The fusion of AI, data, and automation is a pivotal focus for Communication Service Providers (CSPs) aiming to enhance their operations. AI holds immense promise, with the potential to revolutionise decision making processes and optimise operations. However, there is a need for caution. This blog explores the synergy between AI, data, and automation, highlighting key points and critical considerations.

While AI offers powerful capabilities, many CSPs are cautious about fully relying on AI-driven decisions and prefer a Human-In-The-Loop approach for validation. Over time, as operators gain confidence in Automation, they allow more processes to run automatically.

Key points on AI in Automation

There are a few key points to note regarding AI just to set context. AI is a sizeable suite of technologies and capabilities: When hype is high it is often forgotten that this is an umbrella term to a significant array of very different methodologies and technologies. T

This can be illustrated by just considering the following high level list of a few broad categories of AI:

Data analysis: the ability to analyse massive volumes of data – possibly in near-real-time – and detect patterns and trends.

Decisioning: Range from simple reactive machines to machine learning, optimisation, pattern recognition, fuzzy logic etc.

Natural Language: Text to speech, speech to text, classification, translation etc

Computer Vision: Image generation, Image Classification, Object Detection, Video Classification.

Generative AI: For example Large Language models and their application (ChatGPT) fall into this category.

Focusing on the need and objectives

Successful application of any single AI solution requires laser focus on the need and objectives and selection of the most feasible technology.

A relatively small set of AI technologies are market ready for mainstream adoption and production use (E.g. natural language processing). The majority according to analysts are between 5 and 10 years out from being ready for production use.

There are significant risk, reliability, and regulatory challenges to manage for many of the specific technologies and methodologies.

Ill-conceived use of AI can increase the risk of organisational inefficiencies can expose businesses to considerable risks ranging from unexpected behaviours, bias, prejudice, poor decisions/advice to potentially catastrophic consequences such as inadvertent data breaches and copy right infringement. AI will be a powerful tool for CSPs, but right now, it must be considered carefully, and by those with experience of it.

While AI offers powerful capabilities, many CSPs are cautious about fully relying on AI-driven decisions and prefer a Human-In-The-Loop approach for validation. Over time, as operators gain confidence in Automation, they allow more processes to run automatically.

Setting a Successful Foundation for AI

Core benefits of successful foundation automation include consistent business processes and accurate and consistent data sets. These happen to also be the primary requirements for almost all AI technologies and methodologies.  The success of Automation depends on the quality and correctness of datasets. Multiple data sources can lead to discrepancies, requiring a clear strategy to identify and synchronise the master data set. In cases where data is not entirely clean, it’s often better to Automate processes while simultaneously working on data cleaning rather than waiting for perfect data.

Before delving into the realm of AI, organisations should focus on automating processes and ensuring data reliability. Learning from past mistakes and embracing a strategic approach will pave the way for successful Automation implementation.

AI encompasses a wide array of specific solutions and technologies, with some already effectively in use today, while others remain several years away from deployment readiness. A fundamental prerequisite for the successful integration of AI lies in the establishment of a robust foundation of automation and orchestration, which has already found extensive implementation. This foundation serves as a crucial pillar for ensuring not only consistent operational processes but, even more critically, the maintenance of coherent and precise foundational data. This data integrity is pivotal for the effective operation and realization of AI’s potential across various domains.

Conclusion

In conclusion, while AI offers remarkable potential, a cautious approach is vital to manage risks effectively. By establishing a solid foundation of automation and data reliability, CSPs can navigate this evolving landscape with increased confidence.

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