AI has quickly become a boardroom topic. As a member of CNXN Helix™ Center for Applied AI and Robotics, I’ve had conversations with hundreds of organizations ranging from “We’re still figuring out our AI policy” to “We’re trying to scale our third production use case.” While those organizations may be at very different stages of adoption, I’ve noticed they often face the same challenge.
They’re spending a lot of time discussing AI technology and not enough time discussing business outcomes. Often, the goal becomes deploying something rather than solving something.
Conversations often start with topics like copilots, agents, models, platforms, and vendors. Those are important topics, but they’re rarely the reason an initiative succeeds or fails. In fact, one of the most common patterns I see is organizations trying to identify a solution before they’ve fully defined the problem they’re trying to solve.
The results are predictable. Pilot projects struggle to gain traction. Success becomes difficult to measure. Stakeholders lose interest. Teams move on before realizing meaningful value from the original investment. What’s interesting is that this has very little to do with the quality of the technology itself. Most organizations don’t have an AI technology problem. They have a prioritization problem.
The organizations making the most progress tend to frame the conversation differently. Before evaluating technology, they align around a few fundamental questions about the business. Over time, I’ve found myself returning to the same three questions.
1. What Decision Gets Better?
This is the most important question, and it’s often the most overlooked. AI creates value when it improves a decision, automates an action, or helps people execute work more effectively. Yet many initiatives begin with a discussion about features and capabilities rather than outcomes. A better starting point is identifying the specific decision you’re trying to improve.
• Maybe it’s helping customer service teams determine which issues should be escalated.
• Maybe it’s helping operations leaders identify bottlenecks earlier.
• Maybe it’s helping finance teams process information more efficiently.
The use case itself matters less than the clarity behind it. If you can’t clearly articulate what gets better, it becomes difficult to define success, measure results, or justify continued investment. Organizations that answer this question well tend to move faster because everyone is working toward the same outcome.
2. What Happens if We Do Nothing?
Most AI business cases focus on potential benefits. I think the better question is what happens if nothing changes.
• Do operational costs continue to rise?
• Do employees continue spending valuable time on repetitive work?
• Do customers continue experiencing delays or inconsistent service?
• Do you get lapped by a competitor that’s thinking more strategically about AI than you are?
Not every AI opportunity deserves investment. Some ideas are interesting. Others are important. Understanding the cost of inaction helps leaders distinguish between the two.
The strongest initiatives are usually connected to a business problem leadership already wants solved. AI simply becomes one of the tools used to solve it. Organizations don’t need to chase every trend or be first to adopt every new technology. But they should understand the consequences of standing still. That’s often where urgency becomes clear.
3. Who Owns Success after Launch?
This is where many promising initiatives lose momentum. Most organizations know who owns the technology. They know who is responsible for infrastructure, security, support, and implementation. Business outcomes are different. I’ve seen plenty of projects where everyone was supportive and nobody was accountable. That’s a dangerous combination.
Too often, implementation is treated as the finish line. In reality, it’s the starting point.
If an AI initiative is intended to improve customer service, operations, finance, marketing, or another business function, someone within that function needs to own the outcome.
• Who is responsible for adoption?
• Who is measuring success?
• Who is accountable for ensuring the initiative is still delivering value six months after launch?
Successful initiatives typically have a leader whose reputation becomes connected to the outcome. That accountability creates momentum long after the implementation team has moved on. Without clear ownership, even well-designed solutions can struggle to deliver the results everyone expected.
AI Is a Business Conversation
One thing I’ve learned through these conversations is that organizations rarely struggle because they lack access to AI technology. More often, they struggle because they haven’t aligned around the business problem they’re trying to solve, the consequences of not solving it, or who will ultimately own the outcome. At CNXN Helix™, these are the conversations we spend most of our time facilitating. Not because technology isn’t important. It is. But the success of an AI initiative is often determined before a platform is selected, a pilot is launched, or a budget is approved. Before funding an AI initiative, ask three questions:
1. What decision gets better?
2. What happens if we do nothing?
3. Who owns success after launch? Answer those questions first, and the technology decisions become much easier. Need further help with your AI initiatives? Reach out to your Connection Account Team, learn more about our AI solutions and services, or visit CNXN Helix.
Korry Scott
Korry Scott is a Business Development Manager at Connection, managing a dynamic territory encompassing Illinois and Wisconsin, a region that accounts for $100 million in annual revenue. A solutions-oriented sales professional with a keen focus on meaningful business outcomes, Korry helps clients meet and exceed their goals through insightful positioning of Connection’s extensive product and service portfolio. With over 15 years of experience selling IT solutions, Korry is adept at leading conversations around Software, Managed Services, Cloud, Data Center, and Networking. His client-first methodology has led to many long-term professional relationships and a reputation for trustworthiness. Building on a bedrock of experience selling advanced technologies, Korry uses the Helix Center for Applied AI and Robotics as a powerful asset for customers who are embarking on their own transformation journey. He believes that the AI revolution has arrived, and he’s excited to help clients improve and elevate themselves by capitalizing on this thrilling technology. His native curiosity about all things tech naturally bled into AI as the conversation around it became more present and actionable. Utilizing a deep bench of technical experts across the organization, Korry is skilled at positioning the right resource at the right time. This extends to Helix as we help organizations prepare for, and execute on, their AI-assisted vision. Korry graduated from the Haworth College of Business at Western Michigan University with a BA in Marketing. In addition to certifications within Connection’s Healthcare and Manufacturing practices, Korry also holds credentials with our partners Cisco, Microsoft, Veeam, Dell, VMWare, and HPI.
