Welcome, executives and professionals.

Here’s the top 1% of generative AI in the enterprise, featuring proven case studies, best practices, and enterprise accelerators.

This week, we breakdown:

• Gartner’s 10 best practices for scaling GenAI.

• Accenture’s workforce simulation model for GenAI with MIT.

• Europe’s AI opportunity: McKinsey insights for enterprises.

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

• Career opportunities & events.

Read time: 4 minutes.

BEST PRACTICE INSIGHT

Gartner shares 10 best practices for scaling Generative AI

Brief: Gartner outlined its 10 best practices for scaling generative AI in enterprises, offering actionable strategies for effective implementation.

Breakdown:

• By 2025, Gartner estimates that over 30% of GenAI projects may fail post-POC due to poor data quality, inadequate risk controls, costs, or unclear value.

• Gartner recommends: Establish a continuous process to prioritize high-value use cases. Create a framework for build vs. buy GenAI solutions.

• Pilot use cases and design a composable architecture. Prioritize responsible AI, invest in data literacy and instill robust Data Engineering Practices

• Foster seamless collaboration between humans and machines. Implement FinOps to manage total ownership costs and adopt a product-centric, agile approach.

• The near-term future of Generative AI includes smaller models, open and domain-specific models, regulatory impacts, multimodal models, and autonomous agents.

• Gartner also released its GenAI Planning Workbook that supports your AI strategy across four key pillars: vision, value realization, risk, and adoption plans.

Why it’s important: As enterprises scale generative AI, Gartner's recommended best practices offer guidance to address deployment challenges. These insights can help as a starting point to enhance use case prioritization and ensure responsible AI, helping organizations unlock potential while adapting to tech and regulatory changes.

CASE STUDY

Accenture develops workforce simulation model for GenAI

Brief: Accenture, in partnership with MIT, developed a tool to help clients redesign their workforces for generative AI. By analyzing data on tasks, skills, and job transitions, the tool offers insights into AI's impact and effective reskilling strategies.

Breakdown:

• While 97% of CxOs believe GenAI will transform their company, only 5% of organizations are actively reskilling their workforce at scale.

• The tool enables clients to experiment with simulation models, exploring various scenarios and comparing outcomes for better decision-making.

• Adjustable parameters include AI adoption propensity, investment rate, and AI innovation speed, capturing when and how companies invest in AI.

• Users can set simulation duration and upload a CSV file for around 70 parameters, or input requirements via a LLM-enabled chat interface.

• Simulation results highlight job changes, task shifts, and skills needed, highlighting the importance of reskilling while tracking revenue growth and headcount shifts.

• A results summary is also presented through a multi-model approach, transforming visuals into critical insights. Check out the infographic and demo.

Why it’s important: As generative AI disrupts industries, organizations must understand its impact on workforce dynamics to remain competitive. Accenture’s tool helps businesses make informed decisions on reskilling and productivity optimization, enabling them to adapt to changes and integrate GenAI for improved performance and growth.

BEST PRACTICE INSIGHT

Europe’s AI opportunity: McKinsey Insights for Enterprises

Brief: European businesses lag in AI adoption but have an opportunity to catch up. McKinsey’s article shows that addressing key challenges could unlock generative AI's potential, improving productivity and competitiveness.

Breakdown:

• A three-lens approach - adoption, creation, and energy - is key for assessing Europe’s generative AI competitiveness and improving labour productivity by 3% annually through 2030.

• On adoption, European businesses lag 45-70% behind the US in gen AI. With potential productivity gains still untapped, Europe has a wide-open opportunity.

• On creation, Europe leads in AI semiconductor equipment, is a challenger in foundation models, AI applications, and AI services. But holds under 5% market share in raw materials, AI semiconductor design, AI semiconductor manufacturing, and cloud infrastructure and supercomputers.

• On energy, gen AI could accelerate data center power demand to over 5% of Europe’s electricity by 2030. Electricity costs could impact AI hosting AI in Europe.

• Europe has made strides in AI awareness and setting commitments, but bottlenecks remain.

• Policy and business leaders can explore leapfrogging in emerging semiconductor technologies (such as quantum and neuromorphic computing) and addressing talent retention and reskilling.

Why it’s important: Europe risks falling further behind in AI without addressing investment, talent, and energy challenges. Focusing on these areas can enhance productivity, innovation, and growth for enterprises.

Transformation

In the news this week:

1. McKinsey emphasizes the need for companies to prioritize upskilling and reskilling in generative AI, focusing on critical thinking, AI literacy, and adaptability for workforce transformation.

2. Gartner predicts that by 2027, 80% of the engineering workforce will need upskilling in AI capabilities due to the rapid integration of generative AI into engineering tasks. 

3. Deloitte helps clients in partnership with Pramata to utilize generative AI for contract analytics, enhancing visibility across various systems and improving data-driven decision-making.

4. Accenture partners with Nvidia to accelerate AI adoption in enterprises by combining Nvidia's advanced technology with Accenture's industry expertise for customized scaling solutions.

5. Capgemini collaborates with Google Cloud to provide end-to-end generative AI solutions, guiding organizations from discovery to complete delivery through robust cloud infrastructure.

Technology

In the news this week:

1. OpenAI raised $6.6B, increasing its valuation to $157B as the top AI startup globally. They launched Canvas, a ChatGPT interface for collaborative writing and coding, and introduced new API and prompt caching features at DevDay 2024.

2. Google is advancing AI models with reasoning capabilities akin to OpenAI’s o1 system, set to increase competition. They also announced ads in AI Overview search summaries.

3. Anthropic cut RAG errors by 67% by adding key context to small text chunks before storing them. This "Contextual Retrieval" method reduces failed retrievals by 49% or 67% when combined with reranking.

4. Meta launched Movie Gen, that generates videos with sound from text prompts. It also introduced Llama Stack, a suite of tools simplifying LLM-powered app development and deployment in enterprises.

5. Microsoft announced AI upgrades for its Copilot assistant on Windows PCs, including new vision and voice features, personalization enhancements, and the re-release of the Recall feature.

Career opportunities

• Morgan Stanley - Gen AI Training Lead - Vice President

• AWS - Sr Generative AI Strategist, Generative AI Innovation Center

• JPMorgan - Generative AI - Executive Director

Events

• Gartner Webinar - Mitigate the Risks of GenAI by Enhancing Your Information Governance (1 hour) - October 07, 2024

• Generative AI Summit - October 16 - 18, 2024

• Oxford Generative AI Summit - October 17-18, 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.

Or send feedback directly, we’d love to hear from you. Your insights help us enhance your experience.

All the best for the week ahead,

Found this valuable? Share with a colleague.
Received this email from someone else? Sign up here
Let's connect on LinkedIn