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Accenture’s chief artificial intelligence (AI) officer Lan Guan works closely with companies across industries and geographies to develop data and AI strategies that drive value and growth.
She also leads the Accenture Center for Advanced AI —the AI epicentre that enables enterprises to capture and create value from their AI investment by leveraging deep expertise, differentiated scalable assets and solutions underpinned by data prowess and industry knowledge, and market-leading thought leadership and research. Guan also serves on the advisory board of the AI4All, a nonprofit organisation dedicated to increasing diversity and inclusion in AI education, research, development and policy.
Gulf Business spoke to Guan, who was at LEAP 2024 recently to showcase how AI should be driving enterprise reinvention. Here are excerpts from the discussion.
Tell us what Accenture showcased at LEAP 2024. What did you focus on during your talk?
At Leap 2024, we turned the spotlight on our Gen AI strategy, emphasising the firm’s commitment to driving enterprise reinvention through data and AI. As chief AI officer, my focus is on highlighting the significant impact of AI across various fronts. We are investing $3bn in data and AI over the next three years, underscoring Accenture’s dedication to scaling impact and driving value across the entire value chain.
During my presentation at LEAP, I emphasised the importance of moving beyond siloed use cases and focusing on scaled impact.
We discussed how businesses should prioritise enterprise reinvention and leverage cloud, data, and AI capabilities as foundational elements. Additionally, I underscored the importance of addressing workforce reskilling and upskilling to thrive in the era of Gen AI, emphasising the importance of responsible AI practices.
We also delved into the societal impact of Gen AI, discussing its role in bridging the digital divide and promoting inclusion and diversity. I shared examples of startups utilising Gen AI to improve food safety and population health, as well as its implications for education and critical thinking. Responsible AI practices and the impact of Gen AI on humanity were also key topics of discussion.
It was an engaging and fruitful experience, exchanging insights and common beliefs with industry leaders and stakeholders.
In what key areas do you perceive governments, companies and individuals having the potential to influence the deployment of responsible AI?
In driving the mission of responsible AI deployment, several key areas stand out where governments, companies, and individuals can exert influence.
Firstly, education plays a crucial role in promoting AI literacy and addressing ethical considerations such as privacy and security implications. Scepticism surrounding new technologies is natural, and governments need to educate communities and businesses to mitigate risks associated with AI, including biases and misinformation.
Government engagement through educational programmes is vital in alleviating public apprehension and fostering understanding of AI’s potential and limitations.
Secondly, infrastructure is paramount in embedding responsible AI principles at its core. Implementing responsible AI by design ensures proactive measures rather than reactive responses to incidents. Infrastructure improvements, such as secure data sharing and utilisation of secure cloud services, are essential in fostering a robust foundation for responsible AI deployment.
Regulation also plays a crucial role in governing AI deployment, ensuring adherence to ethical standards and safeguarding against potential risks. Collaborative efforts between governments, companies, and individuals are necessary to navigate the complexities of AI regulation effectively.
Overall, education, infrastructure, and regulation emerge as pivotal areas where concerted efforts are required to advance the responsible deployment of AI for the benefit of society.
So, you are talking to companies and advising them on how to use AI responsibly?
Absolutely. Advising companies on how to use AI responsibly is a significant component of our offering.
Our research has revealed that only 2 per cent of the companies we’ve worked with have implemented systematic, responsible AI practices. This indicates that there is still a substantial gap in responsible AI adoption, with 30 per cent of companies just beginning to address this issue, leaving the remaining 70 per cent with non-existent responsible AI practices.
This lack of responsibility poses a significant barrier to AI adoption, as companies may hesitate to embrace technology due to concerns about its ethical implications.
To address this challenge, we advocate for education and transparency in AI usage. By ensuring that companies are well-informed about responsible AI practices, they can utilise technology transparently and ethically.
In essence, our strategy revolves around educating companies to use AI responsibly, fostering transparency, and promoting ethical practices in AI deployment. This approach is essential to overcoming barriers to adoption and harnessing the full potential of AI for positive impact.
Tell us about the highlights of Accenture’s Technology Vision 2024 report.
One of our key findings from Accenture’s Technology Vision Report 2024 revolves around the continued prominence of AI. This year’s report emphasises the significance of AI scaling, outlining imperatives that companies should prioritise to effectively scale AI initiatives.
Furthermore, the report underscores the importance of human-machine collaboration, highlighting the evolving role of autonomous agents. Building upon previous discussions about the augmentation of human intelligence by machines, we now explore how autonomous agents can enhance human-machine collaboration.
These agents possess long-term memory and environmental awareness, enabling them to orchestrate various AI activities within ecosystems. This development represents an exciting advancement in the realm of AI technology.
What are the industries that AI is disrupting?
Well, there are several industries that we see AI transforming. These include banking, insurance, retail/consumer goods, and the public sector, particularly healthcare and life sciences. These industries have been early adopters of AI and have implemented scalable programs across various domains.
In terms of specific use cases, we’ve observed AI being utilised for marketing hyper-personalisation, content supply chain management, creative generation and customer care and contact centres. Additionally, industries such as insurance and banking are using AI for faster processing of insurance claims and consumer loan approvals.
In life sciences, AI is employed for drug discovery and streamlining manual efforts during clinical trials. It’s noteworthy that no industry wants to be left behind in adopting AI. Even traditional sectors such as steel manufacturing are embracing AI to automate administrative tasks and increase efficiency.
One compelling example is a 150-year-old steel plant in North America that approached us to explore AI adoption. They recognised the potential of AI to automate mundane tasks and reinvest cost savings into modern, sustainable technologies. This story illustrates the democratisation of AI adoption across industries, highlighting the importance of education and responsibility in AI deployment.
We believe in democratising AI adoption while emphasising the need for responsible use. It’s essential to educate businesses and citizens to embark on their AI journey at their own pace, leveraging data for evidence-based decision-making.
Accenture recently launched Accenture LearnVantage to help clients and employees gain essential skills for the AI economy. Tell us more about it.
The LearnVantage programme is indeed an exciting initiative, especially in the realm of AI learning. This new service is meticulously designed to assist leaders across various industries and government entities in swiftly identifying skill gaps pertinent to their fields. These skill gaps, often propelled by technological advancements, are seamlessly addressed through industry-specific training programmes, ensuring rapid upskilling at scale.
Drawing upon our extensive experience in internal employee training, including the successful launch of the TQ (Technology Quotient) programme, which focused on technology coaching, and the ongoing efforts in AI learning, LearnVantage amalgamates these experiences. By leveraging these capabilities, we aim to foster industry-building skills among our clients, communities, and government organisations. This initiative not only sets us apart but also serves as a means to contribute to societal welfare through the profound impact of AI.
It’s crucial to recognise that this initiative aligns with a key dependency, equipping individuals with the necessary skills to thrive in an AI-driven landscape. I consider myself fortunate to have been immersed in AI and data work for over two decades. Now, I’m dedicated to ensuring that our workforce grows from 40,000 to 80,000, with our employees receiving similar growth opportunities. LearnVantage provides individuals with the chance to venture into AI, regardless of their prior experience or background.
The enthusiasm and inquiries we receive from colleagues eager to delve into AI underscore the importance of initiatives like LearnVantage. We are thrilled that our leadership has recognised the significance of investing in this space, and I’m personally excited to witness the transformative impact it will have on individuals and organisations alike.
As a female leader, tell us how you support the advancement of women in fields such as AI.
Mentoring women in AI is a topic I’m incredibly passionate about. Firstly, I believe women are exceptionally well-suited for AI and data work due to the unique skills we bring, including attention to detail. It’s crucial to recognise and leverage these strengths. Moreover, improving gender balance in the industry is imperative, especially given that we train AI systems.
Ensuring a balanced gender workforce in AI is essential to mitigate biases that could lead to detrimental consequences. For instance, AI systems have exhibited biases in various scenarios, such as assuming gender roles based on societal norms.
To address this issue, we must take a multi-faceted approach. Firstly, emphasising the importance of STEM education is crucial. Additionally, women must take proactive steps to enhance their skills and credentials, such as pursuing certifications in relevant areas like cloud architecture.
Networking is also vital, despite its challenges. I often encourage women to adopt the “rule of 72”, which involves making a specific number of contacts each month, breaking it down into manageable weekly tasks. This approach helps strengthen professional networks over time.
Furthermore, mentorship and coaching play pivotal roles in career advancement. Establishing mentorship relationships early on can provide valuable guidance and support throughout one’s career journey.
I believe it’s a collective effort, and each of us has a role to play in fostering an inclusive and diverse AI workforce.
On a concluding note, what are the key opportunities and challenges of AI adoption?
One of the most significant opportunities lies in the democratisation of AI. This means that the widespread impact of AI won’t be limited to just a few tech giants advancing their AI capabilities. Instead, it will involve smaller and medium-sized businesses, as well as individual citizens, embracing AI technology. I often refer to this as “lifestyle AI”, envisioning a future where AI seamlessly integrates into our daily lives. If we collectively work towards this goal, AI could eventually become invisible, seamlessly woven into our routines and interactions.
However, a major challenge in realising this vision lies in addressing the skills gap. Investing in skills development, reskilling, and upskilling is essential to bridge the knowledge and mindset gaps that currently exist.
By equipping individuals and organisations with the necessary AI skills, we can unlock the full potential of AI adoption and drive positive societal change.