Preparing your operations for GenAI

Generative AI (GenAI) is rapidly transforming the business operations landscape, enabling you to personalize interactions, automate tasks, and create new products and services at scale. Like most people, I’m excited about the rapid emergence of GenAI and the positive impact it will have on improving the customer experience (CX), while also increasing efficiency across the operations.?

After years of working with ML/AI technologies, recently diving into the new GenAI capabilities, and chatting with peers in the industry who are deploying GenAI -- I wanted to share some key recommendations on: How to prepare your operations to effectively deploy GenAI.

Below are 6 key areas I’d recommend teams invest in to ensure success when deploying GenAI. Here are the key takeaways:

1. Align the organization on a prioritized GenAI roadmap and success metrics
2. Enhance your data infra and quality for improved GenAI model training and accuracy
3. Update your QA programs to measure and improve GenAI model performance
4. Create incubation pods for rapid testing and scaling of new GenAI tools
5. Invest in the team through upskilling to meet new business/skill needs
6. Enhance change management to streamline testing new GenAI tools/workflows

1. Align the Organization

Develop a data-driven roadmap on how/where to deploy GenAI across the CX, focusing on the most high-impact use-cases. Ensure alignment with key stakeholders, as this roadmap will drive plans?to make changes across products, processes, and people. Creating clarity upfront will help track progress, ensure accountability, and also mitigate risks.?

Key considerations:

• Establish success metrics tied to business value generation?
• Define the roadmap identify and prioritize the CX use cases to deploy GenAI
• Scope CX tools/product changes needed to test/scale GenAI workflows

2. Enhance Data Sources

The GenAI model performance will need a reliable “source of truth” data/content foundation (e.g., knowledge base, help center, FAQs, tickets). The CX team will need to invest heavily into curating and optimizing the data/content inputs to the model to ensure continuous training, improvement, and accuracy.

Key considerations:

• Optimize model inputs (content) to train the GenAI model and inform its accuracy
• Utilize insights to curate new content/data to enhance GenAI models and workflows
• Build analytics infra to measure and optimize GenAI model performance

3. Update QA Programs

A robust GenAI QA program is crucial to help measure, detect, and reduce defects. The current QA program will shift from focusing on human-interactions to mostly GenAI-interactions. This involves new sampling methods, having a definitive 'source of truth', actionable feedback loops into the model, and real-time QA monitoring for defect detection.

Key considerations:

• Update QA to measure GenAI interactions with new sampling and scoring methods
• Build structured feedback loops from QA to train the GenAI model real-time
• Build real-time QA monitoring to enable immediate actions and/or triage by CX teams

4. Increase Agility and Speed

Establish small, agile, cross-functional pods to isolate testing new GenAI tools, which will enable rapid test-learn-scale cycles. This strategy minimizes risks, accelerates progress, and increases testing frequency. These pods consist of high-performing agents/leads responsible for human-supervised model training, enhancing GenAI interactions, shadowing GenAI interactions, and testing new tools/experiences.

Key considerations:

• Create cross-functional “incubation” pods tailored to specific CX GenAI use-cases
• Isolate changes minimize risks across the broader CX tools and operations
• Standardize and scale the pod programs that demonstrate high impact

5. Uplevel the Team?

GenAI can create concerns about job displacement, particularly among frontline customer-facing teams. Additionally, GenAI will dramatically change how/where the CX team will spend their time, shifting from repetitive transactions and agent management, to more consultative support and optimization of the GenAI model.

Key considerations:

• Engage frontline agents on GenAI initiatives to harness key insights and drive engagement/retention
• Adapt and upskill team to fill new CX roles that can optimize the GenAI model and workflows
• Identify skill gaps to coach/acquire talent that can drive innovation and execution

6. Design Systematic Change Management

It’s obvious, but effective change management is vital for success. It requires holistic planning, ownership and accountability for tools readiness, project management, and escalations. The CX team should have designated "change managers" to lead the pilots and collaborate tightly during GenAI product/tool standardization and scaling.

Key considerations:

• Develop a "ops readiness" template for consistent planning and execution
• Scope tools and infra needs to deploy new GenAI tools and workflows
• Deepen CX tech/tools expertise to manage ongoing technical integrations and changes

Conclusion

The introduction of GenAI presents an exciting opportunity for companies to elevate their operations, customer experience, and drive efficiency, however, it will also introduce unique challenges in terms of technology infrastructure, CX journey mapping, agent skill sets, and data insights. By following the above recommendations, companies can harness the potential of GenAI to elevate its competitive advantage, delivering a CX that is not only delightful, but also effective and efficient.?

Please share your thoughts, and additional focus areas I may have missed.