Artificial intelligence (AI) is rapidly transforming the business landscape, with the potential to dramatically improve efficiency, lower costs, and drive revenue growth. In fact, 80% of business leaders believe generative AI will deliver these benefits. However, realizing AI's full potential requires overcoming a critical challenge: establishing trust.

A recent Salesforce survey of nearly 6,000 global knowledge workers revealed that AI currently faces a significant trust gap. 56% of AI users say it's difficult to get what they want out of the technology, and over half don't trust the data used to train AI systems. Clearly, for AI to deliver maximum value, organizations must prioritize building trust.

The Data-Trust Connection

At the heart of the AI trust challenge is data. The Salesforce research shows a direct link between confidence in AI training data and perceptions of the technology's usefulness and trustworthiness:

• 75% of workers who don't trust AI training data also believe AI lacks the information needed to be useful
• 68% of workers who don't trust AI training data are hesitant to adopt the technology
• 62% say out-of-date public data would break their trust in AI
• 71% say consistently inaccurate outputs would erode their trust

The message is clear: high-quality, reliable data is essential for AI to be embraced and used to its full potential. When AI is trained on an organization's trusted data, it produces more relevant, useful results that ultimately foster greater confidence and adoption.

As Salesforce Chief Data Officer Wendy Batchelder explains, "The future of enterprise AI isn't about more data - it's about the right data. When AI is grounded in a company's data, it delivers more useful results and ultimately drives greater trust and adoption."

Establishing the Right Data Foundation

So, how can organizations ensure their AI initiatives are built on a solid foundation of trusted data? Here are a few key strategies:

1. Prioritize data quality and governance
   Establishing rigorous processes and standards for collecting, storing, and managing data is critical. This includes validating data accuracy, ensuring data is up-to-date, and protecting sensitive information. Investing in data quality upfront prevents costly errors and rebuilds trust down the line.
2. Leverage your own proprietary data
   While large public datasets can be useful for certain applications, an organization's first-party data is often the most valuable and relevant. Tapping into the unique insights contained in your CRM, marketing automation platform, customer service logs, and other internal systems allows you to train AI on data specific to your business and customer base.
3. Implement strict data security measures
   Safeguarding the data used to train AI is paramount for maintaining trust. This means employing encryption, access controls, secure APIs, and other protective measures. Being transparent about your data security practices can also boost confidence among both internal and external stakeholders.
4. Regularly monitor and refine your data
   Data is always evolving, so it's important to continuously assess and improve the information used to train AI. Establishing processes for refreshing data, identifying and correcting errors, and incorporating new sources keeps your AI current and aligned with business realities. Proactively communicating about data updates also assures users that the AI is leveraging the latest and most accurate information.

The Salesforce Advantage

As a Salesforce implementation partner, we have the privilege of working with a platform that has data quality and security at its core. Salesforce's robust data management capabilities, including advanced validation tools, duplicate prevention, and backup and recovery features, establish a strong foundation for trusted AI.

Additionally, Salesforce Einstein, the platform's AI technology, is pre-trained on aggregated and anonymized data from millions of global Salesforce users. This massive corpus of CRM data provides a solid baseline for Einstein's predictions and recommendations.

However, the real power comes from fine-tuning Einstein with each organization's own Salesforce data. By learning from the unique entities, processes, and KPIs captured in a company's Salesforce instance, Einstein delivers highly customized AI that is grounded in that organization's specific business context and goals.

This combination of a strong data foundation and bespoke AI training enables Salesforce customers to unlock significant value across sales, service, marketing, commerce, and more.

A few examples:

• Sales reps close deals faster with Einstein Lead Scoring and Opportunity Scoring, which predict conversion likelihood based on each company's sales data.
• Service agents resolve cases more efficiently using Einstein Article Recommendations and Einstein Reply Recommendations, which suggest relevant knowledge articles and responses based on past case data.
• Marketers improve campaign ROI with Einstein Engagement Scoring, which predicts how likely each customer is to engage based on the company's unique engagement data.

In each case, the use of trusted, company-specific data makes the AI so impactful. Users can have confidence in the insights and recommendations because they are grounded in their own business realities and customer relationships.

Looking Ahead

As AI continues to advance and adoption grows, the ability to establish trust will only become more critical. While technical innovations like explainable AI and algorithmic transparency will certainly play a role, the Salesforce research underscores that trust fundamentally begins with data.

Organizations that invest now in the people, processes, and technologies needed to ensure data quality, security, and relevance will be well-positioned to harness AI's full potential. They will be able to deploy AI solutions that users can inherently trust - solutions that consistently deliver accurate, meaningful, and valuable outcomes.

We've seen firsthand how the Salesforce platform empowers companies to build this type of trusted AI foundation. As an implementation partner, our role is to help clients maximize Salesforce's extensive capabilities to establish data integrity and to thoughtfully leverage that data to train customized AI that drives real business results.

The future of AI is exciting, and with the right data foundation, it is within reach for every organization. By keeping trusted data at the center of your AI strategy, you can not only navigate the trust gap but leap ahead of it - and unlock a new realm of intelligence-driven innovation and growth.

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