How to Successfully Implement Machine Learning in Business: Top Tips

How to Successfully Implement Machine Learning in Business: Top Tips | Artificial Intelligence and Machine Learning | Emeritus

In recent years, India has witnessed a remarkable shift toward a data-driven ecosystem, where Artificial Intelligence (AI) and Machine Learning (ML) are reshaping business operations. In fact, a 2022 report by UNESCO on artificial intelligence in education also highlights the anticipated growth of India’s AI market. According to the report, it is expected to reach ₹64,165 crores by 2025, with an impressive Compound Annual Growth Rate (CAGR) of 20.2%. Additionally, the AI software market is projected to reach ₹52,649 crore during the same period, maintaining a notable CAGR of 18.1%. Machine learning undoubtedly plays a pivotal role in driving this explosive growth in the AI domain. However, such rapid advancement also presents machine learning challenges that require attention to effectively implement this cutting-edge technology in businesses and harness its full potential.

In this blog, you’ll learn:

  1. How can Indian businesses effectively implement machine learning technologies?
  2. What are the major challenges faced by Indian businesses in adopting machine learning?
  3. Understanding machine learning  challenges faced by Indian businesses through data
  4. What strategies can help Indian businesses overcome the challenges of implementing machine learning technologies?
  5. How Can You Tackle the Machine Learning Challenges Through Emeritus Courses?

How Can Indian Businesses Effectively Implement Machine Learning Technologies?

Types of Machine Learning1. Establish a Clear Objective

First, have a clear vision of what you want to achieve by implementing machine learning in business operations. Define what is machine learning for the business. Then identify the specific problems or challenges the business is facing, and determine how it can provide viable solutions and add value. Also, consider the main challenges of machine learning when it comes to business and how you can overcome them.

2. Identify Your Data Requirements

Machine learning models rely heavily on data, making it essential for a business to define its requirements and ensure access to relevant and high-quality data. Additionally, invest in robust data collection and storage infrastructure, thorough data cleaning, and preprocessing to eliminate errors and inconsistencies.

3. Implement a Dedicated Team

Build a dedicated and skilled team that can work collaboratively across various departments. This cross-functional team should consist of IT, operations, business, sales, and marketing experts working closely. A dedicated team will not only help overcome machine learning challenges but also be instrumental in maximizing its benefits.

4. Create a Change Management Process

One of the common issues in machine learning implementation is the lack of adequate change management processes. Developing a comprehensive change management plan tailored to the organization is crucial to address this. Analyze and re-engineer current business processes based on the updated business model. Additionally, implement training programs covering mission objectives, product features, and the newly created business processes to foster support, awareness, efficiency, and successful implementation.

5. Monitor and Revalidate

Lastly, machine learning is a continuous process. Keep monitoring and revalidating the impact and challenges of machine learning on business operations. Look for any challenges of machine learning that may arise and seek opportunities to improve them.

ALSO READ: Unlocking the Potential of Industry 4.0: Opportunities and Challenges

What are the Major Challenges Faced by Indian Businesses in Adopting Machine Learning?

1. Data Unavailability

Data plays a critical role in training and improving machine learning algorithms. However, one of the main challenges of machine learning implementation faced by Indian businesses is the unavailability and inaccessibility of relevant data. Without sufficient data, developing accurate and effective models becomes difficult. Therefore, businesses must model and process the available data to align with the algorithms they intend to use.

2. Data Security

Another common machine learning challenge is the increasing volume of personal information being collected. Thus, safeguarding sensitive data is crucial for upholding data privacy and security. Businesses should not only implement strategies to protect against breaches and cyberattacks, but they should also encrypt and store confidential data separately to mitigate risks.

3. Lack of Skilled Talent

As mentioned earlier, implementing machine learning in business is a team effort, requiring a group of experts. This is why the demand for machine learning experts in India has grown exponentially and is expected to continue growing. However, there are still not enough machine learning experts to meet this rising demand. This lack of talent gap has become a key machine learning challenge India faces in ML adoption.

4. High Implementation Cost

Machine learning implementation can offer substantial benefits to businesses today. However, developing and deploying machine learning solutions can be quite expensive. This includes the cost of hiring experts and investing in sophisticated technology. Additionally, the Return on Investment (ROI) may not be immediate, posing a significant challenge for Indian businesses, particularly startups. Despite the potential long-term advantages, businesses require careful planning and strategic decision-making before moving forward with machine learning investments.

5. Time-Consuming

Lastly, time is one of the most common machine learning challenges. Businesses that implement machine learning often expect immediate results and profits. However, the reality is that implementing these solutions might take a year or more to show tangible outcomes. Thus,  patience and realistic expectations are essential in machine learning initiatives, as the process of data collection, model training, and optimization requires significant time and effort.

ALSO WATCH: Designing and Building AI Products with MIT xPRO Course Preview

Understanding Machine Learning Challenges Faced by Indian Businesses Through Data

Machine Learning ProjectsAccording to the Future of Jobs 2023 report by the World Economic Forum, which surveyed 803 companies worldwide, including India, nearly 75% of businesses are planning to adopt AI. However, AI is also projected to lead to high churn, with 50% of organizations expecting it to create job growth and 25% anticipating job losses. This dynamic nature of AI’s impact not only underscores the machine learning challenges faced by Indian businesses at present but also showcases the need for businesses to carefully plan and strategize their machine learning initiatives.

The report also reveals that AI machine learning specialists are in high demand, with a projected growth rate of 30% by 2027. Additionally, 42% of organizations prioritize training workers to effectively utilize AI and big data. The data revealed in this report underscores the key machine learning challenges that Indian businesses are currently facing and highlights the urgency for businesses to invest in upskilling and reskilling their workforce to ensure a smooth machine learning implementation.

What Strategies Can Help Indian Businesses Overcome the Challenges of Implementing Machine Learning Technologies?

  1. Develop a comprehensive data strategy for collection, storage, and preprocessing.
  2. Implement a well-defined change management process to align business processes with ML initiatives.
  3. Prioritize data security and privacy with robust cybersecurity measures.
  4. Collaborate with AI and ML solution providers or technology companies.
  5. Establish clear objectives for ML implementation to address specific problem areas.
  6. Continuously monitor and improve ML’s impact on business operations.
  7. Implement scalable and flexible ML solutions to accommodate growth.
  8. Stay updated with data and privacy regulations for compliance.
  9. Start with pilot projects to test feasibility and effectiveness before full-scale implementation.
  10. Invest in training and upskilling employees to build a skilled machine learning team.

ALSO READ: The Top 7 Machine Learning Courses in India You Should Know About

How Can You Tackle the Machine Learning Challenges Through Emeritus Courses?

In conclusion, India is experiencing a remarkable surge in the field of machine learning, promising great opportunities for businesses and professionals alike. However, to truly capitalize on this growth and unlock its full potential, it is crucial to address these machine learning challenges that come with this ever-evolving technology. One of the key ways you can harness the true potential of machine learning is through upskilling and reskilling. If you are looking to upskill in this field, explore AI and ML courses offered by Emeritus.

By Krati Joshi

Write to us at

About the Author

Senior Content Contributor, Emeritus Blog
Varun, a seasoned content creator with over 8 years of diverse experience, excels in crafting engaging content for various geographies and categories. Leveraging this expertise, he seamlessly translates complex concepts into enriching educational content for the EdTech domain. His keen understanding of research and life experiences helps him resonate with students and create fact-based content. He finds solace and inspiration in music, nurturing his creativity for content creation.
Read More About the Author

Learn more about building skills for the future. Sign up for our latest newsletter

Get insights from expert blogs, bite-sized videos, course updates & more with the Emeritus Newsletter.

Courses on Artificial Intelligence and Machine Learning Category