Four ways to ensure effective deployment of generative AI

Generative AI is top of mind for the C-Suite. Business leaders understand the immense gains in productivity, efficiency and customer experience that the technology can bring.

The potential use cases for Large Language Models (LLMs) are endless across virtually every team and industry. It’s no wonder that nearly 70% of IT leaders say they are making generative AI a priority over the next 18 months.

Despite its growing popularity, most companies aren’t sure how to implement generative AI. What’s more, a majority of employees say they lack the skills to use it safely and effectively. To reap the benefits of the technology, businesses need the right strategies in place to manage data, up-skill their workforce and build a foundation of trust.

Laying the foundation for generative AI adoption

Here are four steps companies should take to get the most out of their generative AI journey:

1. Prioritize trust

Trust is paramount to making generative AI successful. A majority of IT leaders are concerned that the technology can produce inaccurate or biassed content, and others are worried about security risks or the impact on jobs. One way to mitigate these risks is to train generative AI models on data that the company produces and owns. This ensures inputs meet standards of quality and reliability.

It’s also critical to incorporate a human element when implementing generative AI to produce outputs that are accurate, secure and unbiased. Getting trust wrong can do significant damage. Implementing AI ethically and safely takes time. AI adoption is a technical evolution, not a revolution.

2. Define a data strategy

For years, companies have faced the challenge of unifying data across silos to establish a cohesive view of the customer. Part of the problem is technical, and the other is operational—many companies work as federations, with various branches acting largely independently. With the advent of generative AI, addressing this issue has become even more urgent.

Before implementing generative AI for CRM, companies need a solid data strategy in place. That starts with understanding where data is located, what information needs to be surfaced, what problems the business aims to solve with generative AI and what outcomes it hopes to achieve.

Next, businesses should ensure all data sources are connected and accessible in one place, all departments follow standard procedures and data is managed in accordance with relevant regulations.

3. Determine what you want to achieve with AI

Generative AI provides endless opportunities for business leaders, though it's important to be prescriptive about what you want to achieve and accomplish and set measurable and attainable goals. A benchmarking process will allow you to determine the specific areas in which the tech is having the most impact and where there is still room for improvement. This will also help inform your implementation strategy so that your company can make adjustments and changes in real time as needed.

4. Build the right skills

When deployed effectively, generative AI has the power to transform how companies do business and free employees’ time to focus on more complex and creative work. In order to reap these benefits, businesses need people with deep AI knowledge and expertise in data science, enterprise strategy and enterprise architecture in order to shape the way they implement and use the technology.

Although the majority of global business leaders report that their company is looking for ways to use generative AI, more than 60% of global workers say they lack the skills to safely and effectively use the tech – signalling a massive and pervasive skills gap. Free online learning platforms like Trailhead can help workers learn the in-demand skills they need to work with generative AI for CRM.

The power of knowledge sharing

With generative AI evolving rapidly, it can be hard for companies to implement the tech safely and effectively on their own. Collaboration with peers and guidance from trusted experts is critical for businesses that want to make progress quickly in making generative AI for CRM a reality across the enterprise and driving real business outcomes. 

Salesforce’s Partner Ecosystem, which consists of about 12,000 consulting partners and independent software vendors, is one example of a network that’s filling this gap. By working with companies to deploy generative AI for CRM, the ecosystem is disseminating lessons learned and collaborating with companies to ensure they have the strategies and resources in place to innovate with AI.