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“We need 1,000 leads — are we on target?”
It seems like a simple business question, but for many teams, arriving at an answer requires hours of digging through manual files and spreadsheets, piecing together data from individual systems and uncovering where information exists across siloed departments.
It’s not only about finding the right data and bringing it together — knowing whether a team is on track toward its goals takes analysis to understand what the data actually means. This requires a level of expertise and training that most employees, outside of data scientists, don’t have.
As a result, many companies are now leaning into AI to bridge this gap.
Employees can rely on AI to pull relevant data, analyze trends, compare current progress to business goals and make recommendations on what to do next — all without any prior data analysis experience. And because it’s all autonomous, AI can track progress in real-time and identify any shortfalls or potential roadblocks as they happen.
With AI, teams can quickly identify their progress towards goals and make informed decisions on what to do next to drive business impact.
With Slingshot — our AI-powered data-driven work management platform — we put data at the center of every organization and enable teams to quickly analyze and visualize data so they can put it to work immediately. Because all of a company’s data is in one place, AI can access all the data it needs — exactly when it needs it — so teams can ask questions in simple business terms and receive an answer in seconds. This AI-driven analysis saves teams hours of searching and sifting through data, so they can focus on making their data drive value for the business.
If AI isn’t delivering these insights, it’s a sign that teams need to check the data feeding it, review their tech stack or upskill employees — otherwise, they’re missing out on AI’s full potential.
Here are five other questions that teams should ensure their AI is ready to handle.
Related: Two-Thirds of Small Businesses Are Already Using AI — Here’s How to Get Even More Out of It
1. Which KPIs are underperforming and need attention?
Key performance indicators — or KPIs — are important for understanding how well a company is running its operations and hitting its goals. Teams often spend time checking individual metrics, like website traffic or how many customers they have, but this means very little in relation to larger company goals. Instead, they need to create KPIs like “increase website traffic by 5%,” or “increase monthly active users of a product by 10%,” to track against larger business goals.
Most of the time, tracking KPIs requires a holistic look at many different departments and business processes. And they require regular review, to both avoid any roadblocks and adjust as a company’s strategy evolves in real-time.
Teams can bring together multiple data sources to calculate KPIs in real-time with AI. This allows them to immediately see if they’re tracking with their KPIs — and if they’re not, AI can recommend actions to improve them.
2. What is our ideal customer profile — and how is it changing?
Go-to-market teams aim to focus on their highest-fit prospects, because they’re the ones most likely to buy their products. Many are, however, relying on outdated personas or their gut instincts on where to prioritize their efforts. AI can analyze CRM data, product usage and support tickets to uncover emerging trends in behavior, sentiment and adoption that would take days to surface manually. With these insights, teams can identify their ideal customer profile, adjust targeting, personalize messaging and refine their go-to-market strategy to drive success.
Related: AI Can Give You New Insights About Your Customers for Cheap. Here’s How to Make It Work for You.
3. What’s our feature adoption rate by user segment?
Product teams, specifically in tech, likely know which features are being used most frequently and how many users they have each month — but they often struggle to break down that usage by user type, industry or reason. Even when that data exists, manually sorting through it can take hours — or even days, making it difficult to understand what’s working, what’s not and which users are truly benefiting from the product.
That lack of clarity can lead to wasted time and resources on features that don’t move the needle for core customers. With AI-powered tools, teams can automatically segment users based on behavior, role, company size, use case and more, and instantly surface adoption trends across these key segments. This enables teams to focus on building features that deliver the most value to the right users, to optimize product adoption and customer satisfaction.
4. Which team members are overloaded and how does that affect our project timelines?
Workload imbalance is one of the most common reasons projects fall behind. In fast-paced, cross-functional work environments, it’s easy for some employees to feel overloaded while others are underutilized. While many managers try to keep tabs on what’s on every employee’s plate and who’s at capacity, it’s difficult without a bird’s-eye view into an entire team or department.
AI can analyze task assignments, due dates, cross-team tasks and project updates to spot patterns that employees or managers might miss — like unrealistic timelines, resource gaps or dependencies that are holding things up. With this insight, teams can rebalance workloads, course-correct before delays spiral and keep projects moving more efficiently.
Related: How to Prepare Your Small Business for the Next Wave of AI Innovation
5. How should we allocate next quarter’s budget and headcount next quarter to drive growth?
While many businesses look backwards to evaluate performance, AI can help look ahead. By analyzing insights such as historical sales data, marketing performance, user adoption and resource utilization, AI can provide recommendations on where to allocate budget and headcount. AI can identify where the largest return is coming from, where additional investment could be beneficial — and where it makes sense to scale back. That may mean doubling down on a high-converting marketing channel, investing into more sales support or reducing focus on a specific product or product feature.
Employees shouldn’t spend hours digging through data or trying to understand what it means. Instead, AI should be able to share instant visibility into what’s working, what needs attention and where to go next with simple questions. That kind of clarity drives better decisions — and better results.