In a time when business models are evolving, AI is enabling companies to reevaluate their goals and adopt a more compassionate approach to empower people. A PwC report reveals 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.

Organizations should prepare for AI innovation to succeed in their AI journey. For AI programs to function, data must be collected, cleaned, and processed. Organizations frequently lack the infrastructure and knowledge necessary to make sense of it. 

AI solution providers can help businesses in overcoming these challenges by focusing on the following four pillars:

1. Create a center of excellence

Solutions providers should collaborate with a firm's internal employees or guide the company's representatives about the AI program and establish their center of excellence (CoE). The CoE can comprise marketing executives, end users, consumers, statisticians, data scientists, and IT professionals—all the necessary minds for collaboration. Think about your subject knowledge, your knowledge of customers and their data, your technology expertise, and your ability to evaluate data. The company may develop a roadmap with its AI solution provider to outline the advantages AI can offer in each area.

2. Prioritize data modernization

Before AI, organizations require information assets. The next crucial step is to build a data architecture around the firm's data assets. The team will need to choose the data that should be gathered and develop an information architecture that can be used for AI. How to collect data, which includes identifying data silos, should be first on the agenda. Additionally, "wide data," or information from numerous sources, including structured and unstructured data, must be used. The team needs to know the automated end-to-end migration services that can move the business from planning to executing. 

3. Embrace cloud transformation

Today, businesses and consumers are switching to cloud services. Many firms still store their data using on-premises and antiquated technology. When building an AI framework, the cloud is increasingly the preferable choice. It reduces the required hardware and enables an organization to use any device to access the AI system without requiring additional installs or procedures. One should transfer data to secure cloud servers if it remains on physical servers.

4. Leverage partnerships

Today, while large enterprises typically have licenses for IBM Cloud Pak or Snowflake, their prime obstacle to successfully implementing AI is their need to understand how to use these technologies. The difficulty lies in connecting the dots and integrating external services for already-running internal systems or data to build a prediction engine. 

To sustain an enterprise, AI is increasingly regarded as a necessary asset, and one must use these pillars to demonstrate the potential of AI-driven businesses.

Mr.Anand Mahurkar, Founder & CEO, Findability Sciences