Companies are commonly using AI to automate repetitive tasks, generate and personalize content, identify patterns in performance and operations, among many other things — all in the name of driving efficiencies, powering more informed decisions and ultimately, accelerating business growth.
It’s clear that AI is already helping companies to cut costs and save time. They’re often, though, applying AI to individual tasks across their business. It’s only when these isolated efforts are aligned in support of a broader strategy that they can unlock a greater impact.
This shift — from AI as a purely tactical tool to a strategic sidekick — is where companies will see the biggest impact to their bottom line.
As the CEO and founder of a global technology company, I’ve seen firsthand how AI is not only accelerating go-to-market execution but transforming how businesses engage with customers. At Infragistics, we’re leveraging AI — and empowering other organizations to do the same — to identify ideal customers for new and existing products, craft and test messaging that resonates and measure performance in real-time, among other things.
But, an AI-powered go-to-market strategy is only as strong as the data behind it. That’s why data is at the core of everything we do as a company — and why we built Slingshot: a data-driven work management platform that puts data at the center of their organization. With all of a company’s data in one place, easily accessible and integrated into the team’s daily workflows, AI becomes exponentially more powerful and its recommendations far more actionable.
Here’s how companies can build a strong AI foundation to effectively target audiences, refine messaging, optimize spend and grow revenue.
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1. Set clear goals for AI
AI has the power to do research, analyze data, make recommendations and forecast trends — all in support of a go-to-market strategy. But, in order for AI to do this effectively, it needs clear direction. Is your company launching a new product? Entering a new market? What does success look like in six months? In a year? And how will progress be measured?
The more clarity you can provide, the more strategic AI can be. Teams perform better when they understand how their individual role within the company contributes to larger company goals — AI needs that buy-in too.
At our company, we make it a priority to regularly communicate business objectives and long-term goals across teams so everyone knows exactly what they’re working toward. And with Slingshot, we’ve taken that a step further. We’ve created purpose-driven templates for key use cases like channel-specific marketing campaigns, growth hacking and product launches, so teams can stay aligned with each other and AI.
With clear objectives, AI can help to identify customer needs, refine an ideal customer profile (ICP), tailor messaging across customer segments, recommend timing and channels for campaigns and continuously measure performance and optimize accordingly. Without an understanding of the bigger picture, both teams and AI end up just going through the motions.
As teams rely more on AI to complete these tasks, they’re not only removing more repetitive research and time-consuming analyses off of their plate, but they also have actionable insights to move faster and make more informed decisions.
But for AI to do this effectively, it needs quality data that’s readily accessible — all in one place.
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2. Break down data silos
AI is nothing without data. Most businesses know this, yet nearly half (45%) of employers say they haven’t yet implemented AI because their company’s data is not ready. But being “AI-ready” isn’t only about quality data — it’s about making that data centralized, connected and accessible across the entire organization.
We’ve seen that companies’ data often lives in siloes–spread across marketing platforms, CRMs, ERP systems, spreadsheets and more. With data scattered across organizations like this, AI can’t see the full picture. This limits its ability to generate insights, spot patterns and deliver meaningful value to companies.
For AI to effectively support a go-to-market strategy, it needs a unified view of the business, from customer and marketing data to sales and operations.
With Slingshot, all of our company’s data — across departments, platforms and channels — is in one place. This allows our teams to easily see what data we have, access it exactly when they need it, and analyze it in real-time.
Where it would take our team 35 minutes on average to analyze three individual data sources (like Google Analytics, Google Ads and Salesforce), it now takes 10 minutes in Slingshot — including sharing insights with the team and assigning next steps.
With this near-instant analysis, AI can start delivering value right away — spotting trends across the entire customer journey, offering real-time insights and making smarter and faster recommendations.
A centralized data foundation will empower AI to inform the best decisions for a business and act as a true collaborator, working alongside a team that can turn these insights into action.
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3. Turn AI into your teams’ go-to collaborator
The impact of AI ultimately depends on how well a team can make use of the insights it generates — especially when executing a go-to-market strategy.
Too often, AI outputs remain unused. In fact, Slingshot’s Digital Work Trends report reveals only 44% of employees say they’ve seen a significant increase to their productivity with AI. This could be due to employees’ hesitation to adopt AI due to fear that it will replace them, a lack of trust in the recommendations AI makes or insufficient training to the technology effectively.
But AI is not meant to replace employees but amplify their potential. It can uncover opportunities, support decision-making and remove administrative lift, enabling employees to focus on more high-level strategic work. This can mean testing messaging across customer segments or reallocating budget based on real-time feedback — all without the guesswork.
Everything AI does requires human input to be meaningful. Teams must understand, interpret and act on what AI surfaces. This means organizations must not only provide the right tools, but also foster a culture where teams are encouraged to experiment with AI and understand AI’s role as a collaborator, not a replacement.
With a go-to market strategy, timing, targeting and iteration are key to success — and AI has the potential to maximize how businesses plan and execute at every step. But success doesn’t come from using AI alone. It’s about integrating AI into how teams think, work and grow so your business can do the same. When companies lead with clear goals, centralized data and empowered teams, they can unlock AI’s full potential —and the business results will follow.
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