Rapid technological advances are changing how people do business—especially in post-pandemic times. Currently, the demand is for AI technology. PwC reported that “AI could contribute up to $15.7 trillion to the global economy in 2030” and will continue to be a game-changer by enabling organizations to increase productivity and consumption.
All businesses—be it healthcare, manufacturing, hospitality and even entertainment—are adopting AI to offer deep insight into their business processes and provide leading indicators that can help an organization prosper. To fully appreciate how AI is shaping the game, let’s look at some stats:
• In 2020, the AI in banking worldwide market was worth nearly $4 billion. By 2030, it’s expected to be valued at over $64 billion.
• AI in healthcare was valued at $7.9 billion in 2021 and is predicted to grow to $201.3 billion by 2030.
Although these numbers are promising, it’s important to note that for a business to succeed in its AI journey, the organization needs to be ready for AI innovation. This means working on its IA—infrastructure architecture—before the actual AI. Doing otherwise can lead the venture to fall to the wayside. According to Gartner analysts, “85% of AI and machine learning projects fail to deliver, and only 53% of projects make it from prototypes to production.”
One major roadblock to a successful AI implementation is that although organizations usually have petabytes of data, the data is often unorganized, uncleaned and unanalyzed and sitting in a number of systems from ERP to CRM. Often, organizations simply don’t have the right infrastructure or expertise to make sense of it.
For AI programs to work, data needs to be collected, cleaned and analyzed. However, the reality is that 82% of organization leaders say that data quality hinders their data integration projects and that they spend too much time on data cleaning, integration and preparation.
The goal for AI solution providers should be to help businesses become data-driven so that enterprises can utilize AI to the fullest in order to drive insights and predictions. AI solution providers can work with enterprises on these obstacles by keeping these four pillars in mind: creating a center of excellence and a collaborative AI team, prioritizing data modernization, embracing cloud transformation and leveraging partnerships.
1. Create a center of excellence.
Solutions providers should team up with a company’s internal employees, train or mentor the company’s representatives on the AI program and create their own center of excellence (CoE). Although the team might include data scientists or IT professionals, the CoE can also include marketing executives, end users, consumers and statisticians—all the minds required for collaboration. Consider the domain knowledge, understanding of customers and customer data, technology know-how and interpretation of data. The organization can work with its AI solution provider to create a roadmap as a guide on what benefits AI can provide in each area.
Each team member should be carefully selected based on the requirements and expertise a business needs. They should not only immerse themselves into the project but also maintain the company culture, as well as work according to the strategic goals.
2. Prioritize data modernization.
Organizations need information assets before AI. Creating a data architecture around an organization’s data assets is a critical next step. The newly formed team, along with the solutions provider, should handle this. They will have to determine which data should be collected and create an information architecture that can be utilized for AI purposes.
The first order of business should be how to collect data, which includes identifying data silos. It’s also necessary to utilize “wide data,” or data coming from a variety of sources—internal and external as well as both structured and unstructured. “Big data” needs to be collected, too, or massive data coming at great speed.
The next step is to determine the processes and use cases the new data architecture can support. The team should keep in mind end-to-end migration services with automation that can take the company from planning to execution.
3. Embrace cloud transformation.
I highly suggest that businesses and customers today make the switch to cloud services. Many organizations are still utilizing legacy and on-premises technologies to store data. The cloud is now the better option when creating an AI framework. It lessens the bulk of hardware and also allows an organization to access the AI system from any device without further installations and processes. If data is still stored on physical servers, they just need to be migrated to secured cloud servers.
4. Leverage partnerships.
Although big organizations usually have licenses with IBM Cloud Pak or Snowflake, their stumbling block to a successful AI journey is that they don’t always know how to utilize these tools for AI implementation. The challenge is connecting the dots—utilizing third-party services for existing internal machines or data to create a prediction engine.
In addition, many popular warehousing or other big data technologies don’t necessarily have AI plugins or prediction engines. The AI team should be tasked with the challenge of creating a system that uses the licenses or partnerships the company has for the solution they want to have. The AI solution provider must build that bridge that gets them to the finish line.
The reality is that the AI journey can be filled with potholes. There’s a large amount of data sitting in an organization’s system, ready for harnessing but often siloed by disparate teams. Other issues are that many companies are already invested in technology that can’t be utilized for AI and, sometimes, the knowledge isn’t there on how to leverage existing licenses and technologies for AI purposes.
The key to guiding companies in AI innovation is to let them visualize the possibilities and remind executive leaders that technology is advancing quickly, and AI is now typically considered a must-have asset for business sustainability. So, use these pillars and present the potential of an AI-driven enterprise.