What Value Are Business Leaders Expecting from AI and How Can They Achieve It?
Did you notice that AI technology is evolving much faster than its application in businesses? Business leaders play a crucial role in bridging this gap, but many are still grappling with how to effectively integrate AI into their operations.
At the recent AI Summit in New York, Nicolas de Bellefonds from Boston Consulting Group shared some eye-opening insights on this very topic. Inspired by his talk, I wanted to share these valuable takeaways with you.
In this article, we’ll delve into the current state of business maturity in generative AI, highlighting the differences across various industries and regions. We’ll also examine how companies can unlock real value from their AI investments. Additionally, we’ll share insights from industry leaders on how they are driving superior value at scale with AI.
Business Maturity in Generative AI
Current State of Business Adoption
- Early Stages (50%): Many large corporations are still exploring AI, asking questions like “Where is the value?” and “How do I manage the risks?”
- Initial Actions (40%): Some companies have started using AI in key areas like customer service and marketing, aiming to improve efficiency.
- Large-Scale Integration (10%): A few leading companies are embedding AI across their entire organization, driving broad-scale impact.
Industry and Geographic Disparities
- Leading Sectors in AI Adoption: Telecom, media, and technology industries are leading in AI adoption.
- Geographic Variations: North America leads in AI adoption, followed by Asia, with Europe lagging behind. Within these regions, there are significant differences in how quickly various industries are adopting AI.
- Lagging Sectors: Consumer goods, pharmaceuticals, and healthcare systems are slower to integrate AI.
The Myth of Catching Up
There is a belief that businesses can wait and then quickly catch up by adopting the latest AI technologies. However, this is not supported by current trends.
- Widening Gap: Leaders in AI adoption are continuously investing in and building capabilities, creating a widening gap between them and the laggards.
- First Mover Advantage: Companies that adopt AI early are experiencing a virtuous cycle of learning, investment, and return, further distancing themselves from competitors. This advantage was evident before generative AI and remains true today.
Understanding the Value of Generative AI
As companies navigate the integration of generative AI, they often ask similar questions: How do we find real value? How do we balance costs and risks? What about the impacts on technology and people?
There are three main strategies to create value with generative AI: deploy, reshape, and invent.
1. Deploy: Enhancing Everyday Tasks
Use AI for everyday tasks like managing emails and calendars.
For example, BCG uses AI tools like Microsoft Copilot, saving consultants 6.5 hours per week.
This translates to an 18% increase in productivity. However, while this widespread productivity boost is significant, it doesn’t drastically change the business model.
2. Reshape: Transforming Workflows
Transform specific jobs and processes, such as:
- Customer Service: AI can transcribe conversations, find answers, and summarize meetings. This reduces call durations by 25%, boosting productivity and customer satisfaction.
- Marketing: AI helps reduce costs per item, allowing companies to produce more content at a lower cost while also enhancing engagement and quality.
- Technical Maintenance: AI assists field technicians, cutting down maintenance time by 30%, leading to either cost savings or more efficient operations.
These targeted changes drive substantial improvements in specific areas, often resulting in a 25–30% boost in efficiency.
3. Invent: Developing New Business Models
Inventing with AI involves creating entirely new products and services. This is particularly relevant in industries like beauty, fashion, and media. Examples include:
- Virtual Assistants: AI helps customers with beauty routines or fashion choices.
- Insights Platforms: B2B companies can turn data into valuable insights for their clients. For example, a financial data company transformed its service into an AI-powered insights platform, generating significant new revenue.
This approach focuses on long-term competitive advantages by leveraging existing data to offer innovative, high-value services.
Challenges and Risks
When implementing generative AI, there are various operating risks to consider:
- Data Privacy: Ensuring sensitive information is protected.
- Intellectual Property (IP): Managing and securing IP rights.
- Confidentiality: Safeguarding confidential data from unauthorized access.
- Technical Stability: Balancing AI performance with system stability and cost-efficiency.
- Ethical Concerns: Addressing potential biases, misuse, and ensuring AI operates within ethical guidelines.
- People: The big challenge is process redesign and workforce planning. Around 50% of employees may be impacted within the next three years, requiring significant adjustments in how people work.
- Operational Risks: Managing AI errors (hallucinations) and ensuring decisions are based on accurate data.
Key Lessons from AI Leaders: Driving Superior Value at Scale
Leaders in AI have shared valuable lessons on how to drive superior value at scale. Here are three key takeaways:
1. Focus on Outcomes, Not Technology
Leaders in the field of AI emphasize the importance of focusing on business outcomes rather than getting caught up in the technology itself.
They prioritize a few critical business outcomes they want to achieve and use generative AI to serve those goals. This approach helps in driving significant impact across the enterprise.
For large companies, leaders typically aim for a 5–10% improvement in EBIT (Earnings Before Interest and Taxes), which can translate to substantial value.
For instance, in a $20 billion company, this could mean a potential value increase of around $1 billion. Achieving such outcomes requires significant investment, often at a ratio of 1:3 .
This means companies may need to invest several hundred million dollars to realize these gains.
2. Right Brain, Left Brain Approach
A common misconception among many clients is that generative AI can replace all previous technologies.
However, leaders understand that generative AI complements rather than replaces existing technologies. They liken :
- Generative AI (Right Brain): This aspect of AI is creative, inductive, and good at making assumptions and solving problems.
- Predictive AI (Left Brain): Complementing generative AI, predictive AI and machine learning provide accuracy, precision, and decision-making capabilities.
Just like our brains, both sides (generative and predictive AI) are necessary to generate true value.Therefore, a balanced approach that leverages both generative and predictive AI is crucial for success.
3. The 10–20–70 Rule
One of the most critical lessons from AI leaders is the 10–20–70 rule, which emphasizes the distribution of effort required for successful AI implementation. According to this rule, effort distribution:
- 10% on AI Models: Focus on developing smart algorithms.
- 20% on Technology and Data: Ensure robust tech and data infrastructure.
- 70% on People and Processes: The majority of effort should be on upskilling, training, and changing processes.
Many companies make the mistake of concentrating too much on the technological aspects, neglecting the critical role of people and processes. This oversight often leads to project failures.
Conclusion
The transformative power of generative AI is undeniable, yet many businesses are still navigating the complexities of adoption and integration. By understanding the current state of business maturity in generative AI, we can see the clear disparities across industries and regions. However, the path to unlocking real value lies in strategic implementation through deploying, reshaping, and inventing.
Leaders in AI are setting the pace, creating a widening gap with those who lag behind. The first-mover advantage is significant, as early adopters benefit from a virtuous cycle of learning, investment, and return. By following these insights and strategies, businesses can better navigate the generative AI landscape and achieve their desired outcomes, securing a competitive edge in an increasingly AI-driven world.
Let’s Make Things Happen with AI!
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- Are you ready to explore what AI can do for your business? Connect with me on LinkedIn to discuss innovative AI solutions that can boost your business’s growth and efficiency. Let’s turn possibilities into realities together!
You can find the original video of the discussion here: AI Summit New York — Nicolas de Bellefonds.
