How Renown Business Executives are Using AI?

Every leader, whether they realize it or not, leaves behind a trail of decisions that shape their organization’s future. Today, these decisions hold more weight than ever. Yet, the question isn’t whether you’re making the right choices. It’s whether those choices set the right example for your company to follow.

The question is: where do you stand? Are you using the latest tools to stay ahead? Are you growing and learning? Most importantly, are you shaping the right kind of mindset for your company culture to grow into?

The leaders on this list are. Their approaches offer lessons for anyone willing to rethink what leadership looks like in the age of AI. Most importantly, we share how their advice molds into actionable ways to use AI as a business executive. 

#1 Co-founder of Coursera on Optimizing Prompts for Agentic Workflows

Andrew Ng, a pioneer in AI and co-founder of Coursera, has been vocal about the shift in how large language models (LLMs) are being optimized. In layman’s terms, one of his goals is to explain that generative AI doesn’t just answer questions but excels in agentic workflows. 

His nuanced tip makes it much more productive and capable in dynamic, iterative tasks.

What Are Agentic Workflows?

Traditionally, LLMs were tuned to provide answers or follow human instructions. They explained concepts and performed straightforward tasks. 

However, agentic workflows go beyond this. They focus on the AI’s ability to think critically, collaborate, use tools, and operate as part of a system. As Ng points out, this shift enables AI to participate in workflows. They reflect on their own outputs, generate plans, and interact with tools or other agents. 

“Agentic workloads call on different behaviors. Rather than directly generating responses for consumers, AI software may use a model in part of an iterative workflow to reflect on its own output, use tools, write plans, and collaborate in a multi-agent setting. Major model makers are increasingly optimizing models to be used in AI agents as well.” — shared Ng on LinkedIn.

Some business executives, sadly, still can’t fully grasp the potential of this fundamental shift in AI — which is why this is an important lesson #1. 

  Agentic workflows are game-changers for executives, developers, and businesses looking to embed AI into more a sophisticated system.

Turning Ng’s Insight into Action

The way you structure prompts matters. To fully leverage agentic capabilities, prompts should be designed to guide the model through complex tasks. Tasks where in one step the model is using tools, while in the other it’s reflecting on its own answers and iterating. 

Here’s a quick rundown of best practices for structuring prompts based on what Ng pointed out:

1. Provide Clear Context: Set the stage for what the model is trying to accomplish.
2. Define the Role and Objective: Specify the AI’s role and the outcome you expect.
3. Outline Explicit Steps: Break down the process so the AI understands the workflow.
4. Incorporate Tool Use: State when and how the model should leverage available tools.
5. Encourage Reflection: Ask the AI to evaluate and refine its responses.
6. Specify Output Format: Make the desired format clear to streamline use.

Ng’s approach reminds us that AI isn’t just about answering questions — it’s about intelligence; workflows instead of trivia; and dynamic and complex prompts rather than simple back and forth. 

#2 Peter Gentsch: AI for Lead Prediction and Profiling

Peter Gentsch, a leading German expert on AI and digital transformation, highlights how AI can revolutionize the process of lead prediction and profiling. 

He explains that AI can analyze vast amounts of data to automate customer profiling and market identification. It does so at an unprecedented scale and complexity. 

As Gentsch notes, “AI enables the automated recognition and profiling of potential customers. For example, new customers and markets can be identified and characterized on the basis of given customer profiles via so-called statistical twins.”

Let’s explore what exactly he means by this, how it works, and how it can be applied.

What is AI-Driven Lead Prediction and Profiling?

AI-driven lead prediction leverages ML algorithms to analyze structured and unstructured data. The goal is to identify patterns that signal a likelihood that a lead will become a customer. 

This process involves the aforementioned “statistical twins.” This is where AI models find the leads that mirror the attributes of existing high-value customers. 

Here’s the gist in simple terms: 

AI uses large amounts of data to create detailed “digital profiles” of companies or individuals. These profiles help AI find new customers who fit within the same box — even if they’re in industries or markets that might not have been obvious targets before. 

Can you see the value this brings to sales-oriented business executives? Anyone operating in the B2B space where sales cycles tend to be costly, lengthy, and resource-dependent may find the following info highly valuable. 

First, here are the key aspects of AI-driven lead prediction:

1. Dynamic Data Analysis: AI processes vast amounts of data, including demographic information, behavioral patterns, and transactional history. This includes structured data, like CRM records, and unstructured data, such as reviews and social media posts.
2. Predictive Analytics: By using predictive analytics, AI models generate insights about when and how to engage with leads. They can suggest communication triggers that improve conversion.
3. Market Segmentation: AI goes beyond static segmentation. It dynamically groups leads based on evolving data points, thus enabling personalized marketing strategies.

Practical Use Case: LinkedIn’s Account Prioritization Engine

LinkedIn’s Account Prioritizer is a pure example of AI in lead prediction and profiling. The engine analyzes sales account data to prioritize leads. It delivers recommendations directly within LinkedIn’s internal CRM. This approach allows sales teams to focus on high-potential accounts and streamline their workflows.

This tool has demonstrated success internally, achieving an 8.08% increase in renewal bookings. This demonstrates how AI-generated insights can directly impact revenue.

While the Account Prioritizer is not available as a feature for users, because of Prioritizer’s success, LinkedIn has incorporated similar functionalities into Sales Navigator. Lead IQ and Account IQ provide concise summaries of leads’ experiences, achievements, and interests, as well as detailed overviews of businesses. They enhance lead generation and engagement with insights into potential clients — still, they are merely a fraction of the full potential.

Actionable Advice for Executives

To implement AI-powered lead prediction and profiling effectively, follow these steps:

1. Nudge Your Teams to Build a Robust Data Infrastructure: Collect and integrate data from diverse sources such as CRMs, social media, and customer feedback channels. As Gentsch emphasizes, “Data is the fuel for the AI machine.”
2. Start with a Pilot Program: Test AI-driven profiling in a specific market or customer segment to evaluate its effectiveness before scaling up.
3. Leverage Statistical Twins: Use machine learning to identify patterns among your most valuable customers and replicate these profiles to find untapped opportunities.
4. Refine Models Continuously: AI models improve with regular updates and retraining. Incorporate new data, such as customer feedback and market trends, to maintain accuracy.
5. Integrate with Sales and Marketing: Ensure alignment between AI insights and your sales team’s workflows. Provide training so teams can interpret and act on AI-driven recommendations effectively.
6. Focus on Context-Specific Communication: AI insights can guide personalized outreach, increasing the likelihood of conversions by tailoring messaging to customer needs and behaviors.

#3 Allie K. Miller: Interactive Data Simulations as the Future of Business Insights

“Claude Artifacts continues to be one of my favorite AI releases the entire year. The ability to quickly create interactive data simulations is one thing. The future ability to be able to bake assumptions, values, and goals into AI models and keep them flexible, can’t wait.” — consider Allie’s words a good starting point.

Interactive data simulations are becoming an essential tool for executives. Every business leader probably prefers making data-driven decisions. This is how they step it up and get a buy-in from the rest of the leadership. 

According to a global survey by Salesforce, 80% of leaders consider data critical in decision-making. These types of tools bring the ability to turn that data into decision-ready simulations.

What Are Interactive Data Simulations?

Interactive data simulations are like a virtual sandbox for business data. 

Imagine you’re running a city. You want to see what happens if you add a new road or change how buses run. In this sandbox, a 3D map let’s say, you can tweak things — add houses, move roads, or change the weather — and instantly see how it affects traffic, businesses, or neighborhoods.

For businesses, it works the same way but with real-world data. You take numbers and facts — sales, costs, or customer trends — and use the simulation to test different ideas. 

“What if we lower prices?” or “What happens if demand suddenly spikes?”, it can all be put up to a test. The simulation shows you possible outcomes in real-time and helps you make smarter decisions without having to guess.

Unlike static dashboards, these simulations allow business leaders to explore multiple paths before they take any steps.

Tools and Platforms You Can Use

Several tools now offer capabilities for interactive data simulations. They each cater to different business needs:

• Claude Artifacts: Enables users to simulate data trends and create interactive reports with ease. For example, a leader can upload historical sales data and simulate various pricing strategies to assess revenue impact.
• Power BI (Microsoft): Provides advanced visualization capabilities. It’s particularly effective for enterprise-wide reporting and scenario analysis.
• Tableau with Einstein Discovery (Salesforce): Combines Tableau’s visualization power with Salesforce’s AI. It enables predictive modeling and dynamic simulations.
• Google Looker: Offers interactive dashboards and simulations by connecting directly to datasets. It’s ideal for tracking real-time metrics and experimenting with market scenarios.

Here are key use cases where interactive simulations offer immense value:

• Financial Planning and Forecasting: CFOs can model various scenarios, such as changes in interest rates or operating costs, to predict their impact on cash flow and profitability. This allows them to test different strategies dynamically, improving confidence in their financial decisions.
• Market Expansion Decisions: CEOs can simulate new market entries by integrating data like labor costs, tax rates, and consumer demand. Adjusting variables in real-time helps evaluate the viability of entering high-growth regions or sectors.
• Mergers and Acquisitions: Executives can use simulations to assess potential synergies, integration costs, and market share impacts. This can provide a clearer picture of risks and rewards when considering mergers or acquisitions.

To begin using interactive simulations, identify key decisions where dynamic modeling adds value, such as financial planning or market entry. Choose a tool that integrates easily with your current data systems, prepare clean datasets, and start small with pilot scenarios. Then, as your familiarity grows, you can slowly expand the use of simulations.

Leading Like It’s Tomorrow, Not Yesterday

In the end, the real measure of leadership in the age of AI is whether you use them well enough to change the way your organization thinks, decides, and grows.

Ng’s agentic workflows show us that AI is a thinking partner. Gentsch’s lead prediction proves it can find opportunity before the market does. Miller’s interactive simulations remind us that the best decisions are rehearsals.

The future will belong to leaders who can connect these dots, not just collect them. Those who treat AI as a lever for culture, not a bolt-on gadget. And who understand that in a world moving at machine speed, the only real risk is leading like it’s still yesterday.