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7 Steps for Crafting an Effective AI Implementation Strategy

By 08.23.23
Reading time: 4 minutes

September 19, 2016 is a significant date in Salesforce history. That’s when Salesforce Einstein was launched and brought the power of artificial intelligence to every Salesforce user. Skip to almost seven years later, and today Einstein runs more than 1 trillion predictions per week. Plus Salesforce has recently added even more AI capabilities with its newest AI Cloud and Einstein GPT additions.

The rapid rise of AI for Salesforce and other business areas can sometimes make it challenging to keep up with why AI is important, the opportunities it provides, and how it can help you be more efficient in your job. Here we share how generative AI is shaping the Salesforce ecosystem and how you can best maximize its potential at your organization.

What is generative AI?

Generative AI is a family of deep learning algorithms that can automatically create new content such as text, imagery, code, voice, and even video. AI algorithms learn patterns and features from massive amounts of data in a system to generate this content. The most well-known generative AI tools are ChatGPT and Google Bard.

Generative AI is built upon large language models (also called foundational models), which are essentially vast neural networks. These neural networks include billions of parameters of tiny, interconnected decision-making elements that collectively give rise to their AI capabilities.  

You may have heard other AI-related terms, such as:

  • AI: The concept of having machines “think like humans,” such as performing tasks like reasoning, planning, learning, and decision-making. 
  • Machine learning: Leverages data to generate predictions. The more data we feed into a machine learning system, the better it can predict.
  • Deep learning: Uses complex algorithms (also known as neural networks) to learn and perform tasks without explicitly programmed step-by-step instructions.

Deep learning is a type of machine learning, and generative AI is a type of deep learning. While these terms are sometimes used interchangeably, they are considered to be distinct types of AI.

Why is generative AI having an inflection point now? 

Many of the roots and interactions of generative AI have been a part of our lives for quite some time. For instance, consider how a search engine knows exactly what you’re going to be typing in and completes the search for you.

There are three main contributing factors as to why generative AI is in the spotlight now:

  1. More data: Availability of large amounts of data
  2. Better algorithms: Algorithms that can exploit and discover patterns in these large amounts of data
  3. Compute power: Large amounts of available compute power that make it possible to run and process the sheer amounts of data required for generative AI

The rise of generative AI is creating opportunities for businesses. Salesforce’s Generative AI in IT Survey reveals that 67% of IT leaders are prioritizing generative AI for their business within the next six months. Many organizations are excited about the potential of generative AI to drive efficiency and productivity. But even with all the focus on how generative AI can transform businesses, it’s important to remember that AI is not perfect. It’s critical to innovate responsibly with generative AI by following these principles:

  • Trust: Make it clear when content is AI-generated and follow company guidelines around usage
  • Security: Protect personally identifying information and proprietary company information
  • Accuracy: Review and revise generated content to ensure accuracy
  • Empowerment: Use AI as an assistant to supercharge your capabilities

Address the common concerns about generative AI that can lead to risks for your business. Generative AI is a form of prediction, and sometimes those predictions generate incorrect responses known as hallucinations. Plagiarism can be an issue since AI models are typically trained on publicly available data, and there is the possibility that the model will learn a style and replicate it.  

7 steps for developing an AI strategy

At Silverline, we often see organizations need help knowing where to start with integrating AI into their business. We recommend following these seven steps to develop an effective AI strategy:

1. Executive AI proficiency

AI will only get off the ground at a company if the executives get it. They don’t have to be experts, but they must understand how AI projects align with the organization’s vision and provide the resources to tackle the projects effectively.

2. Define your organization’s current priorities and vision 

Think about the organization’s current business strategy and its long-term company priorities. The AI strategy should not lead the organization’s path but inform and accelerate it. You should have a consistent, succinct reference to existing priorities and a vision to anchor all your AI conversations.

3. Develop an AI transformation vision

You can’t complete your AI transformation vision until you have a strong understanding of your digital transformation objectives. You can then refine that vision to include AI. An essential AI transformation vision should consist of:

  • What role will AI play in a sustained competitive advantage in the market
  • The approach the company will employ to take advantage of proprietary data streams and sources
  • Specific areas where AI is likely to deliver value in the near and mid-term, including customer experience and core business functions

4. Evaluate AI maturity and readiness

The three parts of evaluating your AI maturity and readiness for successful AI adoption include:

  • Skills:  AI models cannot be built by one person and require a team with a conceptual understanding of AI and its use cases 
  • Resources: The data must be assessed to ensure it is accessible, quality, and mature so that you know you can trust it
  • Culture: Your teams must be willing to experiment with AI’s requirements and challenges and iterate as needed

5. Match AI opportunities with organizational challenges

The next step is to match AI opportunities such as machine learning, natural language processing, or time series analysis with your organization’s challenges. For example, suppose you are trying to enhance a bank’s customer recommendations on the mobile app. In that case, you can use AI to personalize product and service recommendations based on the customer’s behaviors and transaction history. 

6. Rank and score AI opportunities

You are now going to rank and score each of your AI opportunities to come up with an AI Score and a Trust Score. 

Your AI score looks at:

  • Alignment of digitalization vision
  • Data availability
  • Deployment ease
  • Economic benefit

Your trust score looks at:

  • Accountability
  • Explainability
  • Data quality
  • Bias and fairness
  • Robustness

Give each of your AI and trust items a score of one to three. Add up your AI Scores and your Trust Scores. Multiply your AI Score by your Trust Score to get a final Trusted AI Score. 

7. Finalize a shortlist of opportunities and benchmarks

Create a short list of your AI opportunities that you can easily reference. Each opportunity should include the Trusted AI Score, a completion timeline, and the project team. 

Find out how Silverline can help your organization leverage generative AI.

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