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Digital transformation has multiple dimensions and complexities, which are sometimes lost on organizations undertaking it. The recipe for success lies in rethinking the processes and the organizational structure to generate maximum value from the technology framework — something many enterprises continue to struggle with.
A 2020 study by Boston Consulting Group found that about 70% of digital transformation projects fall short of their goals even when the priorities are clearly mapped and leadership is aligned. Compounding the challenge is the need to bring AI into the organization that is transforming. AI is everywhere today and promises great returns from customer experience and organizational efficiency. Not investing in AI is a non-starter now when a digital transformation effort is begun, but the investment can feel like an insurmountable task. Why is this?
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Gaps in the digital transformation roadmap can hinder success of AI initiatives
The factors that lead to failed digital transformation initiatives also act as roadblocks to the success of AI initiatives. These include:
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- Identifying the right problems to solve: Without proper project design and outside intervention, identifying the right problem and the right approach to solve it is unbelievably difficult. This is where a poorly executed digital strategy or a faulty transformation roadmap will act as a bottleneck for AI success: The underlying data strategy was not aligned to the organization’s unique needs in the first place.
- Lack of an overarching data strategy: Companies must have a clear idea of what kind of data they need for digital transformation. Otherwise, they risk investing in improper tech stacks. A proper data infrastructure and strategy form the foundation upon which emerging technologies are built, and formulation of an AI strategy is created on top of it.
- Lack of integration across verticals and units: Too often, digital transformation projects are siloed within individual departments instead of integrated across the company. This can lead to duplicate efforts and wasted resources. Siloing prevents data and insights to move freely across departments, which can make deploying AI challenging. The fact that many AI programmers are led by specific departments rather than centrally managed makes the situation worse. As a result, businesses frequently depend on a small number of vendors for their AI requirements, which can result in vendor lock-in and limited flexibility when using AI systems. Silos can make employees resistant to change. Professionals may prefer their work surroundings within the silo, making them resistant to regulations that would disrupt their environment.
- Lack of CoEs and best practices, proper frameworks and approaches: A poor digital transformation initiative does not create the proper system of best practices, Centers of Excellence and frameworks to develop, test and enhance digital solutions.
- Poor execution due to lack of cross-pollination and overall coordination: Many companies lack the internal expertise needed to effectively manage a digital transformation initiative. Additionally, they may lack adequate change management processes and tools.
- Absence of a human-centric, digital-first culture: The first step in creating an organizational culture that empowers employees to adopt emerging technology begins with a successful digital transformation. If that is not in place, subsequent AI initiatives are doomed to failure.
Connected systems lead to successful AI programs
To overcome these challenges, organizations need to develop AI systems that are connected across the enterprise like a mesh or fabric, ensuring seamless collaboration. This will also require a shift in mindset from thinking about AI as a tool for individual departments to considering it as a strategic objective for the entire organization.
Organizations need to adopt a scalable architecture across the enterprise, one that’s modular, holistic, scalable, de-risked and agile. This will provide a strong AI foundation with tools and processes that manage the end-to-end discover-to-implementation cycle while allowing the organization to take full advantage of the benefits AI can offer and steadily shape their business for lasting growth.
The fundamental dimensions in which AI can flourish include:
- Modular AI architectures provide the flexibility needed to tailor AI solutions to specific business needs. They also make it possible to easily add or remove features as required. Organizations can use modular AI to deploy it for specific use cases, resulting in a more open, focused, and affordable overall AI system and strategy.
- Holistic AI architecture provides a comprehensive view of the business and a deeper understanding of how AI can be applied across all areas. This ensures that enterprises can adopt AI with confidence, as such an architecture provides assurance, support on ethical and legal issues, protection from reputational and financial damage, improved transparency of the system, and risk mitigation.
- A scalable data fabric ensures that it builds links, or talks, to all of an enterprise’s microservices or services. This acts as a common business language for the company irrespective of any underlying technologies, source systems or data formats, and can support millions of micro databases, concurrent or virtualized, in a distributed, high-performing and consistent architecture.
- De-risk AI to manage reputational and performance risks. Analytics model interpretability, bias detection and continuous performance monitoring should be built into various stages of the lifecycle, from development to deployment and use.
- Agile AI architecture is essential for companies that need to quickly adapt to changing market conditions or customer needs so they can rapidly deploy and implement AI solutions. Agile approaches have long been recognized for their capacity to improve teamwork, dismantle silos and empower decision-making and project management, among other things.
Successful digital transformation requires the integration of AI into all areas of a business, like a fabric and mesh. This will result in fundamental changes to how the business operates and delivers value to customers. To fully capitalize on the opportunities presented by digital transformation, businesses need to have a clear understanding of what it entails. With this understanding, they can make their digital transformation efforts effective by breaking down siloed processes that can inhibit AI integration and a powerful digital transformation.
Balakrishna DR, popularly known as Bali, is the executive vice president and head of the AI and automation unit at Infosys.