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Trust and AI: The Key to Driving Adoption and Unlocking Value

Ashley Reichheld, best-selling author and authority on building trust, shares insights on how organizations can maximize the value of their AI investments by earning trust with their workforce and customers.

Artificial Intelligence promises immense potential value for companies hoping to boost process efficiency, reduce costs, accelerate innovation, and grow. Companies around the world have gotten the memo; in 2023 alone, global investment in AI was $300 billion.[1] Despite this hefty sum, 80 percent of AI projects fail—almost double the failure rate of corporate IT projects a decade ago—according to recent research from Harvard Business Review.[2]

Why? Our research suggests that investments are failing due to a lack of trust.

Let’s break it down. Successful deployment of AI requires two main ingredients: 1) trustworthy machines and 2) trusting humans to use those machines to generate value and unlock efficiencies.

At this point, you might be asking: how do we even define trust? At Deloitte, we have developed TrustID, a science-based platform grounded in over 30 million data points, to understand what drives trust and how to build it. TrustID is based on research from more than three-hundred fifty thousand customers and workers over the past few years.  At its core, trust is built when organizations make good promises, and when they deliver on those promises. We call these, respectively, intent and competence. We demonstrate intent through being transparent and human. We demonstrate competence by being capable and reliable. These are the Four Factors of Trust that comprise the TrustID:

  • Humanity: The company/brand demonstrates empathy and kindness toward me and treats everyone fairly.
  • Transparency: The company/brand openly shares information, motives, and choices in straightforward and plain language.
  • Capability: The company/brand creates quality products, services, and/or experiences.
  • Reliability: The company/brand consistently and dependably delivers on its promises.

When it comes to the machines, a key to trustworthiness is putting in place ethical safeguards. Specifically, AI solutions must be transparent, unbiased, accountable, responsible, reliable, secure, and private.[3] Arguably even more critical to the success or failure of AI is the trust of the humans who must adopt the new technology for the organization to realize its benefits. While eventually end customers will use AI when interacting with a brand, employees are likely to be earlier users, given the need to sense check AI outputs. What we’ve found is the prevailing trust in your brand — both from customers and employees — is a leading indicator for how much trust your AI investments will generate, and therefore, how likely adoption of your AI solutions will be.[4]

Our research shows that customers and employees have an inherent lack of trust in AI, meaning that introducing AI causes declines in customer and employee trust across the board. However, trust leaders (brands that have high baseline trust levels) experience these declines much less severely than trust laggards (those with lower baseline trust). When a trusted brand introduces AI, customer trust only declines by 30 percent, and workforce trust declines by 51 percent. In contrast, when a brand with lagging trust introduces AI, customer trust declines by 80 percent and workforce trust by a whopping 149%.[5]

Now we can translate these findings to the likelihood of adoption of AI tools. If a brand is highly trusted, customers are 1.9 times more likely to engage with AI it introduces. Similarly, workers at trusted brands are 2.6 times more likely to report feeling comfortable using AI tools offered by their employer.[6]

Here are three ways you can use trust to unlock the value of AI investments:

1. Prioritizing AI Investments

Organizations should evaluate where in the customer or worker journey they have the most trust. This can be incorporated into existing measurement, as a standalone measurement or for companies with advanced capabilities, through predicting trust scores. This will help to guide where to prioritize AI investments, as this aspect of the customer or employee experience presents higher likelihood of adoption and buy-in to AI’s value. For example, if your brand is known for honest consumer reviews, you could use AI to curate feedback, summarizing insights real-time across multiple reviews, simplifying the evaluation process for shoppers. Whereas if you have low trust scores for customer service, it may be trickier to drive adoption of chatbots for warranty or product issues.

2. Designing With Trust

At this nascent stage of the technology, it’s easy to get bogged down in tech-heavy assessment of the machine’s function and under-invest in demonstrating the value of AI to the humans in your ecosystem. Instead, use the Four Factors of Trust to inform an iterative, human-centered design approach. For example, let’s say you are working to build a virtual sales assistant for your employees to boost their productivity and improve performance. You might highlight how the virtual assistant will speed up daily tasks to remove the more menial or mundane parts of the job, boosting the Humanity factor.

3. Driving Adoption

Your AI is only as valuable as the level of adoption. If your customers or employees refuse to engage with your AI, then you may not realize the potential value of the investment. Organizations should survey the Four Factors of Trust to understand cultural readiness and to identify actionable insights on where trust needs to be reinforced. For example, as you roll out the virtual assistant from the example above, you might also supply an analog view of historical sales data and share this for employees to use as way to check the reliability of the tool. And don’t stop with the launch! Continue to track trust scores to understand what might be driving or eroding engagement and adjust accordingly.

In summary, trust is key to unlocking AI value, and to build trust organizations should take a human-first approach, from prioritization through to design and implementation.

[1] Statista: Artificial Intelligence Worldwide 2024

[2] Harvard Business Review “Keep Your AI Projects on Track”

[3] Beena Ammanath, Trustworthy AI: A Business Guide for Navigating Trust and Ethics in AI, 1st edition (NY, Wiley, 2022)

[4] Based on the Deloitte TrustID Brand Index study of 60,000 customer responses across 150 brands conducted in January 2024

[5] Ibid

[6] Ibid

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