4 Ways To Build Team Trust in Artificial Intelligence (AI)

I’ve seen it time and time again. Organizations add AI-based predictions and recommendations to their sales processes, only to find reluctant teams that don’t have trust in artificial intelligence (AI). And when users don’t trust, they won’t heed a new recommendation or process, and they won’t take action. Even worse, new and misaligned users may dismiss or discount the value of subsequent predictions.

Trust is the foundation of leadership. With 92% of companies accelerating their investment in artificial intelligence (AI), IT leaders need to make sure that teams have trust in AI as a critical step toward digital transformation. We’ll teach you four key steps to lead teams to see AI’s capabilities, processes, and, most importantly benefit, rather than threat, to their jobs.

How to get your teams to trust in AI

To prepare your teams for digital transformation and to help foster their trust in AI, the first step is to define your strategy and ask how AI will impact business processes:

• Are you embedding AI in existing systems and workflows? 
• Do those workflows need to change? 
• Are you creating entirely new end-user workflows? 

Your answers will guide the steps to educate, train, and support your employees as you introduce new tech intelligence to their day-to-day tasks.

1. To build trust in AI, invest in end-user education 

Educate your executives and teams on what you’re doing and the fundamentals of artificial intelligence. Your end users need a clear understanding of how AI will benefit them. All too often, I see organizations overlook this step in the flurry of excitement that AI can generate. And the resulting decisions on priorities and budgets can be disastrous. Research shows that lack of skills and onboarding is one of the top hold-ups for successfully implementing AI.

Teams also need an understanding of the different applications of AI. For example, if you provide an email marketing manager with predictions on audience engagement (e.g. response rates, click rates, unsubscribe rates) and guidance on actions to improve engagement, it will likely enhance – rather than replace – existing processes.

2. Provide context and transparency around AI predictions

To build user trust, always provide transparency around how the machine arrived at a prediction. Show users the top predictive factors in your model that led to the prediction. Strike a balance between explaining the prediction and drowning the end user in excessive detail or surfacing obscure, machine-generated factors. Less is more. Keep it simple, but thorough.

For example, you may have a sales manager accustomed to Excel for forecasting. If you add a predictive forecast into their workflow without explaining how the machine arrived at that conclusion, that’s a significant shift. Suddenly, machine learning is giving them information on top of what they already know about the pipeline. A deeper understanding into how AI functions will help them to trust the prediction.

3. Explain that AI informs human logic

Make certain that teams understand that AI provides context for people to execute human logic, and that they’re not just passive observers to machine logic. AI-powered predictions inform users of the best decision to enhance an outcome, and tell users how likely it is that the decision will affect the desired outcome. Data pulled from AI can help teams decide, for example, if a decision is going to be expensive but only increase the likelihood of closing a deal by 2%. For most organizations, that’s not a good choice. But if that same decision increases the likelihood of deal closure by 15%, it could be the right move.

Build trust with business users by showing them AI gives them insight, not mandates. For example, let a salesperson know that AI can predict the expected impact of a discount tier, but ultimately they have the power to decide how to proceed. By showing AI as multi-faceted for predicting results, your team will feel empowered to generate creative use cases.

4. Create continuous feedback for engagement and improvement

Predictions are probabilities, and markets fluctuate. To build trust in AI for your teams, help them feel more engaged in the process with an easy mechanism to give feedback on predictions. Between the predictions and the actual use cases, you’ll have a hybrid of datasets to help you improve model accuracy moving forward.

Executives recognize the competitive necessity for investment in AI. Companies that neglect to see the business imperative will likely lag behind as pioneers race ahead. Luckily, AI can be  easily implemented – especially if you build a foundation of trust in AI for those in your organization who use it most.