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Deloitte envisions the CEO in 2030

Deloitte envisions the CEO in 2030

Plus, McKinsey's 4 enterprise shifts, Accenture on responsible AI, and more.

Lewis Walker
December 09, 2024

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:

Deloitte envisions the CEO in 2030.
McKinsey’s four GenAI-driven enterprise shifts.
Accenture on responsible AI value and maturity in enterprises.
Top-tier US bank's transition to private AI deployment.
Fast Fives: Transformation and technology in the news.
Career opportunities & events.

Read time: 4 minutes.

CASE STUDY ENVISIONED

Deloitte envisions the CEO in 2030

Image source: Deloitte

Brief: Deloitte's new 18-page report imagines the CEO role in 2030, exploring how GenAI could reshape leadership and decision-making through a futuristic day-in-the-life scenario.

Breakdown:

Deloitte contrasts a CEO’s daily experience today with a 2030 vision shaped by AI, outlining 12 key moments of transformation.
A moment at 8:30 AM in 2030 for instance, CEO Sanjay joins his kitchen cabinet meeting. The AI agent COO collaborates with Erika, the Chief of Staff AI agent, to recommend retracting a recent benefits communication.
"Understood, I'll send a follow-up clarification," responds Hector, the CHRO. The team then reviews AI simulations of their board members to anticipate questions and align their responses.
Later, at 1:20 PM, Sanjay arrives at his office and meets with Sarah, his CSO, to discuss AI chip investments for the next five years. Later, Sanjay uses a digital surrogate for the 2 PM town hall, giving him time to prep for the board meeting.
He joins the last 15 minutes for live Q&A. Some employees are impressed by the surrogate, but one expresses, "You can’t delegate culture."
Page 16 of the report poses questions like would you still want to be CEO in 2030? Why? What layers of introspection (rational, moral, emotional, and existential) does this question provoke?

Why it’s important: AI has the potential to transform executive roles, making leadership even more dynamic, data-driven, and collaborative. Deloitte’s vision offers engaging scenarios for leaders preparing to drive enterprises of the future.

BEST PRACTICE INSIGHT

McKinsey’s four GenAI-driven enterprise shifts

Image source: McKinsey & Company

Brief: McKinsey explores four GenAI-driven shifts in enterprises: AI-led vs. human-led work patterns, application to multi-agent architectures, flatter organizational structures, and a shift from application- to infrastructure-based costs.

Breakdown:

To break down the second shift, IT architecture is set to shift from traditional application-centric approaches to multi-agent architectures over the coming decade.
In this context, Tech leaders will oversee hundreds or thousands of GenAI agents collaborating to achieve common goals. For example, a fleet of GenAI agents monitor inventory and generate orders without complex integrations.
GenAI agents are expected to be deployed in three primary ways: super platforms (e.g. CRM with built-in agents), AI wrappers (third-party APIs), or custom agents (fine-tuning, RAG).
The choice of platform strategy depends on a number of factors, including the potential of proprietary data to differentiate enterprises.
Modular frameworks are key for designing reusable agents that can be modified and assembled, like LEGOs for different workflows.
For more, including the other three enterprise shifts, see the full article.

Why it’s important: Companies often overestimate short-term tech impacts and underestimate long-term shifts. GenAI has the potential to drive profound changes in IT, requiring leaders to reinvent strategies and execute with persistence for sustained competitive advantage.

MARKET INSIGHT & CASE STUDY

Accenture on responsible AI value and maturity

Image source: Accenture

Brief: Accenture published a 31-page report on responsible AI, based on a survey of over 1,000 global executives. It covers expected value, 12 enterprise examples, and the maturity of responsible AI across organizations.

Breakdown:

Responsible AI is about designing, deploying, and using AI to create value, build trust, and reduce risks.
Accenture’s responsible AI maturity framework (page 24 in report), developed in collaboration with Stanford University, identifies four stages; most surveyed companies are at stage 2, and none have reached stage 4. On average, organizations progress from one maturity stage to the next in 18 months.
Accenture identified six value levers for responsible AI: financial performance, customer experience, risk and compliance, sustainability, talent, and inclusion and diversity.
Companies investing in responsible AI expect a 25% improvement in customer loyalty and satisfaction (customer experience), 82% believe it enhances employee trust in AI adoption (talent), and 64% foresee a strong or very strong impact on contract win rates (financial).
Mastercard prevented $20 billion in fraud with a comprehensive responsible AI governance framework and thorough review processes in its fraud detection systems (Financial)
Rolls Royce deployed its 'Intelligent Borescope' AI tool, cutting inspection times by 75% and expected to save up to £100M in costs over five years, guided by its responsible AI Aletheia Framework (Financial).
Accenture trained 700,000 employees in responsible AI, with 25% of those trained being data and AI experts (Talent).

Why it’s important: Responsible AI is a strategic necessity for driving value with trust. With no companies yet achieving full maturity, there is vast opportunity to lead for competitive advantage.

CASE STUDY

Top-tier US bank's transition to private AI deployment

Image source: AI21 Labs

Brief: A leading US bank partnered with AI21 Labs, a top GenAI startup, to transition from public cloud AI models to a private deployment, addressing the bank’s compliance, latency, and scalability requirements.

Breakdown:

AI21 Labs privately deployed two use cases: data summarization and communication intelligence.
Data summarization processes large datasets like news and earnings transcripts, providing actionable insights for wealth managers and analysts.
Communication intelligence analyzes customer interactions, such as chats and emails, to support relationship management and opportunity identification.
The deployment leverages the bank’s AWS Virtual Private Cloud with custom-tuned AI models and integrated security and compliance frameworks.
The results: faster processing, improved customer service, enhanced security and compliance, and a cost-effective, scalable foundation for future AI use cases.

Why it’s important: This case study provides an example for enterprises in similar contexts to help securely unlock the value of GenAI. A 6-minute video overview is available.

McKinsey published articles on scaling agents for IT modernization and the rising demand for SSDs driven by GenAI enterprise adoption.

Deloitte released a 23-minute intro video on GenAI in Sales, a 12-page report on enhancing customer experience in insurance with GenAI, and a 13-page GenAI guide for corporate legal departments.

Gartner released a 32-minute presentation recording on six GenAI disruptions for business, 14 slides on top GenAI use cases for tech and service providers and a brief case study on corporate GenAI policies.

AWS released a 19-page guide on responsible AI, 14 slides on GenAI for digital transformation, and re:Invent videos on topics such as observability best practices and using multiple agents for scalable GenAI applications.

WEF published an article on how Agentic AI will transform financial services, while the CIGI released a 24-page paper on Military AI in great power rivalry.

Amazon furthered its ambition at re:Invent to build a full AI ecosystem, addressing supercomputing (Project Rainier), chips (Trainium), models (Nova), and partnerships (Anthropic).

Microsoft published a 27-page research paper analyzing 12 AI agent-user communication challenges, with examples. It also launched Copilot Vision, enabling it to interact with web pages a user is browsing in Edge in real time.

Google released a free 5-day GenAI course covering GenAI agents, prompting, and more. IBM shared a video on fine-tuning LLMs, while Letta broke down the AI Agents stack and Nvidia published it’s 2025 predictions.

OpenAI launched its o1 model during "12 Days of OpenAI" and introduced a $200/month ChatGPT Pro tier. Reports suggest OpenAI is also considering advertising as a new revenue stream.

The U.S. tightened AI chip restrictions on China. Anduril partnered with OpenAI on AI-powered drone defense systems, and Helsing unveiled the HX-2 AI-enabled attack drone, aiming for mass production at low costs.