How data, AI and automation can transform the enterprise

Today’s data leaders are expected to make organizations run more efficiently, improve business value, and foster innovation. Their role has expanded from providing business intelligence to management, to ensuring high-quality data is accessible and useful across the enterprise. In other words, they must ensure that data strategy aligns to business strategy. Only from this foundation can data leaders foster a data-driven culture, where the entire organization is empowered to take advantage of automation and AI technologies to improve ROI. These areas can transform the enterprise, from cost savings to revenue growth to opening new business opportunities.

Building the foundation: data architecture

Collecting, organizing, managing, and storing data is a complex challenge. A fit-for-purpose data architecture underpins effective data-driven organizations. Driven by business requirements, it establishes how data flows through the ecosystem from collection to processing to consumption. Modern cloud-based data architectures support high availability, scalability and portability; intelligent workflows, analytics and real-time integration; and connection to legacy applications via standard APIs. Your choice of data architecture can have a huge impact on your organization’s revenue and efficiencies, and the costs of getting it wrong can potentially be substantial.

Read more: Why IBM recommends a data fabric architecture as a solution.

The right data architecture can allow organizations to balance cost and simplicity and reduce data storage expenses, while making it easy for data scientists and line of business users to access trusted data. It can help eliminate siloes and integrate complex combinations of enterprise systems and applications to take advantage of existing and planned investments. And to increase your return on AI and automation investments, organizations should consider automated processes, methodologies, and tools that manage an organization’s use of AI through AI governance.

Taking advantage of automation for LOB and IT activities

You can use data to completely digitize your organization with automation and AI. The challenge is bringing it all together and implementing it across lines of business and IT.

For line-of-business functions, here are five key capabilities to consider:

  1. Process mining to identify the best candidates for automation and scale your automation initiatives before investments are carried out
  2. Robotic process automation (RPA) to automate manual, time-consuming tasks
  3. A workflow engine to automate digital workflows
  4. Operational decision management to analyze, automate, and govern rules-based business decisions
  5. Content management to manage the growing volume of enterprise content that’s required to run your business and support decisions
  6. Document processing to read your documents, extract data, and refine and store the data for use

Looking at the digitization of IT, here are three capability areas to evaluate:

  1. Enterprise observability to improve application performance monitoring and accelerate CI/CD pipelines
  2. Application resource management to proactively deliver the most efficient compute, storage, and network resources to your applications
  3. AI to proactively identify potential risks or outage warning signs across IT environments

Help increase ROI on data, AI and automation investments by making data and AI ethics a part of your culture

But process and people can’t be ignored. If you don’t properly infuse AI into a major process in an organization, there may be no real impact. You should consider infusing AI into supply chain procurement, marketing, sales, and finance processes, and adapt processes accordingly. And since people run the processes, data literacy is pivotal to data-driven organizations so they can both take advantage of and challenge the insights an AI system can provide. If data users don’t agree or understand how to interpret their options, they might not follow the process. This can be a particularly high risk when you consider the implications this can have when it comes to cultivating a culture of data and AI ethics, and complying with data privacy standards.

Building a data-driven organization is a multifaceted undertaking spanning IT, leadership, and line of business functions. But the dividends are unmistakable. It sets the stage for enterprise-wide automation and IT. It can provide a competitive edge to organizations in their ability to quickly identify opportunities for costs savings and growth, and even unlock new business models.


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