👨‍💻 AI Use Cases in Tech, Telecom and Media

DTP #32: Which AI developments to focus on in the new year

The tech, media, and telecom fields handle massive amounts of information, offering ample opportunity for Gen AI to step in. 

Companies in these fields have made strides using AI to cut down on manual work, boosting effectiveness. To note, while some are in advanced stages of utilizing AI, others are just beginning.  

Across the three sectors, Gen AI adoption looks as follows: 

Source: EY, Work Reimagined Study 2023, EY Knowledge Analysis

Generative AI could be the key to helping businesses in tech, media and telecom, no matter their AI expertise, speed up digital changes and unlock fresh skills and results for their business. 

We look at the most prominent Gen AI use cases for TMT below:

🌐 AI Weekly News Roundup 

In 2023, the media industry saw a surge in generative AI use, a focus on privacy, sustainability, and brand safety, amid economic challenges. This led to innovation, collaboration, and responsible AI adoption. Industry leaders anticipate AI's incremental impact on content, legal issues, and operations in 2024, emphasizing responsible use and protecting intellectual property. 

Lumen relies on AI and ML for network management, using automation to analyze vast data streams for anomaly detection, customer issue prediction, and preventative maintenance. They emphasize human analysis alongside AI, stressing the need to align technology with specific business needs for effective implementation. 

AI chatbots, powered by vast language models, enable various tasks without coding knowledge. They're reshaping industries, creating new job roles, enhancing productivity, and transforming education and consumer experiences. While concerns about privacy and job displacement persist, adapting to AI technology is pivotal for future work relevance and efficiency. 

💼 AI in Business

AI Developments to Focus on in the New Year

Image generated by DALL-E

Media attention and surveys have highlighted the rise of AI, with 62% of leaders excited about its potential for business growth, but many remain unsure how to leverage it practically and ethically. 

An article from Forbes highlights the key business focuses for using AI. 

Four executives provided insights on AI predictions for 2024: 

Use AI to wow customers while upholding firm policies: 

Zack Toyota aims to leverage AI for superior customer service but emphasizes the need for careful measures to avoid potential hazards, urging organizations to establish ethical and transparent AI systems aligned with established standards and values. 

Expect disruption and transformation within the labor market; don’t fear it: 

Alan Sutton anticipates AI's impact on industries, suggesting it won't solely replace jobs but rather assist workers in various tasks. Soft skills and AI expertise will become crucial in the evolving job market. 

Focus on use cases with immediate benefits:  

Andrew Smart sees AI's potential in communication and content creation, highlighting the launch of Slator Answers that allows subscribers to obtain quick, well-constructed responses without overwhelming company staff. Recommends starting gradually with AI and finding use cases that offer immediate benefits. 

Make AI the secret ingredient for client retention: 

Greg Alexander believes AI can predict and prevent customer churn, particularly valuable for membership-based businesses like his. AI's predictive abilities can enhance client experiences and brand loyalty, contributing directly to bottom-line growth. 

Despite AI's infancy, these insights emphasize that a practical and ethical approach to AI implementation could lead to substantial growth opportunities while acknowledging the need for caution and oversight in its use. 

 According to the EY article linked above, the noteworthy areas of application for Gen AI are: 

  1. Enhancing customer experience: In the short term, Gen AI has the capacity to transform how customers engage by creating more intelligent chatbots and integrating human and digital assistance in innovative ways. 

  2. Enabling novel business models: Over the long haul, Gen AI has the capability to unleash the potential of fresh business models, particularly those encompassing service platforms accessible to ecosystem partners and intermediaries. 

  3. Transforming into an AI-enhanced organization: A Gen AI-centric approach can significantly benefit knowledge management, productivity, and automation within TMT organizations, empowering employees further. 

Breaking these points down into some prominent use cases, we have: 


A voice assistant powered by Generative AI can swiftly address customer concerns while adhering to company policies and standards, potentially maintaining or even improving customer satisfaction levels. 

Personalized customer self-service: 

  • Utilizing Conversational AI with an LLM for localized customer support in their preferred language. 

  • Virtual troubleshooting for personalized customer experiences. 

  • Virtual assistant offering tailored product recommendations and exclusive offers to enhance satisfaction. 

Interactive Q&A: 

  • Automating personalized responses for common customer queries pre- and post-sales. 

  • Reducing customer response times and overall expenses. 

🎯 Context summarization: 

  • Using Generative AI to swiftly document interaction context. 

  • Minimizing agent handle time and the associated costs. 


Generative AI tools can streamline and improve content creation by reducing reliance on manual editing and labor-intensive content management processes. 

Creative assistance: 

  • Leveraging Generative AI to generate imagery and execute edits through descriptive commands. 

  • Offering conversational editing, text-to-template, text-to-image functionalities for faster content editing. 

Video editorial: 

  • Automating video management via video-to-text Generative AI, enabling scene evaluation and tag creation. 

  • Utilizing text-to-video commands (e.g., "add more rain to this scene") to expedite and enrich the editing process. 


Generative AI offers the capability to swiftly assist sales teams in locating and translating technical specifications for customers. Additionally, it aids in documenting and summarizing valuable insights gathered from customer interactions. 

Technical spec summarization: 

  • Creating summaries of technical specifications through targeted text-based queries for sales staff to identify products meeting customer needs. 

  • Assisting staff in recommending features and integrations aligned with the customer's technology stack and existing vendors. 

Knowledge management update: 

  • Utilizing Generative AI to update sales case history, facilitating future resolution of similar technical inquiries using previous resolution steps and specification summaries. 

Automated technical demos: 

  • Training a model on demonstration scripts and interactions to generate tailored demonstrations highlighting solution benefits for specific clients and use cases. 


Gen AI facilitates rapid access and translation of technical specifications, empowering operations and frontline staff for quicker knowledge retrieval. 

Spec summarization and search: 

  • Utilizing Generative AI to generate technical specification summaries through targeted text-based queries. 

  • Assisting in understanding which products match customer requirements. 

  • Suggesting features and integrations aligned with the customer's technology stack. 

  • Providing links to relevant articles or internal knowledge bases for future reference. 

Automated technical demos: 

  • Employing Generative AI to automate the creation of tailored software demonstrations. 

  • Training on demo scripts and interactions to showcase key features and benefits specific to clients and use cases. 


Automating the summarization and documentation of code allows developers to concentrate on more valuable tasks. Simultaneously, it facilitates the comprehension of code for both technical and non-technical stakeholders. 

Minimizing code documentation effort: 

  • Utilizing Generative AI to review and summarize code, producing human-readable application documentation. 

  • Automatically identifying critical code sections and adding explanatory comments or summaries. 

Generating code from natural language descriptions: 

  • Creating code from structured descriptions (e.g., behavior-driven development) provided by non-technical individuals such as business analysts and product managers. 

  • Reducing time-to-development while enhancing efficiency and productivity by avoiding manual code writing. 

The importance attached to each use case will vary across tech, media and telecom companies. Summarizing the order of priority: 

Source: EY Knowledge analysis

💻 Platform Highlight

Eilla AI: AI-powered financial analysis platform. Recently raised $1.5m to aid investor decision-making. 

Together AI: Startup helping pre-train and fine-tune open-source foundation models. Recently announced a $102.5 million Series A round. 

Writer: Enterprise focused “Full-stack” Gen AI platform. Closed a $100m funding round. 

💬 Social Highlight

5 years of r/datascience salaries, broken down by YOE, degree, and more: Post 

“Ways AI can help save your business some serious cash”: A tweet

🤖 Prompt of the week 

Act as a data scientist and coder. I have a dataset [describe dataset]. Write code for data visualisation and exploration.

See you next week,


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