AI’s success is limited by poor digital transformation

Digital transformation has multiple dimensions and complexities, which are sometimes lost in organizations working on it. The recipe for success lies in rethinking the processes and organizational structure to extract maximum value from the technology framework – something that many companies still struggle with.

A 2020 study Boston Consulting Group found that approximately 70% of digital transformation projects fall short of their goals, even when priorities are clearly identified and leadership is aligned. Adding to the challenge is the need to integrate AI into the organization that is transforming. AI is everywhere these days and promises great returns in customer experience and organizational efficiency. Not investing in AI is now a non-starter when starting a digital transformation effort, but the investment can feel like an insurmountable task. Why is this?

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Gaps in the digital transformation roadmap can hinder the success of AI initiatives

The factors leading to failed digital transformation initiatives also act as roadblocks to the success of AI initiatives. These include:

1. Identifying the right problems to solve: Without proper project design and outside intervention, it is incredibly difficult to identify the right problem and approach to solve it. This is where a poorly executed digital strategy or flawed transformation roadmap becomes a bottleneck to AI success: the underlying data strategy was not aligned with the unique needs of the organization in the first place.
2. Lack of an overarching data strategy: Businesses need to have a clear idea of ​​what kind of data they need for digital transformation. Otherwise, they risk investing in inappropriate tech stacks. A good data infrastructure and strategy are the foundation upon which emerging technologies are built, and on top of that, the formulation of an AI strategy is created.
3. Lack of integration between branches and units: Too often, digital transformation projects are self-contained within individual departments rather than being integrated across the company. This can lead to duplication of efforts and wasted resources. Siloing prevents data and insights from moving freely between departments, which can make deploying AI challenging. The fact that many AI programmers are directed by specific departments rather than centrally managed makes the situation even worse. As a result, companies often rely on a small number of vendors for their AI requirements, which can lead to vendor lock-in and limited flexibility when using AI systems. Silos can make employees resistant to change. Professionals may prefer their working environment within the silo, making them resistant to regulations that would disrupt their environment.
4. Lack of CoEs and best practices, good frameworks and approaches: A bad digital transformation initiative does not create the right system of best practices, centers of excellence and frameworks to develop, test and improve digital solutions.
5. Poor execution due to lack of cross-fertilization and overall coordination: Many companies lack the in-house expertise needed to effectively manage a digital transformation initiative. In addition, they may lack adequate change management processes and tools.
6. Absence of a people-centric, digital-first culture: The first step in creating an organizational culture that empowers employees to adopt emerging technology starts with a successful digital transformation. Without that, subsequent AI initiatives are doomed to fail.

Connected systems lead to successful AI programs

To address these challenges, organizations must develop AI systems that connect across the enterprise as a mesh or fabric, enabling seamless collaboration. This also requires a mindset change from thinking of AI as a resource for individual departments to viewing it as a strategic goal for the entire organization.

Organizations must adopt a scalable architecture across the enterprise that is modular, holistic, scalable, risk-free, and flexible. This ensures a strong AI foundation with tools and processes that manage the end-to-end cycle from discovery to deployment, while enabling the organization to take full advantage of the benefits AI can provide and steadily shape their business for sustainable growth.

The fundamental dimensions in which AI can thrive include:

• Modular AI architectures provide the flexibility needed to tailor AI solutions to specific business needs. They also allow you to easily add or remove features as needed. Organizations can use modular AI to deploy it for specific use cases, resulting in a more open, focused and affordable solutione general AI system and strategy.
• Holistic AI architecture provides a comprehensive view of the business and a better understanding of how AI can be applied in all areas. This ensures that enterprises can deploy AI with confidence, as such an architecture provides assurance, support for ethical and legal issues, protection against reputational and financial damage, enhanced system transparency, and risk mitigation.
• A scalable data fabric causes it to build links, or talk, to all of an enterprise’s microservices or services. This acts as a common business language for the company, regardless of any underlying technologies, source systems or data formats, and can support millions of microdatabases, concurrently or virtualized, in a distributed, high-performance and consistent architecture.
• Making AI risk-free to manage reputational and performance risks. Analytic model interpretability, bias detection, and continuous performance monitoring must be built into different stages of the lifecycle, from development to deployment and use.
• Agile AI architecture is essential for businesses that need to quickly adapt to changing market conditions or customer needs, enabling them to quickly implement and implement AI solutions. Agile approaches have long been recognized for their ability to improve teamwork, break down silos, and enable decision-making and project management, among other things.

Overview

Successful digital transformation requires the integration of AI into all parts of a business, such as fabric and mesh. This will result in fundamental changes in the way the company operates and delivers value to customers. To take full advantage of the opportunities offered by digital transformation, companies need to have a clear understanding of what it entails. With this insight, they can make their digital transformation efforts effective by breaking down silo processes that can hinder AI integration and powerful digital transformation.

Balakrishna DR, popularly known as Bali, is the executive vice president and head of the AI ​​and automation unit at Infosys.