Achieving AI Excellence: The Crucial Role of Process Automation in Preparing Enterprises for AI

Summary: In today's rapidly evolving business landscape, artificial intelligence (AI) stands out as a transformative force, reshaping industries with innovation, efficiency, and competitive prowess. However, the true realization of AI's potential within an enterprise is contingent upon a fundamental pillar: data quality. Without precise, dependable, and high-caliber data, AI's capabilities remain untapped. This is where Business Process Automation steps in as a foundational stone, paving the way for a seamless transition into the world of AI.

In this article, we look at the importance of data quality for the successful use of AI and how Business Process Automation can support generating quality data. This article is based on our on-demand webinar “Unlock AI-readiness with Process Automation”. Watch the webinar to explore a live demo of how to streamline the onboarding process using AI.

Artificial Intelligence (AI) and Machine Learning (ML) as revolutionary technological trends

While certain technologies offer incremental enhancements, others, such as AI, internet connectivity, and personal computing, wield a profound impact, fundamentally altering societal and corporate paradigms.

These technologies typically undergo a consistent evolution process: Initial development, followed by a phase of hype, and finally growth. In the developmental stage, they often operate under the radar and attract limited public attention. As they progress to the hype phase, numerous potential use cases emerge, driving extensive experimentation. Yet only a handful of these experiments ultimately prove commercially viable and scalable. When the growth phase arrives, the technologies that manage to endure can potentially become significant disruptors, reshaping the landscape of how companies function and compete.

Understanding the impact of data quality on AI-readiness

Over time, enterprises have incorporated a multitude of software systems to streamline and oversee their operations. As the journey progressed, additional layers were integrated, culminating in a sophisticated ecosystem of enterprise applications. This intricate tapestry encompasses ERPs, CRMs, and bespoke solutions, each contributing to the intricate complexity. In the present context of investing in AI capabilities, enterprises encounter a distinct set of challenges. These challenges arise due to the existence of systems that lack seamless integration, necessitating manual interventions. Moreover, the flow of information within this ecosystem often exhibits irregular patterns, further exacerbating the complexity. This all makes extracting quality data from enterprise systems and applications extremely difficult. On top of this, the difference between the planned process and the executed process varies drastically. Take, for instance, the planned process for a credit application below. The diagram is the representation of the planned process across processes, systems, and people layer:

But more than often, the process looks something like the image below. There is chaos at the system layer as there is neither a single source of orchestration nor task management. This is accentuated by the fact that enterprises throw technologies at every piece of problem and these technologies continue to exist in silos. This leads to issues with the reliability and quality of data being generated by the systems and processes.

This has a cascading effect on AI capabilities, leading to inaccurate predictions, misguided decision-making, and compromised customer experiences. So essentially, data quality impacts the accuracy and reliability of your decisions based on AI models. In turn, as explained earlier the root cause of bad quality data is manual, loosely integrated, and poorly executed business processes.

How does Business Process Automation support data quality and in turn AI-readiness?

Business Process Automation (BPA) involves the use of technology to streamline and manage business processes, reducing manual intervention and improving operational efficiency. BPA ensures good quality data by addressing several key areas:

1. Data collection and integration: Ensures that data is collected consistently and integrated seamlessly from various sources. This minimizes the chances of data duplication, redundancy, or errors during the collection process.

2. Data cleansing and validation: Automated workflows can be set up to clean and validate data using predefined rules. This includes removing duplicates, correcting inaccuracies, and validating data against predefined criteria.

3. Data governance: BPA enables the establishment of data governance protocols, ensuring that data is managed, stored, and accessed in compliance with relevant regulations and internal policies.

4. Real-time insights: BPA systems can generate real-time insights by processing data quickly and accurately. This enables enterprises to make decisions promptly, keeping up with the fast-paced business landscape.

5. Adaptation and scalability: BPA systems can be designed to adapt to changing data requirements and scale as the business grows. This agility is essential in accommodating the evolving needs of AI systems.

In the fast-changing world, data quality and AI-readiness are vital for business success. In this article, we have dived into how data impacts AI. Business Process Automation (BPA) steps in as a powerful solution to generate the necessary data for AI. It streamlines processes, creates top-notch data, and supports AI growth. So, as we navigate tech changes, integrating BPA opens doors for AI potential, boosting efficiency and innovation.

Explore more in our on-demand webinar "Unlock AI-readiness with Process Automation" to see BPA and AI in action.