Artificial Intelligence & Machine Learning

Ensure Data Quality Before Embracing AI Models

Organizations need to pay more attention to their data management, even before they use that data to train large language models for business applications. Data security and privacy must also be addressed.

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For embracing AI models, organizations need to define use cases, prepare data, assess technology readiness, develop and validate models, and plan for ethical and legal considerations.

In an interview with ISMG, Ashish Srivastava, regional head - innovation, Insight Enterprises (Insight), offers advice on how organizations can successfully adopt AI models for business transformation.

Edited excerpts follow:

What issues do organizations currently encounter in managing their data? What steps should be taken to address them?

Good data is fundamental for data-driven transformation and accurate output. However, organizations face several issues with their data, including data quality, data silos, data security and data governance. With the increasing amount of data being collected and stored, data security is a major concern for organizations. Ensuring the privacy and security of a client’s data is critical.

Organizations need to take a holistic approach to data management. They need to establish a sound data strategy, invest in data infrastructure, including hardware and software, and incubate a data-driven culture where business strategies are driven through data and analytics. Lastly, it is important to have the right talent to manage their data, including data engineers, data scientists and data analysts.

What are some of the big business use cases for AI that you are seeing today?

Organizations across diverse industries are utilizing AI's capabilities to solve their business problems, thereby highlighting the technology's widespread presence. Application areas, including fraud detection, predictive maintenance, supply chain management optimization, healthcare, patient outcome evaluation and autonomous vehicles are some of the well-known and significant application cases.

A recent and most significant application of AI was evident in the facilitation of the moon landing project conducted by the Indian Space Research Organization, ISRO. AI played a pivotal role in the Chandrayaan-3 lunar landing mission, overseeing the entire 15-minute landing procedure. AI-driven sensors ensured the precise and secure landing of Chandrayaan-3. The successful completion of this intricate task required a substantial amount of predictive analytics. This analysis encompassed a wide array of variables, including temperature, potential hazards, and lunar topography, among other elements crucial for guiding the spacecraft's touchdown. The example vividly underscores AI's remarkable potential in effectively managing mission-critical assignments within the realm of space exploration.

What additional steps should be taken for/before embracing AI models?

For embracing AI models, organizations need to define use cases, prepare data, assess technology readiness, develop and validate models, and plan for ethical and legal considerations. By taking a comprehensive and strategic approach to AI implementation, organizations can ensure that they are ready to leverage AI models to drive business growth and innovation.

Do legacy and age-old architectures get in the way of implementing AI models? What is the workaround?

Yes, legacy and age-old architectures can get in the way of implementing AI models for organizations. To work around this, organizations can modernize their infrastructure, upgrade hardware and software, migrate to cloud-based solutions, or use AI platforms that are designed to work with legacy architectures. Similar approaches can help organizations overcome the limitations of legacy systems and take advantage of the benefits of AI.

How are you helping clients and adding value?

Insight is helping clients by leveraging its expertise in AI and machine learning to deliver innovative solutions that solve complex business problems. We work closely with our clients to understand their needs and develop customized solutions that deliver real value. Insight's innovation team enables the company to stay at the forefront of AI research and development and is always ready to help clients make the most of their data. By combining cutting-edge technology with a client-centric approach, Insight is helping clients achieve their business goals, drive growth and foster innovation.

You recently launched a service called Insight Lens for Gen AI. How will it help your clients leverage the power of generative AI? How are you using your in-house OpenAI center of excellence to enable this?

Insight Lens for GenAI will enable clients to leverage Insight's expertise in scalable infrastructure solutions, corporate applications, data platforms and technical design.

Insight's in-house OpenAI center of excellence is a dedicated team of experts focused on developing innovative AI solutions using the OpenAI platform. Insight has cross-geography teams who are driving innovations through AI. The team comprises data scientists, machine learning engineers, software developers and other AI experts who work together to develop cutting-edge AI models and algorithms.

Insight's IP development strategy is focused on leveraging the company's expertise in AI and machine learning to create proprietary software and algorithms that can be used to solve specific business problems. The company invests heavily in R&D to develop new AI models and algorithms and collaborates with leading universities and research institutions to stay at the forefront of AI research. Insight's IP development strategy is equally focused on protecting its intellectual property through patents and other legal means.

With more than two decades of experience, Srivastava is a cloud business leader enabling cloud innovations. He is skilled in digital transformation, vendor management, enterprise architecture and IT strategy.