAI.

• Transformation and technology in the news.

• Career opportunities & events.

Read time: 4 minutes.

ACCELERATORS & CASE STUDIES

BCG AI playbooks for senior executives

Brief: Boston Consulting Group (BCG) released two new playbooks, totaling eight, to guide CEOs and senior executives (CFOs, COOs etc.) on AI transformation, including GenAI. Each ~25-slide playbook draws from 1,000+ AI programs, offering strategies, roadmaps, and case studies to drive enterprise value.

Breakdown:

• For Finance Leaders, the 23-slide Finance playbook explores AI opportunities, evolution of processes, operating models, and more.

• For Technology Leaders, the 24-slide Data & Digital Platforms playbook covers maximizing data value, evolving tech stacks for AI, and more.

• For Operations Leaders, the 20-slide Supply Chain playbook highlights GenAI applications and starting points, while the 24-slide Customer Service playbook explores economics, teams, and more.

• For Risk Leaders, the 32-slide Risk & Compliance playbook details AI-driven risk management, capability evolution, and more.

• For Sales Leaders, the 23-slide Customer Engagement playbook covers ideation, personalization, and communication, while the 23-slide B2B Sales playbook focuses on team evolution, strategies, and more.

• For People Leaders, the 22-slide HR playbook outlines future structures, tools, skills, and GenAI performance gains.

Why it’s important: These playbooks for leaders provide actionable strategies and real-world learnings to increase the likelihood of success in realizing tangible value from AI investments.

BEST PRACTICE INSIGHT

McKinsey’s AI transformation blueprint for banking

Brief: McKinsey published a blueprint to help financial services leaders unlock enterprise-wide AI value. While tailored for banks, the insights are widely applicable.

Breakdown:

• Typically 70–80% of incremental AI transformation value in banks comes from just 10 of approximately 25 subdomains, such as customer underwriting.

• These priority areas can be transformed using a mix of generative AI, traditional analytics, and digital tools and platforms.

• Integrated via a tech stack powered by multi-agent systems, incorporating 'New elements' enabled by GenAI (see exhibit 4 in full article).

• New elements include multimodal conversational experiences, digital twins for simulating behavior, AI orchestration for complex workflows, AI agents for specialized tasks like fraud detection, and more.

• A high-level transformation checklist emphasizes establishing a comprehensive AI vision, leveraging a range of technologies, reimagining business domains, and ensuring component reusability.

• Only a few leading banks currently achieve material AI transformation value, but others have a window of opportunity to catch up in the next few years.

Why it’s important: McKinsey's blueprint outlines a path to harness AI, focusing on key subdomains, a full-stack approach, and technologies like multi-agent systems for enterprise-wide transformation.

BEST PRACTICE INSIGHT

Deloitte Legal on contracting for GenAI

Brief: Deloitte Legal suggests key risks to address in contracts when procuring GenAI systems. The 25-page report also considers GenAI risk scenarios in the supply chain, the EU AI Act’s impact, and putting theory into practice.

Breakdown:

• When procuring GenAI systems, ensure lawful training data by verifying its provenance and requesting details if not publicly available. Include indemnity clauses for breaches or third-party claims.

• Be cautious of data privacy by defining the data processing scope, limiting it to necessary use, and allocating security and compliance responsibilities between the GenAI provider and the organization. Involve privacy officers early.

• Make sure your data is not misappropriated by clearly defining what the GenAI vendor can do with the inputs and outputs, ensuring they align with the intended use cases and data permissions. The vendor should implement measures to prevent unauthorized access to the data.

• Avoid contract lock-in by assessing terms like long durations, volume commitments, termination rights, and exit support. Other risks to address in contract include sector regulation, liability, ESG policies, cybersecurity, and more (see full report, page 9).

• The report also covers four GenAI supply chain risk scenarios (e.g. the supplier is developing code for the customer’s systems), the EU AI Act’s impact on contracts, and practical guidance on templates, procurement, due diligence, and governance.

• When managing GenAI risk, remember to consider varying legal and regulatory requirements across jurisdictions, and tailor your approach based on geographic distribution, markets, and risk appetite.

Why it’s important: These considerations can help enterprises protect data, control costs, and improve legal compliance, increasing the likelihood of smooth GenAI adoption in a rapidly evolving landscape.

BCG explores how CEOs can navigate the new geopolitics of GenAI, analyzing USA and China’s dominance and emerging competitors across capital, talent, IP, data, energy, and computing power.

Adobe shared a 19-page report on its experiences and best practices for enterprise AI adoption, while Bain’s survey found 75% of executives see GenAI as a major disruption risk to their industries.

Deloitte published its 72-page Tech Trends 2025 report, where AI is the common thread in nearly every trend. Its GenAI safety checklist assesses risks in strategy, people, process, data, and technology to build trust in AI applications.

Gartner predicts that 85% of customer service leaders will explore or pilot customer-facing conversational GenAI in 2025. Capgemini published a 15-page report including seven levers to transform customer service with GenAI.

NTT DATA achieved an ROI exceeding 400% with Spain’s largest telecom operator using Microsoft Copilot. IBM shared a video on five examples of GenAI in sales, while Cognizant explores how to “think and act like an AI-native business."

Google introduced Agentspace for enterprises to handle tasks involving planning, research, content creation, and execution. It also unveiled Gemini 2.0, the Willow quantum chip, Trillium TPUs with 4x faster AI training, and announced a $20B investment in data centers and clean energy.

OpenAI launched Sora, upgraded ChatGPT’s Advanced Voice Mode for live video and screen analysis, introduced Reinforcement Fine-Tuning for custom tasks, and reintroduced 'Anonymous-chatbot' in the LM Arena, fueling rumors.

Databricks introduced Generative AI Partner Accelerators, Microsoft’s CEO discussed its enterprise AI strategy, Cognition Labs officially unveiled Devin, its AI developer assistant, and Replit upgraded its AI development suite.

Microsoft released Phi-4, a 14B parameter model outperforming larger competitors. Meta unveiled Llama 3.3, a faster, cheaper 70B model, and Anthropic expanded access to its fastest model, Claude 3.5 Haiku.

Nvidia expanded hiring in China despite an antitrust probe in the country amid US chip export restrictions. AMD CEO Lisa Su was named Time Magazine ‘CEO of the Year’ for transforming the company.

CAREER OPPORTUNITIES

BCG - Global Director, GenAI Platform

University of Pennsylvania - Executive Director, Wharton Gen AI Labs

Google - Director, AI Data Partnerships

EVENTS

Unlocking Enterprise AI - January 14, 2025

AI Transforming the Labor Market - January 7, 2025

All the best,

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