Master Data Management

The future is generative: Preparing your data for gen AI-driven innovation

While the scope and future of generative AI are still unclear, its promise of automation and innovation is set to disrupt traditional business models for good. Businesses that effectively harness gen AI will experience unprecedented growth by unlocking higher efficiency, creativity, and scalability than ever before*.

The potential use cases of generative AI are mind-boggling. By tailoring generative AI for business use, organisations can automate tasks, build personalised products, enhance marketing operations, forecast demand and supply, minimise risk, and make data-driven decisions.

While this makes a strong case for AI adoption, it is crucial to note that generative AI is not a magical solution for inefficiencies and knowledge gaps. Businesses must approach it with a clear understanding of its capabilities, requirements, and implementation challenges.

Generative AI for business

Generative AI has myriad use cases across all verticals. These currently focus on marketing, customer service, software development, and business intelligence. However, industry leaders see several creative and non-traditional use cases in the offing, including adaptable product design, virtual prototyping, simulations, and creative problem-solving.

Given the spectrum of possibilities, organisations must identify their unique needs and land on the right AI innovations for sustained growth. Setting use case priorities will help businesses to -

• align their data, technology, and strategy with these use cases
• ensure the right blend of skill sets, resources, and capital allocation
• enable collaboration among IT teams, domain experts, and business stakeholders
• establish the necessary guardrails and governance practices

Business use of generative AI includes vertical solutions across diverse industries such as healthcare, agriculture, insurance, architecture, and life sciences. However, developing domain-specific AI tools involves more intense data preparation processes compared to mainstream products such as ChatGPT and DALL-E2.

Preparing your data for AI innovation

The success of generative AI models depends on the quantity, quality, and handling of training data. Below are seven data requirements to ensure that your enterprise data is fit for modelling and deployment:

Data collection

Depending on your AI goals, collect large amounts of data from multiple touchpoints. This will help you get deeper and more relevant insights into the issue that you intend to resolve with AI.

Data quality

Most AI initiatives fail due to poor-quality data. AI innovation is successful only if the training data is complete, consistent, and accurate. Use techniques such as data profiling, data cleansing, and validation to ensure reliable outcomes and build efficient, unbiased AI models.

Data integration

Siloed data is among the topmost challenges in AI deployment across organisations. Integrating disparate datasets into a unified repository is vital to building an analytics layer and facilitating AI innovation in sectors like healthcare. Use tools such as data extraction, data transformation, and synchronisation to integrate data from diverse sources.

Feature engineering

This step includes selecting, extracting, and transforming raw data attributes into meaningful features for training ML models.

Data labelling

Segmenting data and classifying it with tags or annotations makes it understandable for AI/ML algorithms. This step is vital in enterprise use cases where the AI innovation is trained for specific tasks and designed for continual learning.

Data augmentation

For business use cases, generative AI can yield better outcomes if the existing image or text data is augmented through manipulation, transformation, and synthesis. Data augmentation helps minimise bias, increase data diversity, and improve model generalisation.

Data privacy and governance

Deploying generative AI in a business context requires strict security measures and active governance. These practices help to safeguard data quality and ensure that the AI model functions within ethical boundaries.

Ensuring data security is a continuous process and a shared responsibility across the organisation. It involves tools such as -

• Data encryption and anonymisation
• Classification of sensitive data  
• Regulatory compliance
• Governance measures, including data stewardship and data lineage
• Continuous audits and documentation of all data preparation processes to ensure data quality and sustained model performance

Best Practices for a generative future

While first movers can expect significant competitive advantage, a sub-optimal data strategy may result in a loss of revenue or diminished ROI from AI initiatives. Below are three best practices to optimise your organisation’s generative AI investments:

Take your data to the cloud

Cloud migration provides you with cost-effective data integration and storage solutions, sustainable GPU computing, and the high-speed networking necessary for developing and deploying AI innovations.

Build an effective technology stack

As generative AI gets tailored for highly specific use cases, enterprise data and the AI innovation it fosters become strategic assets that create differentiating value for an organisation.
It is, therefore, crucial to design a tech stack with the right data platform, a relevant foundation model, security tools, data management technologies, and a strong governance infrastructure.

Have a human in the loop

Know that generative AI is not the driver but an enabler. A ‘human in the loop’ is necessary to work the AI and drive sustained outcomes from AI innovations. Hire prompt engineers who can map the AI’s capabilities to your business goals. Invest in continuous employee upskilling and cross-skilling to minimise operational lapses.

How can Infosys BPM help you prepare for the future of generative AI?

The Infosys Generative AI Business Operations Platform offers ready-to-use services, solutions, and design platforms to enable your organisation’s generative evolution. The platform has helped facilitate over 12,000 generative AI use cases across diverse industries, including finance and accounting, legal, and procurement.

Know how Infosys BPM can help you augment generative ai for business processes.