The Network Automation Parallel: Decoding AI Adoption Part II
Network automation and Artificial Intelligence (AI) have a lot in common. As companies study the path for how to adopt AI in order to deliver value back to the business, examining the adoption of network automation can provide insight into the path for AI adoption.
This blog series explores the potential for AI adoption and offers predictions about its future trajectory based on the similarities to network automation.
Part 1 in this series covered the similarities network automation and AI share in their foundational need for clean data and in their ability to unlock new capabilities to deliver value.
This blog is the second in the series. It will examine what it means to “have” AI and how enterprises will go about adopting it.
It’s Not Always Clear What “It” Is
Network Automation
Often, technical and business leaders grasp the concept of needing network automation, but the reality of what effective network automation actually looks like is unclear. It is easy to mistake isolated scripting efforts for true automation that drives scaled business value. Network automation that drives scaled business value requires a comprehensive and well-architected approach, integrating systems to orchestrate both data and workflows.
There are two primary ways that firms go about adopting network automation.
Ease into It
A common initial approach to network automation involves automating repetitive, low-value tasks such as:
- Configuration management via the CLI
- OS upgrades
- Device lifecycle management
- Service activation
It is easy to start with these types of tasks because it’s easy to imagine that in any effectively automated environment, employees would not be burdened with these types of tasks. Automating these tasks also provides quick, demonstrable value, fostering support and a foundation for tackling more complex automation later.
Organizational Transformation Projects
Alternatively, companies with strong mandates for change and accompanying funding may pursue a deliberate, comprehensive network automation transformation. This multi-year effort aims to rapidly shift from manual workflows to fully automated end-to-end workflows, maximizing business value.
These projects often involve external consultants specializing in comprehensive network and IT automation. Their expertise guides the transformation, providing a roadmap to an efficient end-state architecture and accelerating implementation while the client aligns its staff and organization. These consulting firms possess deep experience in architecture, data governance, and organizational change required to navigate these complex transformations.
This type of transformation demands sustained commitment and investment across all levels of the client organization to overcome challenges and achieve lasting end-to-end automation. These types of projects can falter without a sustained commitment over several years, so they do involve some risk. The trade-off is that a successful transformation can give the company a strategic advantage over competitors.
A Mix of Paths
It’s also fair to say that an enterprise may change gears, following one of the above paths first, then switching to the other path. Or, perhaps the enterprise can follow one path in one part or business unit (BU), and follow another path in a different segment or BU.
Larger enterprises are not homogeneous, and the path to an automated future can vary within those firms.
Similar to network automation, it is often unclear how to strategically implement and leverage AI
AI Predictions
Like network automation, companies acknowledge the requirement to adopt AI for competitive advantage, but it is often unclear what it means to “have” AI. It is unclear because the vision of what a truly comprehensive AI program entails is typically hard to define without proper expertise. Basic AI applications, such as data analysis or using LLMs for quick information retrieval will provide some value, but those don’t represent the transformative potential of a deliberately designed, comprehensive AI architecture.
Realizing the full business potential of AI will require a deliberate and comprehensive architectural strategy
- Realizing the full business potential of AI will require a deliberate and comprehensive architectural strategy
- Comprehensive AI adoption will require explicit and sustained effort on the part of everyone, from leadership to individual contributors over multiple years
- Companies will likely follow one of three main adoption routes:
- Starting with smaller, evident AI applications to build support and momentum, which allows for gradual cultural change but risks falling behind the competition
- A more aggressive, transformational approach—often relying on experienced AI consultants, which offers a chance to gain a significant competitive edge but requires strong and sustained commitment from all levels in the organization
- A mix of the above two paths
Conclusion
Conclusion
Wrapping Up
A business’s tolerance for risk, the available funding, and executive/leadership commitment will determine how fast an enterprise can adopt AI.
That mix of factors will play a large part in which path that enterprise employs to adopt AI:
- A gradual path, where smaller, obvious items are automated first in order to prove value and build trust
- A more rapid, business-wide, transformative path, where there is a mandate and funding to fundamentally change how the firm does business
- A mix of the above
– Tim Fiola
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