What’s driving your team—customer experience or task completion?
If your team’s goal is to “get things done,” you’re likely stuck on the task treadmill. And you’re not to blame. Customer expectations are increasing with each passing day, and keeping up with their expectations is crucial for any business.
But in this race to keep up, businesses often find themselves trapped on a treadmill of endless tasks, losing sight of their ultimate goals. This is especially true for customer-facing (Customer Success and Customer Support) teams, who are dealing with increasing complexity, siloed information, and inefficient processes.
The solution here lies not in just another tool but in rethinking how you work. This is where the Essential Methodology offers a refreshing approach to drive customer-centric growth through better prioritization based on data.
Understanding the Essential Methodology
The Essential Methodology is a response to the prevalent “do more, do more” mentality that has dominated in recent years driven by the use of agile practices.
While agile methodologies have pushed teams to complete as many tasks as possible in short time frames, they may have lost sight of what actually matters: creating exceptional customer experiences.
So, the Essential Methodology addresses the shortcomings by shifting the focus on customer needs in every step. This is very much in tune with Amazon’s Working Backwards process as described in the book Working Backwards: Insights, Stories, and Secrets from Inside Amazon by Colin Bryar and Bill Carr. The process involves starting with the desired outcome and working backward until the team knows what to build to achieve the desired customer experience. For instance, as part of this process, Amazon writes a press release for a product before beginning development. The key similarity between Working Backwards and the Essential Methodology is the unrelenting emphasis on customer-centricity.
The Essential Methodology is built on three key components:
1. Customer-centricity:
Making customer insights and data accessible to all teams within an organization. The goal is to break down silos and ensure that customer context and history are available to everyone, without recency bias.
Example: Instead of manually analyzing support tickets or digging through Jira issues for customer insights, teams can use AI-powered tools to cluster and surface relevant customer information across the organization.
2. Product-led growth:
Creating automations and using AI agents for repeatable, scalable processes. It’s about making sure when you do something once, you don’t do it manually again.
Example: Customer Success teams often have to be the first line of defense when a customer needs help, what if that inquiry for help were automatically routed to the right person, instead of just the person your customer knows?
3. AI-native approach:
A company that embraces AI-native solutions looks for opportunities to drive efficiency related to three key aspects:
- Deflect: The most commonly understood/used AI concept in Support, when a knowledge base can be used to provide efficient customer self-service via chat or a help center search.
- Common pitfalls: Deflected information links back to a full article and the user cannot find their answer; chat responses are limited to a simple workflow and any deviation errors out.
- Discern: Making connections between different pieces of information, and finding patterns and clustering similar or like items to reveal trends and commonalities. For example, AI can link similar tickets from a single or across customers to reveal a recurring problem, or suggest prioritization when it comes to engineering work items that apply to certain customer requests.
- Common pitfalls: Data accessible to your AI model is limited to only a certain team, type of work, etc. and therefore cannot connect information across customers, support and engineering work.
- Deduplicate: Utilizing AI to find dirty data and help proactively maintain clean, consistent data. Consider this: Instead of manually cleaning and deduplicating customer account data within your CRM, the system automatically identifies and resolves duplicates, ensuring data consistency.
The Essential Methodology also emphasizes the importance of simplification. It’s very easy to make things complex, as my favorite quote from Mark Twain demonstrates, “I didn’t have time to write you a short letter, so I wrote you a long one.” This methodology encourages teams to distill problems down to their most basic, foundational elements, similar to first-principles thinking.
By adopting the Essential methodology, businesses can shift from a project-based mindset to a product-oriented approach by working backwards from the desired customer-centric outcome. This approach ensures that teams focus on building scalable, repeatable solutions that truly serve customer needs, rather than just completing the next task on their list.
AgentOS: Powering the Essential Methodology
DevRev’s AgentOS, provides a comprehensive platform that is AI-native, brings all your data in one place, and is powered by an extensible and robust knowledge graph. It is designed to bring the Essential Methodology to life by using three core capabilities: Search, Workflows, and Analytics. These components empower teams across the organization to work more intelligently and efficiently.
1. Intelligent Search
AgentOS’s intelligent Search capabilities allow teams to quickly retrieve relevant information from vast amounts of data—whether it’s support tickets, product analytics, or CRM systems. This ensures that customer-facing teams have instant access to the insights they need to make data-driven, customer-centric decisions. It also enhances self-service, helping customers find answers without needing direct support.
2. Proactive Workflows
With Workflows, AgentOS reduces manual, repetitive tasks and allows teams to focus on high-value activities. These Workflows are designed to support a product-led growth strategy by ensuring that processes are scalable and repeatable, improving operational efficiency and driving consistent user adoption. Ultimately, Workflows ensure that the product evolves based on real-time user behavior.
3. Advanced Analytics
AgentOS provides advanced Analytics that leverage AI to offer actionable insights across customer interactions, product performance, and operational data. By democratizing access to these insights through natural language queries and real-time reporting, businesses can make informed decisions faster and with greater accuracy. This drives smarter, AI-native decision-making across teams, ensuring that data is used effectively at every level.
The Impact of AgentOS on Business Operations
1. Reduced complexity:
By centralizing data and providing AI-powered insights, AgentOS helps simplify complex processes and decision-making.
2. Improved cross-functional collaboration:
With democratized access to customer insights, teams can align more effectively on priorities and actions.
3. Enhanced efficiency:
Automated workflows and intelligent search capabilities reduce manual work, allowing teams to focus on higher-value activities.
4. Data-driven decisionmaking:
Comprehensive analytics and AI-driven insights enable more informed and timely decisions across the organization.
5. Scalable customer support:
Self-service capabilities and predictive issue resolution help businesses scale their support operations more effectively.
Simplify, focus, and scale
In an era where businesses are constantly pushed to do more, the Essential Methodology, powered by AgentOS, offers a path to meaningful simplification and customer-centric growth.
By leveraging AI and comprehensive data integration, AgentOS helps businesses move away from the “task treadmill” and towards a more strategic, product-led approach that truly serves their customers’ needs.
Learn more on how you can deliver maximum value to your customers by doing fewer things but better in our The Essentialist Methodology whitepaper.
