AI has transformed industries across the globe, providing businesses with new avenues for improving productivity and keeping customers engaged. Generative AI applications such as ChatGPT, GitHub Copilot and others have captured the imagination of people thanks to their broad utility. The economic impact of AI adoption is tremendous, with a McKinsey report estimating that gen-AI could add between Rs. 225 trillion and Rs. 380 trillion to the global economy.
Most organisations, including tech startups, however, face barriers on the path to successful AI implementation. For Indian organisations, especially small and medium enterprises (SMEs), the issues can even seem bigger at times. The Indian market is linked with unique problems of data quality, compliance, and infrastructure pertaining to technology. For a company that is planning to implement AI for customer service, marketing, or operations, it is crucial to learn about and overcome the issues in order to gain a return on investment.
1. Very high startup expenditure and volatile ROI
The key issue with corporations when investing in AI is that it entails huge initial outlays. Establishing and rolling out AI technologies tends to require enormous investments in manpower, software, and equipment. Established businesses may possibly undertake such expenditure, but not-so-established players may not have the capacity to provide the support for it without promptly seeing results.
Secondly, the investment return on AI programs can be volatile. AI technologies need time to take lessons, adapt, and provide concrete results. Unlike other investments, whose return could be guaranteed, AI outputs rely on data quality and deployment techniques. Businesses have to adopt a long-term perspective and be prepared to accept initial setbacks before AI solutions begin delivering value.
2. Deficiency of skilled talent
AI technology needs experts who specialise in domains like data science, machine learning, software development, and AI ethics. These experts are few in number everywhere in the world, and India is no exception. In India, the demand for Artificial Intelligence (AI) professionals is projected to more than double, increasing from approximately 6 to 6.5 lakh in 2022 to over 12.5 lakh by 2027. However, as of now, most businesses struggle to acquire and retain the right talent to support their AI projects. They have to spend money on employee training and re-skilling the available talent pool in an attempt to make up for such talent deficiencies.
3. Data availability and quality
AI systems are mostly dependent on data to decide and learn. Businesses, however, are usually plagued by the availability and quality of data. Bad data, in the form of incomplete, biased, or wrong data, can create inferior AI predictions and outcomes and, as a result, impact business performance.
For Indian companies, the availability of data can be especially challenging in view of heterogeneous market segments and different consumer behaviours. Integration and collection of data from different regions and segments demand strong data management systems.
4. Integration with current systems
Another key problem is to include AI technologies in the existing business infrastructure. Older infrastructure is prevalent in most companies that may be inoperable on newer AI technology. Backward integration so that AI becomes a part would be very costly and time-intensive.
For example, an e-commerce marketplace that wishes to exploit AI for customer support activities may be discouraged by legacy systems that are not compatible with AI-powered chatbots or predictive analytics tools. This can be avoided by the company by strengthening its IT infrastructure and implementing scalable solutions that enable them to integrate AI easily.
5. Handling change and employee resistance
Implementation of AI is prone to organisational changes, such as changes in workflows, job titles, and decision-making protocols. Such organisational changes are normally opposed by the employees, especially if they believe that their work will be usurped by automation.
In order to offset this, a learning and adaptation culture needs to be developed by organisations. Employees should be trained in AI technology and told how these tools will enhance their work rather than put them out of jobs.
6. Providing transparency and accountability
Deep learning-based AI models tend, at times, to be “black boxes” in the sense that it is difficult to determine how they come to certain conclusions. This lack of transparency may prove to be a huge problem, particularly for sectors like finance, health, and the law, where decision-making needs to be transparent and auditable.
For example, a bank that uses AI to sanction loans has to guarantee that the decision made by its AI model is understandable and fair. In the case of rejecting a loan, the company can provide a simple and transparent reason for rejection. Explainable AI (XAI) solutions will help companies maintain transparency and develop trust among stakeholders.
Conclusion
Although investments in AI are of tremendous business growth and innovation potential, they are also accompanied by a set of challenges that need to be addressed in the right manner. Ranging from initial capital outlay and data quality problems to cybersecurity threats, companies need to address AI with a strategic and informed approach. For Indian businesses such as NBFCs and online marketplaces, the key to overcoming the above challenges is through embracing best practices, talent investments, and creating a culture of transparency. Thus, not only are organisations empowered to manage the intricacies of AI deployment but can also leverage the full potential of AI to success.
