The Need for Vertical Orientation
While GenAI tools such as ChatGPT and Code generation tools (viz. GitHub Copilot, Code Whisperer, Star Coder, etc.) have general horizontal capabilities, the strategic focus for enterprises today should be on vertical GenAI models. These models, tailored for specific segments, with specialized algorithms designed for their use cases, are crucial for a targeted and effective application of GenAI. This strategic approach ensures a faster return on investment, making technology leaders feel informed and in control of their GenAI strategy.
Customization is at the core of vertical GenAI solutions, built for specific industry applications from the ground up. They are based on high-value proprietary and third-party datasets for increased accuracy compared to general GenAI apps and are typically suitable for answering generic queries. Vertical AI solutions are closely integrated with existing business systems and tools to optimize workflows instead of disrupting them. Ahead of enhancing productivity across domains, they drive end-to-end revamping of areas such as customer service, marketing, data analytics, software engineering, finance, and talent management.
Investing in GenAI for Measurable Business Transformation Value
Like all new-age digital transformation solutions, GenAI also comes with a technical debt faster than other technology solutions. In the case of GenAI, the adoption of enhanced capabilities and the need to tailor models for specific business goals can increase this debt. Furthermore, the multi-modal complexity of AI, with large language models (LLMs) working can introduce new challenges and potentially increase an organization's technical debt.
Investing in GenAI impacts operational efficiency, customer engagement and data analysis, product development. We all agree that GenAI has the potential to lead significant business transformations, but to assess the value generated by GenAI, businesses are in process of establishing clear metrics. The best practice to measure the outcome is through defining specific KPIs in advance and then comparing them with the specific goals set.
The Birlasoft Approach to Invest in GenAI and Maximize its Business Transformative Value
Fortunately, some strategies help manage technical debt and improve the returns from investments in GenAI. Birlasoft advises technology leaders to focus on the following points while strategizing a plan for GenAI applications in their organization’s business functions:
- Alignment with Business Goals: GenAI is not yet the solution for every situation while it has a significant impact across all industries. We are in the early stages of what is possible, and it has potential to reimagine the business processes across enterprise. CIOs should collaborate with business leaders to make them aware what is GenAI and what is not GenAI. Businesses are only at the start of reaping the full potential of GenAI. For instance, while customer engagement once implied having chatbots and call centre training in place, it has now evolved to reimagine the domain and undertake coordinated outreach campaigns for lead generation.
- Golden use cases: The key is to identify the golden use cases that drive competitive advantage or result in improving productivity or end to end process reimagination that can seamlessly align with organizational goals. CIOs must map the AI initiatives to specific business outcomes such as improved operational efficiency, better customer experience, faster speed-to-market, and revenue growth.
- Data Quality and Governance: Focusing on high-quality data management practices is crucial to maximizing the effectiveness of GenAI models. These tools generate reliable results with clean, accurate, and well-organized data to learn from. Poor data quality would lead to biased or irrelevant outcomes, misguiding business decisions. Governance is necessary for ethical data usage in compliance with regulations. Robust governance frameworks maintain data integrity, security, and traceability, enabling models to meet organizational goals.
- Deploy Agile Methodologies: Use iterative approaches for rapid development and feedback. Continuously track AI effectiveness against predefined KPIs to monitor the performance. Foster Cross-Functional Collaboration and ensure alignment between technical, business, and operational teams. It is critical that regularly report outcomes and successes to stakeholders.
- Scalability: AI models require significant computational resources, and as data volumes grow, the demands on infrastructure also get heavier. A scalable approach allows CIOs to adapt GenAI solutions to evolving needs without excessive costs or performance bottlenecks. They can leverage cloud infrastructure for storage and processing power to handle increasing data volumes and model complexity. Scalable AI solutions can be seamlessly expanded across departments or use cases to drive continuous innovation and maximize return on investment as the organization grows.
- Talent Acquisition: A company needs to invest in training existing staff or hire skilled professionals to deploy GenAI solutions efficiently. Staff with expertise in this digital transformation domain bridge the gap between complex AI technologies and their practical business applications, facilitating smooth integration and optimal performance. And upskilling internal teams fosters a culture of innovation while making it easier to align AI capabilities with strategic objectives. A combination of technical proficiency and business acumen for AI-driven solutions delivers the value stakeholders expect from them.
- Ethical Considerations: Ethical considerations need a CIO's attention to eliminate bias, safeguard data privacy, and maintain transparency in decision-making. AI algorithms can inadvertently perpetuate biases in training data, leading to unfair or inaccurate outcomes. Choosing diverse datasets and deploying bias-detection mechanisms help to reduce the risk. For data privacy, CIOs should encrypt sensitive details and ensure that their AI systems adhere to applicable regulations such as GDPR and HIPAA. Transparency in decision-making processes strengthens trust as AI users understand how the systems generate results. Finally, organizations avoid reputational risks by embedding ethical practices into AI deployment and promoting responsible, accountable AI usage.
- Clear policies that address the limitations of GenAI: GenAI presents critical risks for which companies will need to be prepared. CIOs need to prepare for risk through clear policies and training that define roles and responsibilities on how to use GenAI with a measure of confidence. They should ensure the organization adapts responsible AI norms for long term risk mitigation.
Business Transformation Value of GenAI
When investments in GenAI are made systematically, the value delivered by the technology grows in the forms of:
1. Enhanced Innovation and Product Development
GenAI expedites prototyping to design personalized solutions and add new product features that respond efficiently to market trends and customer needs. With scalable AI models fueled by high-quality data, companies can experiment with innovative ideas while reducing the time and resources needed for product testing and optimization. AI-powered insights enhance their product development process, helping them stay competitive and continually evolve their offerings, pushing product innovation and market differentiation boundaries.
2. Cost Efficiency
GenAI reduces the need for manual intervention and operational costs by automating repetitive tasks and optimizing processes—such as content generation, customer service, and supply chain management. By keeping their solutions aligned to business goals and scalable, CTOs maximize AI’s cost-efficiency benefits. This efficiency allows businesses to allocate resources more effectively, improving overall profitability. Strong data governance ensures AI systems run more accurately and avoid costly errors or resource misallocations.
3. Improved Customer Experience
GenAI personalizes interactions with customers and helps them have engaging experiences by analyzing data and generating tailored content or recommendations. AI algorithms can anticipate customer preferences to refine service continuously. Moreover, the solutions designed with ethical considerations keep interactions fair and reliable. This enhances customer satisfaction and loyalty to drive revenue growth and get social media or word-of-mouth recommendations.
4. Marketing Transformation
Marketing has evolved from creative content generation to highly targeted, hyper-personalized campaigns driven by GenAI. Now, organizations can engage customers through “segments of one,” offering personalized experiences tailored to individual preferences. GenAI enables dynamic content generation, automated segmentation, and data-driven insights, allowing marketing teams to build more impactful and engaging strategies.
5. Finance Transformation
Finance has transitioned from primarily focusing on business documentation to becoming a market intelligence and strategic synthesis powerhouse. With GenAI, CIOs can help finance teams quickly identify M&A targets, analyze market trends, and provide insights that shape business strategy. AI-driven tools help automate routine tasks while elevating finance to a more strategic role within the organization.
6. Impact on Overall Performance
Integrating GenAI into business functions improves a company’s overall performance by enhancing productivity, fostering innovation, driving better customer experience, and creating a competitive advantage in the marketplace.
Companies implementing GenAI strategies can boost their financial outcomes and market share. Business performance metrics also improve when AI supports decision-making considering possible risks.
The Security Aspects of GenAI
As they scale GenAI and LLM deployments, CIOs must remember that cybersecurity is the cornerstone of responsible AI development. Establishing robust governance frameworks, enforcing encryption, and having zero-trust security models are vital to safeguarding digital assets and upholding stakeholder trust. AI models must also be regularly audited for vulnerabilities and biases that could manipulate output.
While global data privacy regulations and GenAI usage still have grey areas, organizations must recognize the importance of compliant deployment pipelines. In addition to using secure APIs, containers, and orchestration tools, they must ensure continuous monitoring and anomaly detection to prevent unauthorized access to data. Moreover, they must invest in resources, training, and awareness and implement best practices to mitigate risks from using GenAI.
