Leveraging Generative AI for Operational Excellence

The potential of generative AI is reshaping industries worldwide, presenting valuable opportunities for operations managers to elevate productivity and streamline workflows.

Across sectors, leaders are increasingly looking for AI-driven solutions to achieve operational efficiencies, enhance resource management, and maintain a competitive edge. Technology leaders like OpenAI, Google, and Microsoft continue to push advancements in AI, providing tools that promise to transform data analysis, automate processes, and support decision-making at unprecedented scales.

But for operations managers, the challenge is knowing where to start. Which use cases yield tangible returns, and how can generative AI best be implemented to optimise processes effectively? Let’s explore practical applications in key sectors, drawing on insights from early adopters to guide your team’s approach to scaling generative AI.

Covered in this article

Improving Knowledge Workflows
Automating Engineering and Data Processes
Democratising Data Access Across the Organisation
Enhancing Customer Support with AI-driven Solutions
Three Steps to Implement Generative AI Successfully
Conclusion
FAQs

Improving Knowledge Workflows

For many businesses, AI can relieve operational bottlenecks by automating repetitive, time-consuming tasks, particularly those involving data-intensive functions. Generative AI models, like Large Language Models (LLMs), excel in analysing unstructured data, which can streamline workflows and reduce manual workloads for knowledge workers.

Key Industry Use Cases

1. Legal Firms
   AI technology is transforming legal workflows by:

   • Automating compliance and regulatory monitoring, ensuring up-to-date client advisories.
   • Streamlining document creation, from drafting contracts to flagging potential risks.
   • Enhancing research efficiency by summarising case law, statutes, and relevant legal publications.

   Example:  London-based law firm Macfarlanes leverages AI tool Harvey to support research, document analysis, and draft preparation, allowing human reviewers to focus on high-value tasks.

2. Financial Services
   In financial institutions, generative AI improves operational resilience, with applications like:

   • Automating routine accounting and financial reporting tasks.
   • Enabling sophisticated fraud detection and compliance monitoring.
   • Supporting advisors with quick access to in-house research and client data.

   Example: Morgan Stanley’s AI-powered assistant enables financial advisors to instantly retrieve insights on market trends and internal recommendations, improving client service efficiency.

3. Sales and Marketing
   For sales and marketing teams, AI enhances outreach and personalisation, by:

   • Generating content tailored to client interactions and CRM data.
   • Automating lead qualification and scoring to target high-value prospects effectively.
   • Summarising sales calls and video meetings to inform post-interaction strategies.

   Example: Gong’s AI-driven call summaries guide sales teams on effective follow-up actions, optimising resource use in customer engagement.

Automating Engineering and Data Processes

In data engineering and IT operations, generative AI tools reduce manual coding and debugging, enabling teams to focus on innovation rather than maintenance. Operations managers are adopting AI solutions to eliminate repetitive tasks and improve speed-to-insight.

Key Benefits for Engineering Teams

• Code Generation and Debugging
  AI can help engineers by generating code snippets, debugging issues, and suggesting optimisations, reducing time spent on routine troubleshooting.

• Synthetic Data Creation
  Engineers can generate vast amounts of synthetic data, enabling robust testing of applications without relying on sensitive customer data.

• Streamlined Documentation
  AI tools can produce detailed documentation of code and systems, ensuring comprehensive project tracking and knowledge sharing across teams.

Example: GitHub Copilot, integrated with OpenAI models, offers code suggestions and debugging assistance, streamlining the development process across industries.

Here’s an infographic on Efficiency Gains from Generative AI Across Industries:

Democratising Data Access Across the Organisation

One of the most significant advantages of generative AI is its ability to make data accessible to non-technical employees, empowering teams throughout the company to make data-driven decisions. With AI’s natural language capabilities, employees can query data without SQL knowledge, opening new possibilities for cross-functional insights and self-service analytics.

Example: Databricks’ LakehouseIQ allows users to query their data intuitively using plain language, enabling faster insights and reduced dependency on data teams.

Enhancing Customer Support with AI-driven Solutions

Customer support teams benefit greatly from generative AI, which improves response times and personalises interactions. By using semantic search and contextual understanding, AI-powered support systems can reference internal data to help agents resolve issues quickly.

Example: Vimeo’s AI help desk prototype enhances customer support by rephrasing inquiries, searching relevant support articles, and providing summarised responses, significantly improving response accuracy and speed.

Three Steps to Implement Generative AI Successfully

To maximise the potential of generative AI in operations, here are essential steps for successful implementation:

1. Build a Supportive Tech Stack

• Vector Databases: Essential for AI-driven applications that require a semantic understanding of data relationships.
• Custom Model Fine-tuning: Tailor models to specific organisational needs for higher accuracy in predictions and outputs.
• Unstructured Data Processing: Tools like Kafka and Spark help manage high volumes of unstructured data, amplifying AI’s value.

2. Allocate Skilled Resources

Generative AI implementation requires cross-functional expertise. Designate a “tiger team” of skilled data engineers and analysts to oversee initial AI deployments, ensuring operational alignment and sustainable growth.

3. Prioritise Data Quality

Data quality is foundational for AI efficacy. Implement data governance protocols, monitor AI outputs, and conduct regular audits to avoid the pitfalls of poor-quality data impacting decision-making.

Conclusion

Generative AI offers operations managers powerful tools to drive efficiency, streamline processes, and empower employees with data-driven insights. As you explore its potential, focus on practical, scalable applications that align with your operational goals and resources. By approaching AI with strategic foresight, your team can unlock its transformative impact across all facets of business.

Explore how Velocity can support your AI journey with industry-leading insights and tailored solutions. Discover actionable strategies to enhance operational productivity on our blog, and connect with us to see how we can accelerate your organisation’s digital transformation.

FAQs

1. What is generative AI, and why is it relevant for operations managers?

Generative AI uses advanced machine learning models to produce outputs from data, including text, code, or visuals. It enables operations managers to streamline workflows, automate routine tasks, and access data insights more efficiently.

2. How can generative AI improve knowledge workflows in my organisation?

Generative AI can summarise large data sets, automate document drafting, and assist with data-driven insights, helping knowledge workers manage their workloads and focus on high-value tasks.

3. What are some AI-driven tools for engineering and data teams?

Tools like GitHub Copilot and CodeWhisperer offer automated code suggestions, debugging assistance, and documentation support, which save time and reduce errors in engineering processes.

4. How does generative AI make data accessible for non-technical employees?

AI-driven platforms like Databricks’ LakehouseIQ let non-technical employees retrieve and analyse data through natural language queries, fostering a data-driven culture across teams.

5. How should we prioritise data quality for AI implementations?

Ensuring high-quality data involves robust governance, continuous monitoring, and regular audits. Data quality is crucial for AI tools to produce reliable, actionable insights.