From Dashboards to AI: Avoiding the Same Hype Cycle and Building Value

The conversation around artificial intelligence (AI) feels eerily familiar. Organizations are caught up in the same hype cycle that business intelligence and analytics went through a decade ago. Back then, everyone wanted dashboards. Today, everyone wants AI. In both cases, the challenge isn’t about compiling output; it’s about generating business value.

Personal Productivity vs. Process-Embedded AI

Many organizations are focusing their energy on AI solutions for personal productivity: broad-reaching, off-the-shelf tools like chatbots, assistants or large language model (LLM) applications, all designed to help individuals write faster, summarize meetings or search more effectively. These tools are appealing because they’re accessible, inexpensive to try and easy to demonstrate.

But just as dashboards didn’t automatically equal better decision-making, personal productivity AI doesn’t automatically create business impact. What’s often missing is a focus on process-embedded AI applications designed to integrate directly into business processes and workflows or operational systems. This is where real return on investment (ROI) is unlocked.

For example, an AI system that improves supply chain forecasting, enhances outage response or optimizes asset maintenance is embedded in the business’s value-creation process. These solutions are harder to design and implement, but they deliver tangible outcomes that personal productivity tools simply can’t.

Learning From the Analytics Era

In the analytics era, organizations rushed to deliver dashboards before aligning with a strategic, cohesive vision. Everyone wanted them, but few knew what they would do with them once they had them. It took years of maturing tech capabilities, establishing governance, embedding analytics into processes and building a culture of data literacy before businesses could consistently turn dashboard usage into effective outcomes.

AI is no different. Without process alignment and business engagement, AI runs the risk of being another shiny object celebrated in press releases but underdelivering in practice.

Two Paths to Real AI ROI

For AI to deliver sustainable value, organizations can pursue two paths. In one, IT deeply integrates with business processes. Technology teams must move beyond platform delivery and partner directly with business units to identify the right problems for AI to solve. This requires trust, process fluency and a seat at the decision-making table.

Alternatively, the business can develop foundational AI awareness. Business leaders and staff must gain enough literacy to recognize where AI can be applied in their own domains. They don’t need to know how the tools work, but they need to understand what the technology is capable of and where it can realistically drive outcomes.

In most organizations, the second option — building business awareness — is more scalable and effective. By empowering business users to recognize opportunities, IT can focus on enabling solutions that gain traction naturally.

A Framework for AI Growth

Organizations can think about their AI journey through a three-stage maturity framework:

• Readiness. Prepare and set the foundation. This stage is about building literacy and alignment. Activities include leading strategic workshops to grow AI awareness, aligning teams on a readiness framework, and assessing the technology landscape to guide adoption.
• Integration. Build upon the preparation and start embedding AI into workflows. Here, organizations move beyond experimentation and begin aligning AI tools with business capabilities. Workflows are automated with scalable AI platforms, and data infrastructure is assessed to close any integration gaps.
• Transformation. Embrace AI to unlock new value. At this stage, AI becomes a strategic driver. Organizations enable scale with AI-aligned capabilities, modernize asset data with intelligent automation, and advance culture through enterprisewide AI programs.

This progression moves organizations from exposure to adoption to transformation, seeing that AI is not merely an add-on but a true driver of competitive advantage.

Practical Steps to Advance AI Maturity

How can organizations move along this curve? Three actions stand out:

• Educate. Provide training that goes beyond technical demos. Help employees understand AI concepts, limitations and use cases relevant to their function.
• Create awareness. Showcase process-embedded AI examples from your industry. Seeing peers solve real business challenges with AI is far more motivating than another generic chatbot demo.
• Enable development. Give business units the tools and guardrails with which to experiment. Low-code AI platforms, innovation labs or guided sprints can empower teams to explore opportunities safely while aligning with enterprise standards.

Moving Beyond the Hype

The history of analytics teaches us that technology alone doesn’t create value; adoption, alignment and awareness do. Organizations that focus on embedding AI into processes and empowering their people will be better positioned to capture the full potential of emergent technologies. By moving from readiness to integration to transformation, companies can unlock new efficiencies, spark innovation and create sustainable competitive advantage.

The AI era is still unfolding, and those who invest today in education, awareness and process-embedded solutions will not only avoid the mistakes of the dashboard era but also lead the way in defining what successful, value-driven AI looks like for their industry.