We nurture lasting relationships, enabling stronger teams, bold and intelligent decisions, better products and services.
For over 25 years, Torry Harris' focus on integration solutions has fostered seamless digital connectivity, enabling better and faster commerce for businesses through platform business models.
From innovation hubs to delivery centers, we bring the right people, skills, and technology together to support your digital transformation journey.
Our relentless focus on excellence has earned us prestigious awards and recognition across various domains. Learn about our achievements.
From enhancing customer experiences to optimizing complex integrations, we’re proud to be a trusted partner in helping organizations achieve their strategic goals. Explore our client transformation stories.
Our WeCare initiative is more than just a program-it’s a promise to uplift and empower individuals who are often overlooked, helping them find a sense of purpose, self-worth, and economic independence. Whether through training, collaboration with social enterprises, or providing direct support, we work to ensure that dignity is restored and futures are reclaimed, one project at a time.
We believe that the right partnerships can make all the difference. Our strong partnerships enable us to deliver on our promise of high performance, flexibility, and competitive pricing, ensuring that our customers achieve their strategic objectives with confidence.
Our summary decks bring together years of collective experience and industry knowledge, offering actionable industry insights. Condensed for quick consumption, these resources are packed with strategic insights, case studies, and methodologies that can help you adapt and excel.
Cloud & Automation: Changing CSPs’ OpEx outlook
Generative Artificial Intelligence (Gen AI), with its ability to autonomously produce novel insights from data it is fed, holds immense business potential. From optimizing customer service, automating business processes, and enhancing security to creating new revenue channels, Gen AI is supplementing transformative outcomes across various industry sectors today.
These capabilities are particularly vital for telecom companies (telcos) that are grappling with dampened revenue growth amid stiff competition, growing pressure to deploy new services at market speed, 5G infrastructure enhancements, and customer experience enhancement with faster, better, and more personalized offerings.
There is a strong fit for Gen AI in telecom, as the industry generates vast data volumes, including call records and customer interactions. According to the Telecommunications Generative AI Study conducted by global consulting firm Altman Solon and commissioned by AWS, the technology can help process, interact, and derive insights from the telecom industry’s large datasets. The study points out that Gen AI can help Communications Service Providers (CSPs) stay competitive, while managing risks and costs, through applications that improve customer experience, enable new product features and services, and enhance operational efficiency.
A large telco integrated Gen AI with its software engineering operations, labeling it as a“virtual assistant” for coding. This implementation has led to productivity increases of30-45% during trials with around 250 developers. In another Gen AI application, a SouthKorean telco has leveraged the technology to develop a super app, connecting users to services such as music streaming, e-commerce, and personalized solutions for everydaylife challenges.
A recent NVIDIA survey of over 400 telecom professionals worldwide revealed a growing enthusiasm and adoption of both Gen AI and AI in general. Respondents revealed that AI implementations have contributed to increased revenue and cost savings in the industry.
However, due to the vast amount of sensitive customer data handled by CSPs amid a strict regulatory environment, the telecom industry has been cautious with technology investments compared to other sectors, according to the Altman Solon study. Integrating Gen AI presents challenges, as telcos require high-performance models tailored to theirunique data environments. Besides regulatory compliance, they face hurdles such aslegacy system integration, data security, scalability, infrastructure costs, talentavailability, and technical debt.
What modern telcos need is for their business, tech, and legal teams to work together witha Gen AI partner or in-house team to deploy cost-efficient, competitive, and scalable applications with the technology while meeting industry compliance and ethical standards.
Use cases of Gen AI include personalization of customer interactions and services,development of new services and features, predictive maintenance and efficient resourceallocation, and adaptive defense against cyber threats. Gen AI: Operators take their firststeps, a report published by TM Forum, a leading global alliance of telecom andtechnology firms, reveals that 31% of telcos are currently experimenting with Gen AI proofs of concept before widespread deployment.
Gen AI's advanced algorithms can leverage customer and market data patterns to target specific audiences, according to McKinsey. Gen AI can also help develop virtual assistants tailored for telecom operators, enhancing customer service. TM Forum’s research highlights customer operations as the most significant Gen AI application thus far fortelcos, with 92% leveraging its capabilities to process both unstructured and structured data to enhance chatbot experiences. BT believes that Gen AI could significantly speed upits transformation process from years to months. One example of BT's Gen AI integration is its chatbot, Aimee. The chatbot offers various functions such as providing weather updates, bill status, and allowing users to modify subscriptions or add-ons. BT expectsthat, by 2025, Aimee will achieve a Net Promoter Score (NPS) exceeding 80, following more than 400 million customer conversations. In telecom, NPS is a widely used metricto gauge a customer's likelihood of recommending the company to others.
Gen AI facilitates the generation of innovative ideas and designs, and helps reduceproduct development cycles, according to Forrester. In the telecom industry, the technology can be leveraged to develop new services and features with insights from autonomous analysis and interpretation of customer data in real-time. For instance,Vodafone’s Gen AI-driven chatbot, TOBi, has conducted around 13.5 millionconversations across all markets, with “between 65% and 80% fully automated, requiring no human intervention”. With this data, TOBi now helps users with purchasing SIM-onlyplans and continually explores new add-ons to enhance customer value.
AI has already enhanced network performances and efficiencies by predicting network failures early. This has led to timely corrective actions and yielded financial benefits by minimizing costly maintenance and network downtime. With Gen AI integration, systems can adapt and learn from dynamic network conditions in real-time. This enables advancements in network planning, load testing, and performance optimization.
In 2022, Nokia actively partnered with a top APAC CSP to develop a GenAI solution forextensive automation, aimed at enhancing the telco's network and service operations.This collaboration aimed at tackling concerns such as “siloed domain knowledge, limited data sharing, high data analysis costs, and inefficient DevOps development cycles”. The project achieved an 80% reduction in the telco's knowledge acquisition time and over a 70% increase in data analysis efficiency. Furthermore, it delivered significant annualsavings of Euros 6.5 million in network operations for the telco.
Telcos can leverage Gen AI to improve network security by quickly identifying threats, monitoring` unusual activity, and predicting potential risks. Gen AI can help prevent unauthorized network access by monitoring consumer activity and flagging unusual usage patterns. BT is harnessing Gen AI to establish a programmable network core that combines network and security. The goal is to have a software-defined core network that can easily accommodate security-driven changes desired by the company. According to BT, “the organization’s security stance, in turn, can adapt to any requirements the network needs, using network telemetry to identify and proactively respond to threats,leveraging the capabilities of Gen AI”.
As per global consultancy firm, Roland Berger, Gen AI’s ability to simulate complex behaviors, generate novel content and personalized content, adapt in real-time, and remain robust in the face of data scarcity sets it apart from traditional AI and ML models.
Gen AI autonomously creates new content such as images or text, mimicking examples from a training dataset. This is primarily through the Gen Adversarial Networks (GANs)and Variational Autoencoders (VAEs) models.
GAN model: It has a generator that creates realistic data and a discriminator that helpsdistinguish real data from realistic data. Through this process, the GAN model can learnand produce convincing artificial data such as photorealistic images for creative purposes.
VAE model: It first compresses data into a compact form and then picks the highlights of this data to generate new data. Use cases for the VAE model include anomaly detection, image generation, and data compression.
Gen AI applications in telecom, from virtual assistants to content generation, are extensive, enhancing marketing initiatives, customer service, network optimization, and product development. According to Altman Solon, 64% of CSPs consider numerous usecases to be novel applications, not addressed by existing non-Gen AI methods. Precedence Research reports that the global Gen AI market in telecom, which was about US$150.81 million in 2022, is expected to grow to around US$4,883.78 million by 2032 – at an estimated compound annual growth rate (CAGR) of 41.59% from 2023 to 2032.
McKinsey defines digital twins as “the virtual representations of real-world products orsystems, to collect and model data”. For network infrastructure, digital twins can simulatethe entire network for predictive adjustments and maintenance, optimizing energy usage. Digital twins can also help telcos pinpoint profitable resource allocation and refine security measures before actual implementation. For example, China Telecom is exploring the implementation of a digital twin to visualize the effects of changes in factors such as customer activity and usage of network components on overall network performance before real-world application.
Gen AI excels in creating artificial or simulated data, which is not drawn from the realworld but generated by an algorithm. This synthetic data can be leveraged for better predictive modeling and improved anomaly detection, speeding up testing processes andenhancing security against cyberattacks. The use of synthetic data is quickly gainingtraction among telcos. For instance, Vodafone has launched a proof of concept to test howsynthetic data can be used for training and testing its machine learning models.
Consultancy firm Roland Berger emphasizes that reducing customer churn is vital fortelcos aiming to address dissatisfaction in network performance, customer service, andproduct pricing. Churn rate is the rate at which a business loses customers annually, according to Gartner. During churn modeling, businesses often struggle with poor dataquality and interpretability issues. Gen AI surpasses traditional AI for churn modeling with enhanced predictive accuracy, personalized recommendations, real-timeadaptability, and automated insights generation.
Implementation of Gen AI in telcos comes with challenges that range from poor data quality and lack of interoperability with legacy systems to compliance and ethical concerns. However, in its report, The Generative AI Advantage, Forrester recommends that many of the potential challenges of Gen AI can be turned into opportunities that improve data quality, drive innovation, and strengthen customer trust.
Data is the foundation of all AI and Gen AI models. Ensuring the quality of data and implementing resilient data analysis practices are imperative for the effective utilization of Gen AI in the telecom sector. The quality of the data input also mirrors the final outputof the Gen AI model. According to McKinsey, proper and reliable data management includes awareness of data sources, data classification, data quality and lineage,intellectual property, and privacy management. However, in case of ‘nonsensical outputsor hallucinations’, Forrester suggests RAG, which is a model that searches through testedand high-quality information from credible external sources to find the best possible data to use. This helps make the final information more accurate and useful.
Many telcos have legacy systems that can make integration difficult and costly. However,it is crucial for telcos to invest in connecting their extensive data repositories with emerging Gen AI platforms to foster innovation and stay competitive. According to Forrester, businesses should start with use cases for Gen AI projects that are practical andmeasurable. This method could also help telcos test Gen AI interoperability with their existing technology. For example, Deutsche Telekom's chatbot Ask Magenta, which was initially based on a manual decision tree, now leverages an LLM to better understand customer inquiries and provide accurate responses. At present, the chatbot relies on adecision tree for 80% of its functionality, while leveraging an LLM for the remaining 20%.
EY research reveals that 68% of telco respondents believe they are not adequately managing the unintended consequences of AI. According to Gartner’s ‘Top Three CSP Market and Technology Trends from DTW 2023’ report, CSPs are rapidly exploring GenAI for its hyper-personalization, network automation, and cost optimization benefits. However, trust, risk, and security management (TRiSM) are often neglected, increasing risk exposure. To address this, Gartner recommends that C-suites of CSPs establish adequate governance procedures via a steering committee tasked with ensuring TRiSM when exploring and operationalizing Gen AI technologies. Forrester advises that besides focusing on tackling data leakage, data lineage and observability, and privacy concernswhen it comes to Gen AI, businesses must showcase their privacy and security measures through customer-friendly messaging. Additionally, they should educate both employees and customers on privacy and security practices, such as the Zero Trust approach, to boostuser confidence in Gen AI.
AI is facing increased regulatory scrutiny worldwide, with policy makers introducing unprecedented legal requirements that will impact telcos as critical infrastructure players. For example, the proposed EU AI Act suggests fines for violating responsible and ethical AI guidelines, potentially reaching up to 6% of annual revenue for noncompliance. To safeguard sensitive data, it is essential for businesses to prevent their Gen AI models from accessing customer or company information. This involves employing advanced anonymization techniques like just-in-time anonymization or obfuscation to shield customer data from Gen AI public models. For instance, confidential information isreplaced with pseudo data before reaching the Gen AI model, with the original data restored afterward. Regulations push telcos to act quickly and tackle delivery challenges.However, according to global consultancy Bain & Company, doing this well and quickly can build trust among consumers, employees, and investors, making ethical AI acompetitive edge for telcos.
Telcos handle sensitive customer data, including personal information, call records, and location data. In this scenario, data regulations play a crucial role in protecting the privacy rights of customers. For example, the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the US have introduced new standards for data privacy. These laws specify how personal customer data should behandled, stored, and protected, with non-compliance potentially resulting in hefty fines. Companies should, however, focus on compliance with these regulations as essential to maintaining consumer retention and credibility, and not just to avoid penalties.
Chief Information Officers (CIOs) of CSPs are prioritizing investments in technologies like the cloud, AI, and ML to enhance operational efficiency.
As per Altman Solon, among early adopters of Gen AI, telcos in the top 30% for data proficiency are leading in leveraging the technology for purposes beyond productivity. These applications extend to revenue-generating tasks like product development and marketing. Such proficient organizations commonly feature dedicated AI centers ofexcellence, widespread use of advanced data analytics, and contemporary data infrastructures like cloud computing.
In its ‘Securing Generative AI’ report, Forrester emphasizes the importance for businessesto adopt a centralized and leadership-driven approach in scaling AI/ML and Gen AI within their organizations. With the rapid adoption of Gen AI, it advises businesses to train their security personnel in prompt engineering and prompt injection attacks. Additionally, it recommends establishing third-party risk management programs tocreate questionnaires and frameworks for evaluating Gen AI partners and suppliers. Italso advises business leaders to ensure that their compliance teams are equipped to navigate the legal landscape according to their company’s specific use cases. Moreover, Forrester highlights the significance of providing infrastructure teams with theknowledge to secure the technical integration and deployment of systems.
Amid growing demand for 5G infrastructure to accommodate advanced technologies, modern telcos are striving to ensure profitability while meeting customer demands for personalized products and services. By harnessing the power of Gen AI, telcos can tackle current business challenges by exploring new revenue streams, enhancing operational efficiency, and providing unique customer experiences, thus gaining a competitive edge.To successfully implement Gen AI, modern telcos must have a dedicated team or a partnerwho can work toward addressing data, regulatory, integration, and security concerns.
Previous Post
Next Post