By Arun Balasubramanian, Vice President & Managing Director – India & South Asia, UiPath
In today’s fast-paced and ever-changing business landscape, harnessing the power of AI isn’t just an option—it’s a necessity. AI has become a driving force behind innovation and efficiency across industries. Today, more businesses are investing in their AI-driven initiatives.
To put things into perspective, according to Stanford University’s annual AI Index report, India ranked fifth in terms of investments received by startups offering AI-based products and services last year. Total investments in AI startups in India stood at $3.24 billion in 2022, placing it ahead of South Korea, Germany, Canada and Australia, among others. Goldman Sachs Research estimates that AI investment could approach $100 billion in the U.S. and $200 billion globally by 2025.
The short lifespan of AI projects
AI projects definitely sound enticing. However, what many don’t know is that there is a pretty high failure rate on AI projects – as high as 83 percent to 92 percent in some instances. In many cases, as high as 40 percent of businesses. Failure can be attributed to a variety of factors –
Seeing AI projects like a functionality-driven project: This focuses on what insights need to be absorbed from the data in whatever state it is in, instead of commencing with an idea of what functionality it should hold.
Poorly defined goals: Business leaders need to set the objective right, one based on Return on Invest and Key Performance Indicators in mind to develop an AI tool that would make a difference in the growth of the business.
Overpromising AI’s capabilities: A common cause of failure is overpromising what AI can achieve, and the actual deployment being unable to meet those promises, leading to under-delivered projects. This mostly is due to a lack of understanding of AI’s limitations and setting realistic expectations and scope for project iterations.
Lack of quality and quantity of data: An AI solution is only as good as the data it is using. Often businesses deal with data that is not clean, kept in incompatible formats or locales. This leads to data scientists wasting a ton of their time untangling data instead of applying their knowledge to developing solutions.
Strategising for AI deployment
A good strategy is paramount for a successful AI deployment that delivers actual business value.
According to a recent Gartner report, the right way to start is by defining the criteria and key performance indicators that make use cases impactful and investing in those measurable business use cases that can be achieved in a tangible time frame.
In addition, involve a skilled team that possesses in-depth knowledge of analytics, IT, and business. Organise the delivery of the AI use case based on cross-functional teams that have the right mix of competencies – involving automation, AI, IT, and the relevant subject matter.
Data experts must be assigned to select, secure, and prepare the appropriate data linked to the defined use case. Based on the use case and the problems to be tackled, determine the most apt AI technique.
Lastly, businesses must keep the AI technical debt at a minimum and continue learning within the organisation by building an AI expertise organisational structure designed for knowledge transfer and problem-solving.
Specialised AI for complex business processes
What can additionally help in the successful execution of AI deployment is automating more complex business processes that go beyond mundane tasks. Specialised AI, pre-built solutions trained on a company’s data, can understand screens, mine tasks, process documents, and utilise unique and proprietary data sets within enterprise workflows.
Trained for specific tasks in mind: An evolution of Robotic Process Automation, Specialised AI is essentially a process enhancement opportunity, capable of transforming paper documents into automation-powered apps with the click of a button. The AI is trained for a specific or limited set of tasks instead of a broad range of capabilities like Gen AI. These can execute much of the tasks in business processes and help people work smarter with automation.
Specialised AI can be trained with a customer’s data and optimised for their specific needs, resulting in prompt, accurate, and tailored solutions that are cost-effective to operate and that deliver high-value outcomes.
To put things simply, Specialised AI results in swift, precise, and cost-effective tailored solutions that deliver high-value outcomes.
Works in tandem with Gen AI: Today, capable platforms exist that combine the understanding of Specialised AI with the intelligence of Generative AI. This allows them to observe work happen, understand what is being done, and automate it end-to-end within the platform.
By combining of both types of AI, organisations can realise incredible business outcomes. It solves unique business problems that neither can when functioning alone.
Attaining the winning edge with Specialised AI
The road to a winning AI strategy can be challenging, but the benefits are undeniable. Businesses that invest in a well-structured AI strategy, learn from the experiences of others, and stay informed about the evolving landscape will be better positioned for success in a world increasingly intertwined with AI.
Harnessing the power of automation in the form of Specialised AI could further help alleviate numerous challenges along the way towards creating AI solutions that offer the most business value throughout.
