Accelerating AI Adoption

By collaborating closely and gaining a deeper understanding of the needs of every stakeholder, data experts can deliver a continuous stream of actionable insights that the pod can learn from and use to refine further development.

Accepting a new mindset

Although the benefits are high, the rapid pace of technological change has brought about significant challenges and risks for companies looking to embrace AI transformation. Implementing AI systems requires access to high-quality and relevant data, considerable computing power, and the ability to address factors outside of technology itself, such as security and privacy compliance, end-user engagement, and trust in the insights generated. Furthermore, a willingness to change and transform work is critical for success, and the failure to consider these factors can result in projects that do not produce meaningful financial outcomes.

In order to reduce these risks, companies need to adopt an agile, explorative and adaptable mindset. TELUS has been using a Multi-Armed Bandit approach to structure our prioritizations. Having a risk-weighted metric to compare opportunities helps us better select the right focus. Doing so, we are creating an effective "fast-experimental" culture where teams are encouraged to recognize when a project is unlikely to deliver the full expected outcome and when time has come to move onto the next best opportunity. With an incentive to constantly explore for alternatives, each squad can reduce the time spent on projects that may not produce a meaningful outcome and maximize time spent on successful projects.

It is also important to track the right performance metrics and monetize at each stage of a project - not only once completed. Starting with insights in the form of consolidated data or statistics can be a great way to prove value early on, before moving up to more powerful tools that AI technologies are providing.

Prioritizing digital safety

With the switch to AI, companies require a strong foundation in data management, collaboration across communities of data scientists and a relentless focus on prioritization on common goals. Equally important are processes required to efficiently ingest and then manage our data assets into a cloud environment while following high privacy and security standards to protect customers and employees.

All of these technologies can come with risks. Without question, a safe, humanized AI experience comes through the intentional consideration of the impact that the use of data has on people and ensuring that there is a benefit every step of the way.

The TELUS approach to responsible AI builds upon our Customers First commitment and is structured on the TELUS Trust Model by using data in a way that builds trust by generating value, promoting respect and delivering security. Our process begins with clear accountability for our use of AI, ensuring that the technology is the right fit for the circumstances. From the outset, we are thinking about the particular beneficial outcome we are working towards and keeping fairness and the human impact top of mind. This approach includes planning for how we will explain the model and establish ongoing monitoring to ensure it continues to work as intended. We use robust tools and processes which build upon our foundation of Privacy and Security By Design principles. By considering potential bias and using tools that allow users to explain how AI models arrived at a conclusion, we actively strive for fairness through the responsible use and enablement of data. For example, large language models (LLMs) are trained on billions of human created data sets which contain bias, stereotypes and yes - mistakes. However, with the added layer of human oversight, we can help to potentially mitigate those issues to realize further benefits.

A look ahead

AI solutions are meant to boost productivity and allow us to be more efficient but there they all required oversight in terms of reliability, responsibly generated content and ensuring we continue innovating and creating new ideas, not regurgitating the same ones

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However, this adoption of AI is truly transforming the way companies operate and create value for their customers, while also allowing companies to be more socially responsible. Embracing digital transformation, driving collaboration, prioritizing digital safety and accepting a new mindset are crucial as we move forward in ensuring that AI projects are successful. But we know that by embracing this change, we can tailor offerings and services to help meet specific needs and create a stronger and improved experience for our operations and most importantly – our customers.