The Foundation of Every Successful AI Strategy is a Strong Data Strategy: Here’s Why

It’s official, AI(Artificial Intelligence) is taking over. It’s in virtually every business today, influencing everything we do, from processing information, to serving customers with chatbots, and even creating marketing content. A study by Gartner even found that 79% of corporate strategies think AI will be essential to their success in the following years.

There’s certainly cause for excitement around artificial intelligence. Used correctly, it can help you unlock hidden insights, automate routine tasks, increase sales, and boost customer loyalty. Unfortunately, in all the hype, a lot of companies are overlooking the foundations of a strong AI strategy – a good data strategy.

Data and AI are intrinsically connected. If your data strategy is problematic, then your AI solutions are more likely to suffer from everything from dreaded“hallucinations” to everyday inaccuracies. Here’s why no company should dive into the AI revolution, without first paving the way with a solid data strategy.

AI and Data: The Need for Strong DataFoundations

Everyone is keen to jump on the AI bandwagon right now. Promises that AI will help you increase revenue and boost productivity are difficult to ignore. However, research shows that the highest-performing companies implementing AI, are the ones with a clearly defined strategy.

Building a comprehensive AI strategy doesn’t just mean defining what kinds of intelligence you’re going to implement into your business, or which vendors you’ll use. It means thinking about the lifeblood of your AI solution: your data.

Data is what powers artificial intelligence. It’s what allows your generative AI bots (likeChatGPT) to predict the next word in a sentence, and create unique content.It’s what allows your analytical tools to surface information about patterns, trends, and opportunities in your go-to-market strategy.

Effective AI tools don’t just need huge volumes of data, they need access to quality data, to ensure they can deliver accurate, consistent results. If you don’t have standardized, easily-accessible, and well-integrated data in your business, then your AI model won’t perform. After all, no matter how intelligent the latest tools likeGoogle Bard might seem, they can’t think for themselves.

AI models can only produce responses based on the information they have. That means if you train an AI chatbot on discussions with customers that only cover returns and refunds, it can’t magically answer a question about delivery times too. It might produce confident answers, to queries, but they won’t be accurate, or even relevant in some cases.

Poor data strategies also hamper AI automation strategies. If you want an AI tool to draw insights from your marketing and sales reports, you need to teach it how to do that, otherwise you’ll end up with confusing results (or nothing at all).

Data Strategies are Crucial toUnlocking AI’s Potential

While many companies know data is the fuel behind artificial intelligence, it’s easy to overlook the importance of having your own data strategy in place. These days, companies are constantly producing pre-built AI solutions that promise exceptional functionality out of the box.

While these tools might be fine for basic, generic tasks, they’re limited in their scope. If you want AI to really deliver results specific to your business, then you need to enhance your technology with your own data, processes, and technology.

Data is at the heart of every organization, and it’s the foundation AI needs to elevate your go-to-market strategy, whether you’re investing in sales, marketing, or customer service. Building a robust data strategy means defining everything from the purpose for collecting specific information, to the type of data you need, and the methods you’ll use to collect, unify, and leverage data.

For the best results, you’ll need to answer questions like:

·       How accurate and valuable is your data?

·       How do you define and ensure data quality?

·       How can we centralise or at least centrally access our data?

·       Where does your data originate (where does it come from)?

·       Are you using your data to its fullest potential?

·       Who has access to this data?

·       What forms of data do you need to collect?

·       Does your data include sensitive elements?

Data and the AI Trust Gap

Answering the questions above with a robust data strategy, before you start implementing artificial intelligence into your workflow, helps you to create a more comprehensive plan for success. However, it also serves another purpose –addressing the AI trust gap.

If you’re using artificial intelligence within your go-to-market strategy, to power personalization throughout marketing, sales, and customer service, you need to overcome a few hurdles. To provide personalized experience, your AI model needs to be securely provided with access to the right information (contextual customer data). This helps to prevent AI hallucinations, and ensure you’re producing the right responses to customer questions, and the right sales and marketing strategies.

At the same time, you need to ensure the data you’re using to power your AI solutions adheres to privacy and security guidelines. While you want to make sure your AI systems can personalize experiences, you can’t risk giving them data that would put you at risk of fines and compliance issues.

Tips for Creating a Data Strategy toPower AI Success

So, how do you create a data strategy that successfully leads the way to AI innovation? It’s all about taking a cautious and informed approach to growing your business.

·       Start with your business objectives: Before investing in any new business strategy, it’s worth thinking about your specific goals and use cases. You’re not just implementing AI to keep up with the competition (or you shouldn’t be). You should have a specific idea for what you want to achieve. Defining your goals will help you determine what kind of data you need to prioritize when developing your AI models.

·       Identify your data landscape: You can’t have a strong data strategy if you don’t know where your data is located, what type you’re collecting, and how you need to manage and store it. Take the time to examine all of the various datasources you have in your business, from interactions with customers, to software solutions like your CRM platform.

·       Define strategies for measuring success: Based on your objectives, think about how you’ll measure the success of both your data and AI strategy. For instance, if you’re planning on building AI chatbots to help deliver customer service, you might monitor metrics like NPS and CSAT scores, as well as customer retention and churn.

·       Ensure compliance: Any form of AI can have data risks. One study found 79% of IT leaders believe generative AI in particularly will introduce new security risks to companies. Making sure you have a strategy in place for compliance is essential. Think about best practices, such as multi-factor authentication, data encryption, and identity and access management.

·       Implement data governance: Create robust and transparent data governance strategies to ensure the integrity, security, and accessibility of your data. This will ensure you’re collecting, protecting, and using high-quality data, without exposing yourself to unnecessary risks.

Combining Your Data and AI Strategies

AI may be the future for many businesses, particularly when it comes to optimizing and enhancing your go-to-market strategies, but you won’t accomplish anything without a solid approach to data. A strong data strategy ensures you can build the right AI initiatives, and generate real results from your digital transformation efforts.

Building a strong data strategy before you dive into AI initiatives ensures you can deliver better customer experiences, and unlock the true potential of artificial intelligence.

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