From efficiency to innovation: A smart roadmap for implementing AI
Given the potential that AI has to offer, it’s no wonder that it has the world at large hooked and businesses hurrying to integrate it into the network strategy. According to McKinsey, 65% of surveyed organizations are already regularly using GenAI, which is nearly double the percentage from their last AI survey conducted less than a year ago. However, prioritizing speed over strategy in AI adoption can lead to mistakes, including wasted resources, improper training, and potential network compatibility issues.
Businesses must avoid the trap of adopting AI merely for its novelty. A common mistake is treating AI as just an add-on to existing products or services. Instead, companies should focus on using AI tools to fundamentally improve their operations and the experiences they deliver to customers and partners.
Although businesses might consider shifting entire models to be AI-driven, it’s often more effective to start by deploying AI with specific use cases in mind, ensuring quicker value realization. Identifying specific applications where AI can have an impact allows for quicker implementation and more immediate results. For example, deploying AI in network operations can lead to significant gains in issue detection, remediation, and overall efficiency and performance. By focusing on targeted applications rather than an overarching cultural shift, businesses can achieve the benefits of AI faster, and with a greater impact.
This concept is best illustrated through what we call the ARC framework, which provides a structured approach to AI implementation. This framework outlines three pivotal stages in AI implementation: augmentation, replacement, and creation. Each stage represents a step towards maximizing the ROI from AI, demonstrating a clear progression from basic enhancements to innovative transformations that is easy for businesses to follow, no matter where they are in their AI journey.
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Augmentation
The initial phase, augmentation, involves enhancing existing capabilities with AI. This is where many enterprises begin their AI journey. For example, AI can be used to improve IT operations (AIOps) by automating routine network monitoring tasks, including anomaly detection, remediation and root cause analysis, thereby increasing efficiency and reducing downtime.
By freeing IT staff from manual tasks, they gain back the time to focus on higher-value aspects of their role. While augmentation can offer immediate benefits, such as improved performance and reduced operational costs, relying solely on this phase can limit long-term ROI. Many organizations find themselves stagnating at this stage, causing hesitance among boards regarding further AI investments.
Replacement
The second phase, replacement, involves AI taking over entire tasks previously performed by humans or outdated systems. This phase offers a more substantial boost in efficiency and cost savings. For instance, in customer service, AI chatbots can replace human agents for handling routine inquiries, freeing up human resources for more complex issues. This phase not only enhances productivity but also prepares the organization for more substantial innovation.
By transitioning from augmentation to replacement, businesses can demonstrate tangible improvements and build confidence among stakeholders in the potential of AI. However, it should be noted that even replacement phase activities are best implemented and planned with the assistance of humans. IT staff can still embrace this phase and view it as an overall opportunity to encourage automation and optimization across their department.
Creation
The third and most transformative phase is creation. This is where the true potential of AI is unlocked, as it goes beyond just enhancing or replacing existing processes. It becomes a catalyst for entirely new business models and revenue streams. Take sports stadiums as an example. Organizers can use AI to analyze real-time data on fan behavior and preferences, allowing them to personalize their customer experiences by recommending concession items or merchandise based on past purchases. Additionally, AI can identify lucrative sponsorship opportunities by analyzing fan demographics and engagement in real-time across specific applications or areas of the stadium.
This phase demonstrates the long-term ROI of AI and its role in sustaining business growth. By creating new value propositions through AI, organizations can address any concerns of their businesses’ CFO regarding the cost-effectiveness of AI investments. The creation phase exemplifies the ultimate goal of AI implementation: fostering innovation and propelling businesses forward by creating entirely new possibilities.
Other considerations
The ARC framework offers a robust approach to integrating AI into business operations, but it’s crucial to recognize that its phases can occur concurrently, not just sequentially. This flexibility allows businesses to simultaneously address various aspects of their operations, creating a more dynamic and responsive implementation process. Unlike previous technological advancements, Generative AI is moving so fast that all three phases of the ARC framework—augmentation, replacement, and creation—are often overlapping and running in parallel. At each phase, human assistance and leadership are still essential.
To fully harness the power of AI, businesses must reimagine every aspect of the user journey and lifecycle. This involves applying AI-driven insights and solutions at every step— from training and enablement to day-to-day operations. Each phase should be infused with AI to enhance and transform the overall experience.
An effective AI strategy must also be agnostic, leveraging all available technologies without becoming locked into any single one. This vendor flexibility allows organizations to adapt and integrate new advancements as they emerge, a necessity given AI’s continuous evolution. Additionally, ensuring seamless integration across all people and devices is crucial. This comprehensive connectivity supports the deployment of AI across various touchpoints, enhancing its effectiveness and reach.
Conclusion
AI is more than just a technological upgrade; it’s a transformative force that can redefine entire business experiences. For CIOs and business leaders, adopting AI requires a fundamental shift in how interactions with customers, partners, and vendors are envisioned. Instead of viewing AI as a simple enhancement, it should be central to business design and architecture. This approach can reshape experiences, processes, organizational structures, and business models.
While GenAI captures much of the spotlight, the real potential lies in developing comprehensive AI ecosystems that integrate multiple technologies with existing infrastructures, driving productivity and innovation. Rather than succumbing to FOMO and rushing into AI adoption, businesses should adopt a focused, use case-driven strategy, guided by the ARC framework, to maximise ROI. This ensures that AI becomes an integral, long-term component of the business, delivering tangible benefits, justifying investments to stakeholders, and fostering ongoing support for future AI initiatives.
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Given the potential that AI has to offer, it’s no wonder that it has the world at large hooked and businesses hurrying to integrate it into the network strategy. According to McKinsey, 65% of surveyed organizations are already regularly using GenAI, which is nearly double the percentage from their last…
