The little helper around the office?
As well as major projects such as smart sewer systems, AI may also be proving its value in the smaller admin-type jobs that help companies run on a day-to-day basis – taking notes during meetings, producing content, and helping with procurement processes, points out the CIO of another water company. “I’m interested in how people are finding AI for the in-office experience.”
The power network CIO explains the company has taken its first tentative steps in this area by providing 200 licences of Copilot to a cross-section of the business to understand where the biggest gains are. “That was successful in highlighting where we would get the best benefits.” Users need to be made aware of the capabilities Copilot offers and feel able to ask questions, he adds. Eighty-six percent of the target group were now using Copilot productively thanks to this effort. (Wipro believes that Copilot has the potential to continually reduce costs.)
This type of widespread usage, the CIO believes, is an inevitability. “There is a pervasive creep of AI across all technology. There is not much you procure that will not have an AI component. So, we need to make sure people have enough awareness to leverage the optional aspects of AI in the tools they are given.”
The head of data and analytics at another network says what we have seen with generative AI is “excitement about doing tasks and the ability to query large volumes of data and get it in front of people to increase productivity”. But, bearing in mind the cost issue that opened the debate, they cautioned: “It’s really expensive to run generative AI solutions across the organisation. Not only do you need the tech skills, but you also need the stack, and good quality data.
“Add in the fact that it’s expensive to the environment and you really need to know what you are going after.”
What are the next steps you want to take with AI?
We asked Digital Think Tank participants what they intended to do next with the technology. Here are their answers:
Establish a central inventory of AI use cases, including embedded AI, purchased, outsourced and built AI solutions, supporting our understanding of value, priority, and risk.
Continue building our repository of AI frameworks, guardrails, and best practices, working across cyber, legal, and AI practitioners' communities.
Ensure AI training, awareness, and acceptable use with Copilot.
Continue to develop the scope of our AI CoE and AI working group to foster closer collaboration, knowledge sharing, and targeting AI use cases that have synergies and repeatable patterns.
Scale AI by moving use cases from lab to production, ensuring they are supported with the right skills, practices, support models, and security frameworks.
Develop an LLM assurance service to ensure deployed LLMs are compliant with internal risk and legal frameworks, speeding up innovation.
Investigate enterprise model management and prompt engineering capabilities to ensure models are trained, secure, and remain compliant with risk and legal frameworks.
Continue expanding use cases carefully, focusing on high-impact areas while building the skills and infrastructure needed to support them responsibly.
Pushing to increase adoption in secure environments like Copilot, alongside ongoing training to keep users up to speed and unlock real productivity gains.
Embedding the use of an AI-based tool for knowledge management to enable us to deliver greater insights and join up teams.
AI and data science-led triage for change management and development opportunities.
Exploring internal governance around AI tools. Thinking about board and executive level training.
Incorporation of AI enabled capabilities within work management solutions, delivering benefits to front-line operational staff.
Getting Copilot rolled out more widely, delivering an AI strategy and governance framework, and developing the capability to start identifying initial use cases and taking them into exploitation.
Internal and industry-wide knowledge management.
How to implement AI
Reflecting on the latest session of the Digital Think Tank, Wipro has identified several strategic actions that companies should consider in order to leverage AI effectively.
S.Hariharan, consulting partner, utilities
Kishor Gowdra, industry cluster head for energy, utilities, and manufacturing
First and foremost, it's crucial to focus on value rather than just hype. Companies should be realistic about what AI can achieve and start with low-risk, high-value projects. By selecting AI use cases based on weighing up risk versus value, carefully chosen small pilots can spark interest and gather momentum, leading to more significant changes over time. Additionally, rethinking processes is essential as AI implementation often requires reengineering existing workflows. This reengineering can help streamline operations and make them more efficient, ultimately leading to better outcomes.
Another critical factor is developing the skills and knowledge of staff involved in AI projects. Ensuring that employees are well-trained and aware of AI's capabilities can help maximise its benefits. This involves not only technical training but also fostering a culture of continuous learning and innovation. Moreover, having a clear strategy with a view of the risks and how to mitigate them is vital for successful AI adoption. Companies need to be aware of potential pitfalls and have contingency plans in place to address any issues that may arise.
Data quality is also of paramount importance. While perfect data is rare, it's essential to work with data that’s good enough to gain insights and improve operations. Establishing a centralised inventory of AI use cases and building a repository of AI frameworks, guardrails, and best practices can support responsible AI usage.
"While perfect data is rare, it's essential to work with data that’s good enough to gain insights and improve operations."
Reflecting on the latest session of the Digital Think Tank, Wipro has identified several strategic actions that companies should consider in order to leverage AI effectively.
S.Hariharan, consulting partner, utilities
First and foremost, it's crucial to focus on value rather than just hype. Companies should be realistic about what AI can achieve and start with low-risk, high-value projects. By selecting AI use cases based on weighing up risk versus value, carefully chosen small pilots can spark interest and gather momentum, leading to more significant changes over time. Additionally, rethinking processes is essential as AI implementation often requires reengineering existing workflows. This reengineering can help streamline operations and make them more efficient, ultimately leading to better outcomes.
"While perfect data is rare, it's essential to work with data that’s good enough to gain insights and improve operations."
Another critical factor is developing the skills and knowledge of staff involved in AI projects. Ensuring that employees are well-trained and aware of AI's capabilities can help maximise its benefits. This involves not only technical training but also fostering a culture of continuous learning and innovation. Moreover, having a clear strategy with a view of the risks and how to mitigate them is vital for successful AI adoption. Companies need to be aware of potential pitfalls and have contingency plans in place to address any issues that may arise.
Data quality is also of paramount importance. While perfect data is rare, it's essential to work with data that’s good enough to gain insights and improve operations. Establishing a centralised inventory of AI use cases and building a repository of AI frameworks, guardrails, and best practices can support responsible AI usage.
Embedding AI into everyday operations, such as using AI-based tools for knowledge management and improving quality assurance, can bring efficiencies and enhance productivity.
This centralised approach ensures that AI initiatives are aligned with the company’s overall strategy and that best practices are consistently applied across the organisation. Furthermore, it is worth recognising the importance of breaking the disconnect between OT and AI, to maximise network information to deliver predictive analytics, condition based / reliability-centred maintenance and improve safety of operations.
Lastly, embedding AI into everyday operations, such as using AI-based tools for knowledge management and improving quality assurance, can bring efficiencies and enhance productivity.
By taking these steps, companies can ensure that their AI initiatives are both cost-effective and ambitious, leading to significant improvements in their operations and customer experience. For example, AI can be used to automate routine tasks, freeing up employees to focus on more strategic activities. Additionally, AI can help identify patterns and trends that may not be immediately apparent, providing valuable insights that can inform decision-making,
Embedding AI into everyday operations, such as using AI-based tools for knowledge management and improving quality assurance, can bring efficiencies and enhance productivity.
This centralised approach ensures that AI initiatives are aligned with the company’s overall strategy and that best practices are consistently applied across the organisation. Furthermore, it is worth recognising the importance of breaking the disconnect between OT and AI, to maximise network information to deliver predictive analytics, condition based / reliability-centred maintenance and improve safety of operations.
Lastly, embedding AI into everyday operations, such as using AI-based tools for knowledge management and improving quality assurance, can bring efficiencies and enhance productivity.
By taking these steps, companies can ensure that their AI initiatives are both cost-effective and ambitious, leading to significant improvements in their operations and customer experience. For example, AI can be used to automate routine tasks, freeing up employees to focus on more strategic activities. Additionally, AI can help identify patterns and trends that may not be immediately apparent, providing valuable insights that can inform decision-making,
The next Digital Think Tank session will take place at Utility Week Live in May – stay tuned for more insights.
Wipro Limited is a leading technology services and consulting company focused on building innovative solutions that address clients’ most complex digital transformation needs. Leveraging our holistic portfolio of capabilities in consulting, design, engineering, cybersecurity and operations, we help clients realise their boldest ambitions and build future-ready, sustainable businesses. With over 230,000 employees and business partners across 65 countries, we deliver on the promise of helping our customers, colleagues, and communities thrive in an ever-changing world. For additional information, visit us at www.wipro.com
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