Are CSPs ready to take on AI?
08 Jul 2019
Are CSPs ready to take on AI?
TM Forum’s new AI Readiness Check helps communications service providers (CSPs) harness the potential of artificial intelligence (AI) by identifying gaps across six key dimensions of their business: strategy; operations; culture, people and organization; data; party and technology.
Telcos have big plans for AI to help them process and understand data, enable automation, reduce costs and much more. But they are just at the beginning of harnessing the technology and need to make sure they do it right.
According to IDC, global spending on cognitive and AI solutions will increase at a compound annual growth rate of 54.4% over the next few years, exceeding $46 billion by 2020. This is no small expenditure and demonstrates the risk in getting implementation wrong.
As explained in TM Forum’s recent research into AI and customer experience, “Success is not just about understanding AI, but working across an organization to create use cases and identify points of commonality.”
Indeed, a survey by McKinsey shows that “many organizations still lack the foundational practices to create value from AI at scale – for example, mapping where their AI opportunities lie and having clear strategies for sourcing the data that AI requires.”
Where to start?
So how do CSPs begin, or continue, their journeys and ensure they are on the right path? There is, of course, no single route to AI readiness, but a structured deep dive into CSPs’ preparedness can help.
At Digital Transformation World last month, TM Forum launched the AI Readiness Check which has been developed through collaboration between leading players across the telecoms industry and is supported by global service providers, including Axiata, BOCO, BONC, BT, China Mobile, China Telecom, China Unicom, Deutsche Telekom, and PCCW-HKT. The AI Readiness Check is aligned with the TM Forum Digital Maturity Model and helps CSPs harness AI’s potential by identifying gaps across six key dimensions of their business:
“Using the AI Readiness Check, service providers can diagnose areas of improvement with technology or related services,” says Aaron Boasman-Patel, Vice President, AI & Customer Centricity, TM Forum. “Going forward, CSPs will be able to benchmark their AI readiness against their peers.”
Below, we examine why companies need to examine their AI readiness in these six areas.
As with all other kinds of business transformation, AI adoption must first start with a viable strategy to define, and focus on, primary business objectives, and prioritize the various ways AI can help the company meet these objectives. AI can pay off in many ways for the telco, but needs to fit in with the company’s broader digital strategy, while budgets must also be considered, and success metrics/KPIs agreed upon.
Once the relevant strategies are in place, operators will need to use them to embed AI into their operations to meet their goals, particularly in terms of governance, service lifecycle management and service assurance.
Applying AI means the business runs very differently, shifting from manual operations – with much human intervention – to being fully automated. All processes need to be changed, so it’s critical to know how to correctly conduct an operational gap analysis. Other aspects to consider include AI governance, such as closed-loop service assurance, which is new for most CSPs.
An AI mindset necessitates cultural change to support the deployment of new technologies and new ways of working. Nurturing a culture of continuous learning is crucial. A lot of companies are reticent about embracing AI because it will be a cultural shift. Companies will also need to arm their own people, with the different skills needed to adopt AI, and employ new people who already have these skills – people who have been trained around data science, understand the algorithms, and comprehend the pace and needs of digital change.
The right leadership and culture is also crucial as an AI mindset can only be embedded from the top down.
“They need to understand that it’s not about losing jobs to AI; it’s about retraining people and ensuring they can work with AI,” Boasman-Patel explains.
AI needs data, and more importantly good quality data, to enable successful operations. Good data governance, curation and capitalization can ensure this. For AI, a company’s data must be machine readable, accessible, and importantly, there must be a consistent data model across all parties.
Furthermore, companies have gone from having siloed data, to having data lakes that are fast becoming data swamps. All data must be intact, machine readable, and should follow a consistent data format and modelling data schema so that the machine can read it, and so that the company gets intelligent insight.
“The whole thing about data is not only the machine-readable aspect of it, which is very important,” says Boasman-Patel. “You’ve got to have the right governance around it. What is the governance around that data? If someone's going to corrupt that data, how can you trace it back?”
He adds: “You’ve got to have the right curation and the right data capitalization. What data do you need that you didn’t need before? Because AI is only as good as the data that you have. If you want a truly robust AI sequence in place, you have to be able to connect as much data as possible.”
To be truly customer centric, organizations must understand the experience and satisfaction of customers, partners, employees and all other engaged parties. There must be trust to use products and services offered by the organization without privacy and security concerns. AI products and services must also offer human-like experiences through their engagement channels to effectively drive adoption.
“How do you measure your party's experience with AI?” Boasman-Patel asks. “How do you get insights from other companies that you might be partnering with? What is the trust, and how do you manage trust correctly?”
He suggests all telcos ask this of themselves to know whether they are truly meeting the needs of all involved.
Last but certainly not least, the technology considerations are many because there are so many types of AI technologies. Companies need to pinpoint the specific ones needed for their business, determine whether and how they need to integrate it with their existing systems, and establish an open AI architecture.
Relevant technologies include machine learning (as well as deep learning), natural language processing, speech recognition, image recognition, sentiment analysis, forecasting, pattern recognition and anomaly detection, chatbots and other technologies.
Because of a large number of parties within an AI ecosystem, technologies should be standardized where possible, with the tools to process information in line with industry-recognized methods for common languages and methods to ensure interoperability and seamless operations.
Visit the AI Readiness Check page to find get details about what the check covers and how it can help your organization. If you’d like to learn more about how to get involved in TM Forum’s AI collaboration projects, please contact Aaron Boasman-Patel via aboasman@tmforum.org.
Telcos have big plans for AI to help them process and understand data, enable automation, reduce costs and much more. But they are just at the beginning of harnessing the technology and need to make sure they do it right.
According to IDC, global spending on cognitive and AI solutions will increase at a compound annual growth rate of 54.4% over the next few years, exceeding $46 billion by 2020. This is no small expenditure and demonstrates the risk in getting implementation wrong.
As explained in TM Forum’s recent research into AI and customer experience, “Success is not just about understanding AI, but working across an organization to create use cases and identify points of commonality.”
Indeed, a survey by McKinsey shows that “many organizations still lack the foundational practices to create value from AI at scale – for example, mapping where their AI opportunities lie and having clear strategies for sourcing the data that AI requires.”
Where to start?
So how do CSPs begin, or continue, their journeys and ensure they are on the right path? There is, of course, no single route to AI readiness, but a structured deep dive into CSPs’ preparedness can help.
At Digital Transformation World last month, TM Forum launched the AI Readiness Check which has been developed through collaboration between leading players across the telecoms industry and is supported by global service providers, including Axiata, BOCO, BONC, BT, China Mobile, China Telecom, China Unicom, Deutsche Telekom, and PCCW-HKT. The AI Readiness Check is aligned with the TM Forum Digital Maturity Model and helps CSPs harness AI’s potential by identifying gaps across six key dimensions of their business:
- Strategy
- Operations
- Culture, People & Organization
- Data
- Party
- Technology
“Using the AI Readiness Check, service providers can diagnose areas of improvement with technology or related services,” says Aaron Boasman-Patel, Vice President, AI & Customer Centricity, TM Forum. “Going forward, CSPs will be able to benchmark their AI readiness against their peers.”
Below, we examine why companies need to examine their AI readiness in these six areas.
Strategy
As with all other kinds of business transformation, AI adoption must first start with a viable strategy to define, and focus on, primary business objectives, and prioritize the various ways AI can help the company meet these objectives. AI can pay off in many ways for the telco, but needs to fit in with the company’s broader digital strategy, while budgets must also be considered, and success metrics/KPIs agreed upon.
Operations
Once the relevant strategies are in place, operators will need to use them to embed AI into their operations to meet their goals, particularly in terms of governance, service lifecycle management and service assurance.
Applying AI means the business runs very differently, shifting from manual operations – with much human intervention – to being fully automated. All processes need to be changed, so it’s critical to know how to correctly conduct an operational gap analysis. Other aspects to consider include AI governance, such as closed-loop service assurance, which is new for most CSPs.
Culture, People & Organization
An AI mindset necessitates cultural change to support the deployment of new technologies and new ways of working. Nurturing a culture of continuous learning is crucial. A lot of companies are reticent about embracing AI because it will be a cultural shift. Companies will also need to arm their own people, with the different skills needed to adopt AI, and employ new people who already have these skills – people who have been trained around data science, understand the algorithms, and comprehend the pace and needs of digital change.
The right leadership and culture is also crucial as an AI mindset can only be embedded from the top down.
“They need to understand that it’s not about losing jobs to AI; it’s about retraining people and ensuring they can work with AI,” Boasman-Patel explains.
Data
AI needs data, and more importantly good quality data, to enable successful operations. Good data governance, curation and capitalization can ensure this. For AI, a company’s data must be machine readable, accessible, and importantly, there must be a consistent data model across all parties.
Furthermore, companies have gone from having siloed data, to having data lakes that are fast becoming data swamps. All data must be intact, machine readable, and should follow a consistent data format and modelling data schema so that the machine can read it, and so that the company gets intelligent insight.
“The whole thing about data is not only the machine-readable aspect of it, which is very important,” says Boasman-Patel. “You’ve got to have the right governance around it. What is the governance around that data? If someone's going to corrupt that data, how can you trace it back?”
He adds: “You’ve got to have the right curation and the right data capitalization. What data do you need that you didn’t need before? Because AI is only as good as the data that you have. If you want a truly robust AI sequence in place, you have to be able to connect as much data as possible.”
Party
To be truly customer centric, organizations must understand the experience and satisfaction of customers, partners, employees and all other engaged parties. There must be trust to use products and services offered by the organization without privacy and security concerns. AI products and services must also offer human-like experiences through their engagement channels to effectively drive adoption.
“How do you measure your party's experience with AI?” Boasman-Patel asks. “How do you get insights from other companies that you might be partnering with? What is the trust, and how do you manage trust correctly?”
He suggests all telcos ask this of themselves to know whether they are truly meeting the needs of all involved.
Technology
Last but certainly not least, the technology considerations are many because there are so many types of AI technologies. Companies need to pinpoint the specific ones needed for their business, determine whether and how they need to integrate it with their existing systems, and establish an open AI architecture.
Relevant technologies include machine learning (as well as deep learning), natural language processing, speech recognition, image recognition, sentiment analysis, forecasting, pattern recognition and anomaly detection, chatbots and other technologies.
Because of a large number of parties within an AI ecosystem, technologies should be standardized where possible, with the tools to process information in line with industry-recognized methods for common languages and methods to ensure interoperability and seamless operations.
Visit the AI Readiness Check page to find get details about what the check covers and how it can help your organization. If you’d like to learn more about how to get involved in TM Forum’s AI collaboration projects, please contact Aaron Boasman-Patel via aboasman@tmforum.org.
