Introduction

Organizations are moving beyond the initial fear that GenAI will lead to job loss. They can now see its potential, yet uncertainty still lingers, especially around how to implement GenAI, harness its strengths and deliver value to both the business and customers. The most pressing concerns we hear include:

• Exposing customers to experimentation
• Limited clarity on regulatory and compliance guidelines
• Risk of exposing business and customer-sensitive data
• The cost of experimenting with GenAI, which can be substantial in monetary terms but, more critically, may result in loss of customer confidence and brand value

This uncertainty is real and it’s causing many projects to stall at various stages. Consider this: Gartner forecasts that 30% of GenAI projects will be abandoned after proof of concept by the end of 2025. An AWS survey found that out of roughly 45 GenAI experiments per organization in 2024, only about 20 made it to production. Despite these hurdles, investment is not slowing down. By 2030, global investment in AI projects is expected to reach $19 trillion.

A phased approach to GenAI adoption

So, how do you move forward and capture the GenAI opportunity? The answer: adopt GenAI in well-defined phases, tackling a concern at a time. Every organization’s journey is unique, but a phased approach helps manage risk and build confidence at every step, paving a strong path to success.

Phase 1: Internal enablement with GenAI solutions

Start with a GenAI-based solution for your teams to use data and materials that don’t include sensitive client information. Gradually progress to more sensitive data as confidence grows.

Why this works: This approach keeps customers and their data safe from early experimentation. GenAI-enabled solutions help staff with daily tasks, reduce manual effort and improve the quality of work, making work more rewarding. Teams can test the solution, share feedback with the development team, shape improvements and gain confidence in GenAI. Early involvement means greater employee engagement throughout the GenAI journey.

Short-term gain: Employee satisfaction, enhanced accuracy, improved efficiency and better customer experience

Long-term gain: Clear technical requirements and higher confidence to continue with GenAI adoption, work with the risk and compliance team to review and refine policies

Use case: Membership organizations
Customer service is the heartbeat of membership organizations. It is critical to support existing and new members as they explore features and ask questions. To deliver a strong membership experience, it is key to depend less on self-serve options and provide real human support. An agent-assist solution can help agents in real time by pulling information from SOPs and policy documents and using member data and historical patterns to answer questions and make recommendations.

Use case: Roadside assistance support
Many organizations promise white glove service — no self-serve, just human support from the first call to resolution. To boost efficiency and satisfaction, an agent assist solution can tap into SOPs, process documents and customer details like vehicle information, location and nearby workshops. This means agents get accurate information fast, reduce effort and improve customer experience.

Phase 2: Leveraging GenAI for business analytics

Once your teams are comfortable, GenAI can be leveraged to drive business analytics, without putting customers in direct contact with GenAI solutions.

Why this works: GenAI accelerates data exploration, generates complex reports and makes advanced analytics accessible. It creates dynamic narratives, answers questions, delivers predictive and prescriptive insights, forecasts trends and uncovers hidden patterns. This lets organizations realize the full value of their data while keeping customers out of the risk zone.

Short-term gain: End-to-end business visibility, strategic decision-making and leadership empowerment with business insights

Long-term gain: Sharper understanding of technology gaps and greater trust in GenAI-generated responses, work with the risk and compliance team to review and refine policies

Use case: Membership organizations
GenAI can power operations analytics, analyzing member data, predicting churn and spotting engagement trends. It automates KPI reporting, highlights performance anomalies and generates natural language insights. Predictive analytics for membership growth and resource planning supports sustainable growth, better retention and operational excellence.

Phase 3: Enhancing automation with Agentic AI

Take automation further by integrating Agentic AI — combining large language models with traditional automation.

Why this works: Many organizations have invested in robotic process automation (RPA), but rule-based bots only go so far. Integrating RPA with large language models boosts effectiveness, enabling decision-making, action planning, adaptation to feedback and continuous improvement. With Agentic AI adopting a modular approach, there are many off-the-shelf solutions for common business needs, so you can experiment and benefit from GenAI with minimal effort.

Short-term gain: Enhanced efficiency and accuracy, resulting in cost savings

Long-term gain: Clearer view of technology gaps and increased confidence in GenAI-generated responses, work with the risk and compliance team to review and refine policies

Use case: Accounts payable
Digital transformation in accounts payable often stalls because bots can’t analyze unstructured data or make decisions. GenAI changes enable bots to handle complex steps like spotting invoice anomalies and matching invoices to cost centers, to name a few. With GenAI’s self-learning ability, bot performance keeps improving.

Phase 4: Scaling GenAI adoption

At this stage, organizations are ready to fully adopt GenAI, invest in customizable solutions to maximize benefits and address environment-specific challenges.

Conclusion

Implementing GenAI is not just about technology. It’s about people, regulatory requirements, cost and above all, the customer. A systematic, phased approach helps you address each of these elements:

• Partner with staff and agents first. Once they see the benefits, you’ll have both a successful GenAI experiment and their support for the journey ahead.
• Use GenAI for business intelligence. When outcomes are reviewed by the broader organization, including leadership, you build trust in your data and GenAI’s capabilities.
• Adopt Agentic AI. Expand traditional automation by integrating GenAI — maximizing current investments and achieving savings with minimal effort.
• Scale GenAI adoption, tailoring solutions to your organization’s unique needs.

By taking a phased, risk-aware approach, organizations can realize GenAI’s value while addressing key challenges and building confidence at every stage of adoption.