How to Make Smart Decisions When You Don’t Have All the Facts, with Marili’t Hooft Bolle

Ever had to make a high-stakes decision without all the facts? It’s like trying to solve a puzzle with missing pieces. In today’s fast-paced world, especially with the rapid evolution of artificial intelligence, making the right call can feel even more daunting. Marili’t Hooft Bolle, CEO of Trengo, dives deep into this challenge, revealing her strategies for navigating imperfect data and why embracing uncertainty might just be the secret to success.

The landscape of decision-making

Marili’t has over 20 years of experience in technology and is passionate about innovation. She emphasizes that the industry demands swift decisions, often based on imperfect data. As she puts it, “Too many people are making too many problems; this is the land of confusion.” In the realm of AI, this confusion can be compounded, leading to challenges in departments and Centers of Excellence. Whether determining a budget for a holiday party or strategizing for product features, decision-making remains constant.

Situational decision-making

There’s 3 various scenarios that illustrate the complexity of decision-making:

1. Hiring: Bringing on a new team member, especially a senior hire for a startup, may seem straightforward. With interviews, colleague notes, and reference checks, it feels like a data-rich decision.
2. Office space: Deciding to move to a larger space is more complicated. Considerations like lease terms and hybrid policies can cloud the decision-making process, especially regarding future hiring.
3. Product development: Take the automotive industry’s move toward self-driving cars. Manufacturers are grappling with long-term questions, like whether these vehicles need steering wheels. The absence of clear regulations only adds to the uncertainty.

Embracing uncertainty in AI pricing

Marili’t delves into Trengo’s approach to integrating AI into their pricing model. The company has rolled out numerous AI features for free but now faces the challenge of determining how to price these offerings amid constant industry changes. The dataset they currently have is limited, raising questions about how accurately they can set prices. In a competitive landscape where others also struggle, the complexity of establishing fair pricing can be daunting.

Making decisions: The importance of action

Making a decision is better than no decision. However, many find themselves paralyzed, leading to implicit decisions that are often less effective. For Trengo, offering AI features for free has prompted customer inquiries about future costs. Establishing clear criteria for decision-making has become essential.

Establishing Criteria for Pricing

To navigate these challenges, there are several key criteria to have in mind:

1. Simplicity: The pricing structure needs to be straightforward, aligning with what customers are already familiar with.
2. Retention: Pricing must encourage long-term retention, providing enough value to prevent users from reverting to free versions.
3. Competitive: Staying competitive in a fluctuating market is crucial.
4. Long-term viability: Pricing should be stable and appealing enough for customers to commit long-term.
5. Exceptions: The criteria should avoid convoluted conditions that complicate the pricing model.

Rational analysis vs. gut feeling

In decision-making, Marili’t urges leaders not to shy away from gut feelings. Intuition often combines insights from customer interactions, market reports, and industry trends. While data analysis is valuable, she cautions against over-reliance on spreadsheets, noting that more analysis does not necessarily yield clarity. Instead, leaders should focus on understanding the main drivers behind their decisions.

Sensitivity analysis: Understanding the variables

Sensitivity analysis is crucial for effective decision-making, requiring consideration of the following main drivers:

• Automation potential: Understanding what percentage of customer interactions can be automated.
• Cost variability: The costs associated with using AI technologies can vary significantly across different interactions.
• Surcharge implications: Leaders should explore how varying surcharge amounts could impact their pricing model.

Committing to decisions

One common struggle for founders is the tendency to backtrack too soon. Marili’t encourages commitment to a chosen strategy, using the analogy of sports. Once a path is selected, it’s vital to give it your all instead of jumping between options based on immediate market responses.

Building flexibility into decisions

Flexibility is crucial in today’s dynamic environment. Marili’t suggests considering various scenarios when making decisions, such as how to respond if AI engagement drops significantly. Anticipating these shifts allows for better-prepared responses rather than scrambling for solutions at the last minute.

Pricing propositions

Trengo has developed a pricing model that includes:

• Bundled basic AI automation: A specific volume of AI interactions included in the package.
• Usage-based surcharges: Additional costs for exceeding the bundled volume.

This approach is comparable to assembling a structure with lego blocks, where each decision incrementally enhances the product’s overall value.

Key takeaways

In summary, Marili’t Hooft Bolle provides valuable insights for navigating decision-making in the age of AI:

1. Make a decision is better than no decision: Taking action is essential.
2. Rational vs. gut feeling: Balance data analysis with intuition.
3. Don’t backtrack too soon: Commit to your choices and see them through.
4. Build in flexibility: Prepare for various outcomes and adjust accordingly.

As companies like Trengo forge ahead in the uncertain landscape of AI, these principles will guide them in making informed, impactful decisions.