Selling with Data #88 - Fear of Messing Up

Fear of Missing Out (FOMO) is the anxious feeling people experience when they think others are having fun without them. My teenage kids have big time FOMO.

I have a good buddy who hates going out. We joke that he has JOMO – the Joy of Missing Out. He prefers staying home. We have grown to appreciate that he has JOMO.

When it comes to deploying AI, many leaders at enterprise companies are facing something similar. They have FOMU – the Fear of Messing Up.

For many, the conceptual benefits of AI are obvious: more revenue, reduced cost, faster processes. Still, while 98% of companies have completed an AI pilot only 25% have put AI into production.

From my experience, there are four reasons why.

1) Fear of leaking proprietary or private data. Companies have spent the last 20 years locking down their data with security and governance tools. AI technics, like RAG, make pointing to a simple .pdf document and running Question and Answer seem simple, because it is. What happens when you apply the same approach to your entire document management repository – some sensitive and some harmless? What user authentication controls are in place to ensure people can only view the information and data that they are allowed to see? AI project and pilots look like they are going well until someone uncovers the CEO's annual salary. An equal concern is leaking proprietary data to the public cloud because an employee was using a consumer grade model without the appropriate controls and governance. No one wants to be responsible for a data leak or security breach.

2) Fear of run-away costs. Running several hundred models on the public internet to prove out a potential use case is easy, and the cost is easy to contain. What happens if an AI app goes viral and thousands of  employees or customers quickly start using the application and the expenses spike? It is easy to know at what stage a client is on their journey by asking if they hit the "oh sh*t" moment of AI. That is the moment when an AI application goes viral, and the monthly cloud costs unexpectedly surge to several million dollars. No one wants to have to explain a million dollar plus surprise cloud bill to their boss.

3) Lack of AI skills and dealing with the tax of technical debt. A common issue for many large regulated and public companies is that many are so busy managing the technical debt of legacy applications that freeing up time or expense to innovate with AI is a challenge. While many companies would like to take AI to production, the skilled resources are focused on mission critical long term transformational projects. Most companies have access to AI skilled people, but typically they are still in innovation or incubation centers working on cool projects but not deeply integrated with revenue producing functions to identify high-impact focused projects.

4) Unsure where to start. The paradox of choice happens when people are unable to make a decision because they are overwhelmed by too many choices, leading to inaction or anxiety about choosing the right option. Some people feel that the first AI project will set the tone for the organization and so they wait for perfect at the cost of progress. What if they pick the wrong AI use case to start with and it fails?

These reasons, plus others, create a scary situation for leaders and cause FOMU. After all, companies do not buy products, people in companies buy products.

Contrary to common belief, people don't make decisions solely based on what is best for their companies. Instead, they are motivated by the positive impact being associated with solving the problem has to their career or compensation. Most people are risk adverse, so unless they see personal upside, won’t take the leap. They experience FOMU!

Here are some tips to help overcome FOMU.

Tip 1 - "A confused mind always says no." When a potential customer is unclear about what a product or service offers, or if the sales pitch is too complicated, they are more likely to decline to move forward because they don't fully understand the value proposition and feel hesitant to commit. Few people are comfortable admitting they do not understand AI or how it works. Instead of admitting their confusion, they offer a diversion. The best remedy is to slow down and help them cross their mental chasm. Be a strategic partner and invest the time and resources to build out an AI application using their data in their environment to demonstrate you can help overcome their concerns.

Tip 2 - "People do not move when they see the light, they move when they feel the heat." Just because AI is the latest technology is not enough. AI needs a meaningful use case to unlock business value that offsets the risk of failure. If the existing approach to human resources is working, for example, many leaders may be reluctant to change regardless of the potential opportunity. But, if their leadership is demanding a solution to increase volume while reducing HR operating expenses – and they are out of ideas for solutions but know they need to suggest something lest they they be fired – they may find themselves motivated to move not because of the light of AI but because of the heat! If there is a burning platform that is worthy of the investment, the risk becomes relative to the value. The risk of deploying the AI project is lower than the alternative risk to their career.

Tip 3 - "Pioneers gets scalped and settlers get rich." Being first isn't always best. There are high rewards because there are high risks. Being second has its benefits because there is a playbook to follow and lessons learned to manage the risk.

Tip 4 - "Experience is the best teacher. The only thing better than learning from your own mistakes is learning from someone else's mistakes." AI projects at scale and for enterprises are hard. Just like any learned skill, there are multiple errors that aren't obvious until they are experienced. Seeking out and working with an experienced partner who has done similar projects in peer companies is one of the best ways to mitigate risk. As the saying goes: smart knows how to get through a problem, but experienced and wise knows how to avoid the problem all together. The best way to de-risk an AI project is to partner with an expert in the industry who has done a similar project before and include professional services and training / learning to up-skill the team.

Please leave a comment with any examples of FOMU you have seen and tips you have to help companies overcome their fears and capture the AI opportunity.

Good selling.