As artificial intelligence (AI) makes information easier to analyze, the harder business challenge remains: connecting what an organization knows with what it actually does.
A marketing campaign generates thousands of leads, sales conversations reveal which prospects are serious, yet the next campaign repeats the same targeting mistakes. The information is available, but what is missing is the connection. That gap matters as businesses adopt AI. It can analyze data, identify patterns and automate tasks. But its value depends on what happens next: whether the findings reach the right people and change the decisions they make.
The difference between using AI and getting value from it
Gartner’s 2026 survey found that only 22% of organizations had successfully scaled AI across multiple business units or adopted an AI-first approach. PwC’s 2026 AI Performance Study found that the top 20% of organizations accounted for 74% of the AI-related economic value reported in its research. These leading companies were twice as likely as others to redesign workflows around AI rather than simply add new tools.
The lesson for business leaders is practical: adding AI to an existing process is different from rethinking how that process works. Take demand generation: AI can help identify audiences, personalize campaigns, and prioritize leads. But if marketing and sales disagree on what makes a lead valuable, greater efficiency will not fix the problem. Better results require campaign data, sales feedback, and conversion rates to inform one another. The question is not simply how much work AI can do. It is whether the business makes better decisions because of it.
What organizations fail to connect
A typical scenario involves sales facing customer rejections, operations seeing recurring delays, and finance tracking costs. Each team has a part of the big picture but the information may never come together when a decision is being made. Now, AI can help connect these sources and surface patterns. People still need to understand what those patterns mean and decide what to do about them.
A delivery delay, for instance, may look like an operational problem. Link it to the lost repeat orders and rising fulfilment costs, and it becomes a wider commercial concern. The response may require changes across several teams, not simply a faster delivery process. This is where a broader idea of organizational intelligence becomes useful.
Introducing Systemic Intelligence
Systemic Intelligence (SI) is a proposed framework for describing how a business connects information, technology, people and experience to improve decisions. AI can analyze the evidence. SI asks whether the organization can use the findings collectively. At Kings Global, this question has practical relevance. Work is under development on AI-powered framework to improve efficiency in demand generation. Its potential lies in connecting insights across targeting, engagement, and conversion, rather than treating each stage as a separate task.
Eventible is also developing AI-powered capabilities for event reviews and discovery, serving B2B marketers, organizers, speakers, and sponsors. Here, the challenge is helping people assess which events best fit their objectives by making relevant information easier to find and interpret.
Both initiatives are in development. Their eventual value will depend on the decisions they improve and the results they deliver. The applications differ, but the principle is shared: information becomes more useful when it helps someone make a better choice.
4 questions for business leaders
Before investing in another AI tool, leaders should ask:
- Where does information get lost?
Look at the handovers between teams, not just the technology they use
- Does feedback change what happens next?
If results do not influence future decisions, the business is missing an opportunity to learn.
- Can people question the output?
AI recommendations need context, scrutiny, and clear accountability.
- Are we measuring results or activity?
Automation matters when it improves outcomes such as conversion, customer value, cost or decision quality.
The answers can help identify whether the real obstacle is technology, poor data, unclear ownership or a broken process.
The real measure of intelligence
As AI becomes more accessible, the technology alone may offer less distinction between competitors. How effectively a business uses it will matter. That takes reliable information, clear responsibility and a willingness to change course when evidence demands it. It also requires people who can bring context and judgment to what machines produce.
One question captures the challenge: when a business learns something important today, does it change a decision tomorrow? AI can find the signal, while SI can put that information to work.
