Six principles for success with AI and analytics
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Six principles for success with AI and analytics

Discover the formula for achieving digital transformation

Artificial intelligence (AI) and analytics are critical for unlocking insights and creating better ways of working across the enterprise. Despite their importance, many leaders struggle to integrate them into business processes – leaving their organizations vulnerable to missed opportunities and ineffective operations.

Recognizing this gap, we’ve developed six principles for using AI and analytics at scale.

1. Identify the right projects

With AI and analytics tools, you can analyze mountains of business data to uncover trends that may not be obvious. With these insights, enterprise leaders can evaluate business outcomes against value generated, implementation complexity, and risk, helping them identify the appropriate projects for technologies like generative AI (gen AI). This process also enables organizations to allocate resources efficiently, optimize operations, and identify new growth opportunities.

To select the right projects, we suggest creating a center of excellence (CoE) to:

  • Develop expertise in AI and analytics methodologies
  • Analyze infrastructure, process, and budget requirements to provide a clear view of costs and benefits
  • Democratize ideas while limiting production to the most promising use cases

At Genpact, we've established a program for employees to pitch ideas and AI applications for internal and client use.

2. Embed AI and analytics into processes

By seamlessly integrating data analytics and AI into existing processes and workflows, you can empower decision-makers with real-time insights and data-driven recommendations. To achieve this, we suggest taking a strategic approach that includes the following steps:

  • Define your objectives and specific business problems
  • Assess data quality and availability to ensure you have reliable sources
  • Choose data analysis tools that align with your goals and data requirements
  • Set up data pipelines to streamline data flow
  • Develop user-friendly dashboards and customized reports for decision-makers
  • Incorporate analytics capabilities into applications and automate data-driven tasks
  • Train employees to enhance their data literacy and analytical skills
  • Monitor performance to optimize analytics integration continuously

Case study

Transforming customer service

A media conglomerate struggled to analyze customer feedback and data at scale. We used generative AI, natural language processing augmentation, and other AI and machine learning (ML) technologies to create a superintelligent assistant for chat agents. This model analyzes customer queries in real-time, looks for upsell opportunities, and provides agents with a response recommendation. Using this model, employees have seen significant improvements in customer satisfaction, vendor relationships, and sales.

3. Prioritize data governance and the responsible use of AI

Building a solid data foundation goes beyond data management. It requires integrating people, processes, data, and technology to harness the full potential of AI and analytics. In tandem with this, you must also establish sound business practices for master data governance, ethics, and compliance – otherwise, the consequences can be severe.

To ensure you develop AI solutions that are fair, trustworthy, and accountable, your CoE should act as an ethics board. This diverse team – made up of people with different experiences, perspectives, and skills – can oversee AI development from start to finish, catch biases up front, and prevent issues down the road.

This strategy is essential for technologies like generative AI, where hallucinations and unintended biases may arise. You can use our responsible AI framework if you don’t know where to start.

Case study

A global bank puts responsible AI into practice

A bank wanted to streamline loan approvals while removing potential biases. First, Genpact enhanced the bank's data management and reporting systems. Then, we applied our responsible AI framework. We also improved transparency to show the data behind these decisions. Finally, we developed a monitoring system to alert the AI ethics board of any issues. Now, the bank is deploying similar AI ethics models across the organization.

4. Establish a robust technical architecture

Your technical architecture should provide the foundation for the seamless integration, efficient processing, and reliable deployment of AI and advanced analytics. To build an effective tech stack, consider the following components:

  • Scalable cloud-based storage to handle the large volumes of data required for AI applications
  • Data pipelines to ensure the continuous flow of high-quality data to feed AI models
  • A system for managing and deploying AI models that includes version control, model monitoring, and automated deployment processes
  • Integration with existing IT infrastructure and systems to enable seamless collaboration between AI solutions and business processes

Case study

From outdated to cutting edge: A data and analytics solution for Heineken

Heineken, a multinational brewing company, struggled with disconnected data and inefficiencies due to rapid global growth and acquisitions. By embedding Genpact's PowerMe platform, we provided a 360-degree view of data lineage and improved data accuracy and compliance. This strategy enabled swift cloud migration, increased productivity, improved decision-making, and streamlined digital transformation.

5. Enable a scalable operating model

To develop an operating model that drives innovation, operational efficiencies, and business outcomes across all organizational functions, we recommend you:

  • Standardize processes and workflows for data collection, analysis, and decision-making
  • Embed automation and orchestration tools to streamline repetitive tasks and workflows
  • Embrace cloud-based infrastructure and services for flexibility and scalability
  • Develop cross-functional teams that can adapt quickly to changing business needs
  • Upskill and reskill employees to use advanced analytics and AI technologies effectively

Case study

Moving from siloed systems to seamless data flows in retail

A global retailer had 20 nonintegrated systems processing 6 million invoices across its stores and warehouses, leading to supplier disputes – over 70% of which ended in refunds. We developed a procure-to-pay data fabric as part of a data-on-cloud strategy. Seamless data flows – enhanced with ML and automation – now match the right invoices to the right receipts. The improvements in employee and supplier experiences have reduced disputes by 40% to 50%.

6. Nurture talent

To thrive in the digital age, organizations must nurture new skills for all employees – rather than relying on a limited group of individuals for AI and analytics initiatives.

Organizations can democratize access to information, enhance decision-making, and foster a culture of continuous innovation and collaboration by equipping all employees with the right data science skills and tools.

The path to data-driven success involves inspiring employees, empowering and training them, focusing on customers, and building business resilience. In doing so, organizations can harness diverse skill sets and experiences to drive sustainable growth and competitive advantage.

As data and analytics skills become integral to every function, we’ve launched DataBridge, a program that aims to upskill more than 100,000 Genpact employees in data science techniques.

The path forward

The journey toward digital transformation goes beyond embracing the latest technology. To thrive, enterprise leaders must also prioritize data governance, build robust technical architectures, and nurture a workforce equipped with diverse skills. By adopting these principles, organizations can forge ahead with confidence, drive innovation, and achieve lasting success in the digital age.