No matter where you go, the message about artificial intelligence is the same: success depends on high-quality data. This became the mantra in every boardroom and conference room.
Companies are investing millions of dollars to clean, label and organize data, believing that once they get it right, AI will transform it.
But this belief is incomplete. Cleaning and collecting data is step 0. Even the cleanest data set won’t drive your business forward if you don’t have the engineering, architectural, and operational readiness to use it.
Most companies try to cross the finish line without building a car.
According to Gartner research, 63% of organizations do not have the right data management practices in place for AI or do not trust it.
But even if your company doesn’t know where to start transforming data into AI, there are some simple strategies any organization can use to achieve business results.
Why does progress stop at level zero?
When there is a gap between data and activation (strategy, engineering, modernization, visualization, readiness), progress stops. Some organizations have ambitious data strategies that fail to deliver measurable business results.
Other companies collect and store large amounts of information without planning how to transfer it between systems. In most cases, legacy IT infrastructure makes modernization nearly impossible and isolates data teams from decision makers.
Gaps in skills and experience are also common barriers. Companies may have data analysts who can interpret dashboards, but they lack data engineers and architects who can create pipelines and governance structures to make insights reliable and scalable. Without enough talent, organizations get stuck somewhere in the process.
This doesn’t prevent you from understanding the numbers more deeply. This hinders innovation within the company. Nearly half of the executives surveyed by IBM said data challenges continue to hinder the adoption of artificial intelligence in their organizations.
If your team can’t trust the data, you won’t be able to use it as the basis for your AI strategy, even with pressure from senior management. Maybe AI is the shiny thing everyone wants to talk about, but making it work is “boring”.
Turn data into real business results
Solving this problem doesn’t require hiring an entire department or investing in dozens of new data tools, but it does require a change in how your organization prepares. Real preparation starts with planning data operations with business outcomes in mind.
Established companies in the field consider engineering and construction as business areas. Define clear ownership of data pipelines, establish governance from the start, and modernize infrastructure so data moves securely and efficiently.
Once these elements are implemented, business results will be seen. Some organizations have reduced downtime and increased productivity by integrating production and maintenance data. Systems that can eventually exchange data could generate significant revenue.
In some cases, consolidating financial and operational data can also reduce infrastructure costs by eliminating duplicate software licenses. This can save you thousands of dollars every month. Having foresight saves money.
Risk is also significantly reduced when control and visibility are integrated into daily operations. Leaders can believe what they see and demonstrate integrity in every decision. When data is consolidated, organizations can proactively detect vulnerabilities and significantly reduce the likelihood of cybersecurity breaches.
Many companies try to integrate these layers internally but ultimately realize they need a partner to guide them through the entire process, from strategy to architecture, modernization and AI readiness. The right partner brings structure, talent and repeatable processes to turn readiness into results.
Speed ​​is more important than size
When your organization has this foundation, you can move from vision to implementation faster. Small organizations with modern data architectures have outperformed larger competitors using legacy systems. When data flows freely, decisions are made faster, forecasts are more accurate, and automation is improved.
AI literacy is now an important component. Artificial intelligence adoption is what separates companies that thrive from those whose projects fail.
In the race for artificial intelligence, the one with more data doesn’t always win. They know how to build the fastest car and take it to the finish line.
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