
Transforming Telco: AI in Telecommunications
Based on our previous work with telcos and our research, we have identified many impactful AI-led use cases.
Since the release of ChatGPT in November 2022, the corporate race is on to identify and successfully integrate the most impactful generative AI-based business applications.
With a broad range of use cases, early results revealed by Google Cloud already demonstrate its impact, from increased operational efficiency (66%) and improved customer experience (57%) to accelerated innovation (49%) and increased employee productivity (48%).
Furthermore, the latest research from McKinsey estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually to the global economy.
In this blog post, we’ll review the use cases for generative AI, the enterprises already leveraging GenAI, how to approach ROI assessment for GenAI initiatives, and where to get started with them.
The majority of GenAI adopters rely on off-the-shelf features integrated into productivity applications (71%), standard applications (68%), and enterprise platforms (61%).
Let’s review the five common use cases for generative AI that transform today’s workflows. We’ll list them in the order of increasing complexity and value.
Ask a tool like ChatGPT to summarize a 20,000-word report, and it’ll oblige. This is one example of how GenAI tools speed up working with large amounts of text data, increasing employee productivity.
Data transformations powered by generative AI span:
As a new stage in the evolution of computer-human interfaces, NLIs can replace graphic user interfaces to interact with databases or applications.
Users can formulate their requests in natural language instead of clicking buttons or tapping the screen. Those requests are then processed by a large language model (LLM) to execute a specific action and return a natural language response.
Here are two examples of NLIs’ application in the corporate setting:
GenAI tools can automate business workflows based on factors like:
To identify which workflows to automate, you need to collect and analyze big data on your internal operations. Once you pinpoint the areas of improvement with the biggest ROI, you can leverage GenAI to streamline those workflows.
Here are six examples of workflow automation powered by enterprise generative AI:
Copilots are NLIs enhanced with connections to multiple additional data sources and systems. They’re meant to assist employees across diverse tasks and activities.
Here are several examples of existing GenAI copilots:
The most complex GenAI use case is also the one bearing the highest reward. An autonomous agent is capable of navigating a wide range of complex workflows with little to no human intervention, allowing for automating whole job functions. This creates the potential of replacing human employees with GenAI tools.
Implementing autonomous agents requires integration with diverse knowledge sources and tools. However, for now, autonomous agents are mainly experimental due to their many limitations. They reportedly struggle to remain focused on the stated objective. There are also concerns about autonomous agents’ reliability in complex settings and unpredictable scenarios.
According to McKinsey, about three-quarters of the value from GenAI use cases concerns four areas of operations:
Let’s break down the three business functions where GenAI can bring tangible value today.
Solutions powered by LLMs can understand and generate natural language in ways previous technology spurts couldn’t, enabling:
LLMs can identify user intents and contexts and take sentiment analysis to the next level. This data can power hyperpersonalized recommendations, search results, and location-based suggestions.
Generative AI can also enhance media discovery via conversational interactions, as exemplified by this sample architecture for personalized content discovery by Google.
These two areas are notoriously heavy on routine administrative tasks. However, automating such tasks is what GenAI excels at. As a result of this automation, employees can dedicate their time to higher-value tasks and complex problem-solving that can’t be offloaded to GenAI.
Here are three accounting and HR tasks GenAI can automate:
Generative AI can also reshape recruiting by helping managers write better job descriptions and personalize candidate outreach efforts.
Although 48% of business leaders expect GenAI to transform their organization within one to three years, that doesn’t mean that generative AI tools aren’t finding their application in today’s corporate world.
* Now decides next: Insights from the leading edge of generative AI adoption report, Deloitte
Here are just three examples:
Gartner proposes assessing the ROI across three types of GenAI investments:
Discover Neurons Lab’s GenAI solutions and take a deep dive into how we helped Peak Defence enhance business operations with enterprise-generative AI:
As the technology’s applications are diverse, it’s crucial to zero in on the specific domain where generative AI can bring the highest ROI. It can come in the form of automated or accelerated tasks, better idea generation, or democratized access to business intelligence.
Once you zero in on the domain, identify the job functions that can be made more productive and conduct buy vs build analysis. Deploy the GenAI tool as a pilot project, ensure your employees can work seamlessly with it, and measure the results.
If you have a few ideas but don’t know where to start with them, Neurons Lab can help you. Our AI design sprint will enable you to identify the business opportunities with the highest ROI and clarify how you can leverage AI for the maximum impact.
As an AI consultancy with 100+ GenAI projects on our track record, we have the expertise required to rapidly conceptualize, design, and prototype your idea during the sprint. Get in touch with us to discuss how we can help you boost operational efficiency and gain a competitive edge with GenAI.
Generative AI is a type of artificial intelligence used to manipulate and generate content (text, code, audio, images, videos). Enterprise generative AI is applying this technology to achieve business goals like increasing employee productivity and creating new revenue streams.
Five common use cases for generative AI are, in order of increasing complexity:
Generative AI can be applied in:
Based on our previous work with telcos and our research, we have identified many impactful AI-led use cases.
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