AI adoption is booming, but many companies struggle to make their AI investments profitable. Should they bet big on AI with ambitious, resource-intensive projects? Or should they settle for small, isolated AI experiments that don’t contribute to long-term business value?
In his insightful article, Tobias Zwingmann presents a structured approach: Orchestrate and Sequence AI initiatives to ensure each step builds upon the last, maximizing returns while minimizing risks.
The Reality of AI Implementation
Many companies fall into one of two traps:
The Big Bang Approach: Companies invest heavily in a massive AI project, expecting game-changing results—but these projects often stall due to complexity, cost, and integration challenges.
The Siloed AI Tools Approach: Others deploy AI in isolated use cases (e.g., chatbots, recommendation engines) that provide quick wins but lack a broader strategy, failing to create lasting impact.
Zwingmann argues that businesses should aim for a structured, phased approach where each AI initiative contributes to a cumulative and scalable AI ecosystem.
Orchestration and Sequencing: The Winning Formula
Rather than launching standalone AI projects, successful AI adoption requires:
Orchestration – Ensuring AI initiatives work together as part of a broader ecosystem, delivering compounding value over time.
Sequencing – Breaking down AI adoption into manageable steps, each designed to pave the way for the next while delivering measurable ROI.
This approach allows companies to build momentum, improve adoption rates, and establish profit checkpoints to validate progress.
How This Looks in Practice
To illustrate this approach, Zwingmann shares a real-world case study of a call center company that successfully implemented AI in a phased manner. Their roadmap followed five key steps:
Internal Chatbot – Laying the Foundation
The company first introduced an internal chatbot to assist employees with tasks like knowledge lookup, meeting notes, and onboarding. Goal: Familiarize employees with AI while improving efficiency. Profit Checkpoint: Save at least 1 hour per employee per week on documentation tasks. By starting with a low-risk, internal use case, the company ensured a smooth transition into AI adoption.
In-Call Augmentation – Boosting Agent Productivity
Once employees became comfortable with AI, the company implemented real-time call augmentation tools to assist customer service agents during live interactions. Goal: Reduce customer call resolution time without sacrificing quality. Profit Checkpoint: Achieve a 20% reduction in resolution times, allowing agents to handle more calls. This step immediately impacted profitability, improving both efficiency and customer satisfaction.
External Testing – Customer-Facing Chatbot
Having built AI capabilities internally, the company moved to a customer-facing chatbot to handle routine inquiries on their website. Goal: Automate repetitive queries, freeing up human agents for complex cases. Profit Checkpoint: Reduce customer service workload by 30% while maintaining high satisfaction levels. This move allowed AI to take on a direct revenue-generating role by improving response times and lowering operational costs.
Proactive Support – Predicting Customer Needs
With AI now integrated into internal and external processes, the company took a proactive approach to customer service. Goal: Use AI-driven analytics to anticipate customer issues before they arise, offering proactive support. Profit Checkpoint: Reduce repeat calls by 25%, improving customer experience and loyalty. By leveraging AI insights, the company transformed its customer support from reactive to predictive, setting a new industry standard.
Voice AI Pilot – Next-Level Automation
The final phase of their AI roadmap was a Voice AI pilot, allowing customers to interact with AI-powered virtual agents over the phone. Goal: Automate routine calls while ensuring a human-like experience. Profit Checkpoint: Achieve a 40% automation rate for common inquiries, cutting call center costs while maintaining quality. With a structured AI journey, the company gradually scaled its AI initiatives while delivering value at every step.
Conclusion: Small Steps, Big Wins
The key takeaway? Start small, think big. Rather than diving into risky, large-scale AI projects, businesses should sequence their AI adoption through strategic, profit-driven steps.
Start with low-risk internal AI use cases to build trust and familiarity. Implement AI in customer service and operational workflows to boost efficiency. Scale towards more advanced AI applications like proactive support and Voice AI.
By orchestrating and sequencing AI initiatives, companies can maximize ROI, mitigate risks, and build a sustainable AI strategy.
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