Enterprise AI capability is not built through ambition alone.

It is built through disciplined experimentation, clear operating structure, and use cases that prove business value early.

Richard Fleming, Partner at Bain & Company, joins Kavitha Mistry, Chief Technology Officer at AMP, Artak Amirbekyan, Chief Data Officer at EBOS Group, and Dr Amy Shi-Nash, Professor at Monash University, Chief Executive Officer and Co-founder at Occubuy, and former Chief Data and Analytics Officer at Tabcorp, took the Data & AI Edge stage to to examine what actually helps organisations move AI from strategy into execution.

Key takeaways:

  • Early experimentation matters when it helps organisations test real opportunities, expose risks, and shape a more grounded AI strategy.
  • Enterprise AI scales more effectively when leadership backs it, ownership is clear, and capability building is structured across the business.
  • The best first use cases build credibility because they are tied to real business outcomes rather than novelty or generic efficiency claims.

 

Experimentation matters when it sharpens the strategy

AI programs get stronger when experimentation is used to test practical value, surface risk, and narrow the focus.

Without that phase, strategy stays too abstract.

Amy makes that point through her focus on early experimentation.

She argues that generative AI becomes more useful once teams begin testing where it can unlock value and where it introduces risk.

In her case, that included working with unstructured data and tools such as knowledge graphs to surface value from information that had been hard to use.

That work helped shape a more realistic strategy with clearer guardrails.

Structure and sponsorship determine whether AI adoption scales

Enterprise AI does not scale through isolated pilots or technical enthusiasm alone.

It needs executive backing, clear ownership, and a model that supports different forms of adoption across the business.

Kavitha outlines that through AMP’s approach.

She points to direct sponsorship from the CEO, a centralised AI hub, responsible AI frameworks developed with academic partners, and a flexible technology stack designed to avoid vendor lock in.

She also makes clear that adoption has to be tailored.

Productivity tools, customer experience applications, and domain specific use cases do not all need the same operating model, but they do need leadership support and structured capability building.

The right first use case creates momentum

AI programs gain traction when the first use case is tied to a real business problem and delivers a result the organisation can recognise.

That is what builds trust and creates room for broader adoption.

Artak brings that discipline into focus.

He argues that use cases need to connect directly to business value, whether that is fraud detection, better explainability, or operational efficiency.

His examples also show that AI value does not always show up in obvious places.

Fraud detection delivered stronger explainability, while ventilation optimisation created sustainability gains.

The point is not to prove that AI works in theory, but to choose a starting point that proves where it matters.

Enterprise AI capability improves when organisations experiment early, build the right structure around adoption, and choose the first problems carefully.

That is what turns AI from activity into execution.

Contributors
Dr. Amy Shi-Nash Professor at Monash University | CEO & Co-founder at Occubuy | Former Chief Analytics & Data Officer at Tabcorp
Amy is a purpose-driven executive, focused on creating value and competitive advantage through customer obsession and future ready capabilities. She has 25... More

Amy is a purpose-driven executive, focused on creating value and competitive advantage through customer obsession and future ready capabilities. She has 25 years’ experience in Gaming, Banking, Telecoms, Retail, Technology across three continents. Her career is grounded in deep technical expertise, strong commercial acumen, and a passion for delivering meaningful, scalable outcomes for both Customers and Organisations.

As former Chief Analytics and Data Officer at Tabcorp, Amy led its analytics and AI strategy and execution, drove significant business transformation and profitability growth, next-gen customer experience while upholding regulatory compliance and player safety. Previously, Amy held senior leadership positions at CBA, HSBC, and NAB, leading large-scale analytics and data science functions, driving strategic initiatives such as data management modernisation, data-driven decision making and advanced risk practice across complex and highly regulated environments. Amy also co-founded DataSpark, a pioneering data monetisation spin-off, taking it from inception to commercial success, scaling the business into five countries. Through these roles, Amy has demonstrated significant leadership in strategy and innovation, building positive culture and delivering commercial success.

Amy serves in various external roles, such as a Data Board member at MIT Sloan Management School’s CISR and a Board Member at Mental Health Innovations. She was appointed as independent member of AI Public Private Forum by Bank of England and Financial Conduct Authority. Amy is named one of the Top 30 Women Tech Leaders in Australia and Global Top 100 Innovators in Data and Analytics. Her commitment to promoting positive social impact and data-driven innovation continues to make a significant impact both within and outside her professional endeavors.

Amy has a Ph.D in Data Mining, a MSc in AI, a BSc in Computer Science and an MBA. She is also a Graduate member of the Australian Institute of Company Directors (GAICD). She was awarded an Honorary Doctorate for her leadership and contributions in the field of data science.

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Kavitha Mistry CTO at AMP
Kavita Mistry is an accomplished technology leader with expertise in driving transformational change to deliver strategic and commercial objectives. She has more... More

Kavita Mistry is an accomplished technology leader with expertise in driving transformational change to deliver strategic and commercial objectives. She has more than 20 years’ experience across a variety of technology roles specialising in financial services, including superannuation, investments, digital, data, lending, and corporate applications. 

Prior to AMP Kavita was at AustralianSuper, where she held the roles of co-acting-CTO and Head of Enterprise Technology. There, she established and transformed technology capabilities across investments, member experience, cloud infrastructure, employee experience, data, CRM and enterprise technology assets. Prior to this, Kavita held various senior positions over 14 years at ANZ, including leadership roles within Home and Business lending technology.

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Artak Amirbekyan Chief Data Officer at EBOS Group
Artak Amirbekyan, PhD is currently the Chief Data Officer at EBOS Group. Formerly, he was the Head of Data, AI, and ML... More

Artak Amirbekyan, PhD is currently the Chief Data Officer at EBOS Group.

Formerly, he was the Head of Data, AI, and ML at Transurban, where responsibilities included executing data and advanced analytics strategy and managing substantial CAPEX/OPEX budgets.

He recently won the the iTnews Benchmark Award for Best Industrial Project.Prior to this role, Artak was the Executive Manager of AI Labs at Commonwealth Bank, leading a large team of data scientists on strategic projects and capability building.

Earlier experience includes Data Science Manager at Caltex Australia, where Artak led a team focused on analytics capabilities and cloud infrastructure, and Senior Product Data Analyst at CoreLogic RP Data, specialising in predictive modeling and customer analytics.

Artak’s career also encompasses roles as a Computational Scientist at ESSCC, The University of Queensland, and as a Research Assistant at Fraunhofer ITWM, focusing on mathematical modeling and image processing.

Artak Amirbekyan holds a PhD in Data Mining from Griffith University and a Master’s degree in Industrial Mathematics with a focus on Image Processing from the University of Kaiserslautern.

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Richard Fleming Partner at Bain & Company
Richard Fleming is the leader of our Asia-Pacific Advanced Analytics practice. He is also a leader in our Transformation and Change practice.... More

Richard Fleming is the leader of our Asia-Pacific Advanced Analytics practice. He is also a leader in our Transformation and Change practice.

He has more than 25 years of management consulting experience, advising business leaders on their biggest opportunities and challenges. He has supported clients with business transformation across strategy, customer experience, performance improvement, M&A, operating model design, digital, data and technology. His work is increasingly to support clients transform their businesses with digital, data and technology.

He has deep experience working with financial services clients in banking, insurance, wealth management and payments. Richard has also worked across industries including retail, consumer products, telecommunications, medtech and manufacturing.

He has applied his strategy consulting expertise globally, having served clients in the North America, Australia, the United Kingdom, and across Asia.

Prior to joining us in 2001, Richard worked for two other global consulting firms with a focus on technology.

He earned an MBA from Australian Graduate School of Management and a BA with Honors in physics from Oxford University.

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