Council Post: Here’s How AI Can Turn Every Decision Into A Data-Driven One

Eilon Reshef, cofounder and CPO of Gong, is a seasoned entrepreneur, executive and investor in the internet and software spaces.

Data-driven decisions are more impactful than decisions made without a substantive basis in data. Research shows little ambiguity: In a 2023 Harvard Business Review survey sponsored by Google Cloud, 75% of respondents said that “having a data-driven culture is very or extremely important to their organizations’ overall success.” And leaders who use AI most strategically tend to be more profitable than their AI-less peers.

The problem is that not every domain has equal access to high-quality, easily analyzable data. Take, for example, marketing versus sales.

Marketing is faced with an embarrassment of riches: They can track webpage bounce rates, email click-throughs, social media post performance, demo requests driven by certain pages and much more. If you’re a director of social media marketing looking to refine your social media identity, you have a lot of data to iterate around.

Sales has historically had it harder. Their primary source of data has been their CRM system, and most of that data is created by manual input from sales reps. Compared to marketing’s data, this is harder to produce, more biased and less structured—more challenging for leaders to incorporate in effective decision-making.

Recruiting is another example, where information about candidates comes largely from resumes and recommendations, which don’t share a common structure. Or think about product teams, whose data about how customers are using the product may come in the form of surveys and usage trends but also informal conversations and easy-to-overlook comments during check-ins.

But just as CMOs turn to metrics about their website to make strategic decisions, leaders of HR, product and sales teams must do the same. So, how can these leaders—with less immediate access to structured, analyzable data—ensure they’re steering their organizations in the right direction?

Understanding and embracing AI as a key decision-making aid can make a world of difference here.

How AI Helps Leaders Make Data-Driven Decisions

AI’s primary strength here is translating unstructured data into structured data.

Let’s go back to recruiting. In the past, HR leaders would have to sift through dozens of resumes and perhaps even attend interviews to discern what made a successful candidate or recruiter. How well this was done depended on the leader’s skill. AI, on the other hand, identifies information with precision and in a fraction of the time. For example, AI can read through a resume and understand what roles the person has completed. Or, it can analyze an interview call transcript and understand how the candidate addressed a certain question. Such data points can then be correlated with the employee’s performance over time.

AI platforms with expertise in a specific area can produce enormous volumes (think about how many sales calls your organization conducts) of new structured, analyzable data. From there, it’s just a matter of incorporating it effectively into your existing processes.

Putting It Into Practice: How To Use AI For Decision-Making

AI shouldn’t function as the decision-maker but as an indispensable assistant in the decision-making process. Here are the steps to take to make this happen:

1. Understand what questions you want to ask. Even before you start capturing data, it’s crucial to have a sense of what you’re looking for—what strategic insights you aren’t currently getting that you believe your data can yield.

2. Collect all relevant data. Comprehensive data capture is essential to making data-driven decisions. If you’re a sales leader, you need to capture every word of every prospect and customer interaction so that your AI tool can turn it into analyzable data. This is the principle my company, Gong, has based all of our innovation on: Capture everything; turn everything into fodder for analysis.

3. Convert it from unstructured to structured data. This is where AI magic takes over. Find an AI tool—ideally, a tool with deep specialization within a particular domain—equipped to turn raw unstructured data into structured data.

4. Perform detailed analysis. Having defined the questions you’re trying to ask and answer and having generated these new raw materials, engage analysts or data-savvy team members to mull over the data, extract relevant insights and propose action steps based on it.

5. Iterate. Refine and redefine the process depending on whether it answered your initial questions. This will likely mean incorporating new tools and processes into your decision-making, so iteration will be essential.

The better your data, the better your decisions. In my experience, the highest-performing organizations use AI to maximize the value of their data. Use the steps outlined above as a road map for up-leveling your strategic decision-making as a leader to drive business success.

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