Key managerial requirements for AI-based business transformations

September 2025  |  SPECIAL REPORT: DIGITAL TRANSFORMATION

Financier Worldwide Magazine

September 2025 Issue

Successfully implementing artificial intelligence (AI)-based projects in commercial applications requires not only the technical side of the AI to be done well, but also invoking a broader set of organisational capabilities to ensure that success results in business change rather than merely technology adoption.

We examined 10 detailed instances of companies that have successfully implemented AI applications of various kinds within their businesses. Our central question was: “what are the common organisational requirements that underpin success in implementing AI initiatives?”

We identified three categories of factors that underpin success with AI: internal behaviours, internal capabilities and external conditions of the organisation.

Internal behaviours

Of the behaviours that are required inside the organisation, the central and most foundationally important was proactive leadership. While ‘top management support’ is commonly cited as a critical success factor in many projects, our research suggests that successful organisations demonstrate management support at a significantly higher and more engaged level.

This was not a form of management support typically associated with asking for a business case and periodic status reports – proactive leadership in these cases meant that the most senior leaders in the organisation were personally committed to the initiative.

When this is strongly in place, leaders were future-focused, making it their business to have sufficient knowledge about AI and what it can do for them. They were also more likely to set up the resources and the projects that break down silos and resistance points to AI-based transformations and change.

Successful organisations also connected this proactive leadership to having an AI-sensitive tolerance of the risks of doing innovative AI projects. This requires managing rather than avoiding risk and willingness to experiment and push boundaries, and includes overcoming resistance as part of managing workforce expectations.

Another significant internal behavioural factor was having an innovation culture that is appreciative of the potential for new technology such as AI. This involved having processes that move AI projects forward and avoid stalling, empowering employees to implement AI projects, and enabling crisp, data-led decision making based on monitoring that progress.

Internal capabilities

In addition to behavioural success factors, our case study companies had important internal capabilities in common that provided the foundations for taking AI business transformations forward.

In common with many other types of initiatives, market and customer awareness – indeed ‘customer centricity’ and the ability to understand customer oriented value creation potential – was a central capability and focus.

A second key capability was to simultaneously run the business and change the business. Sometimes referred to as ‘ambidexterity’, this is key to overcoming the challenge whereby the ‘urgent crowds out the important’, where urgent refers to the business issues of the day that can, if allowed to, crowd out important and strategic initiatives, such as AI-based transformations which are key to the future of the organisation. This capability is directly related and built upon future focused proactive leadership.

A further internal capability that can and must be built is having AI-oriented data and information system readiness, and an orientation to take on AI capabilities. While this can be developed, it should not be underestimated as a key piece of investment.

The final internal capability common to successful AI implementers was the ‘strength of strategic process management’. Elements of this include an ability to make decisions, implement projects with focus and purpose, commit to coordinated platform development, and a high level of accountability for progress and project outcomes.

External conditions

AI business transformations tended to occur more forcefully and effectively in industries that were influenced by and subjected to high levels of industry dynamism and strong motivating drivers of opportunity and threat.

When industry dynamism is high, such that the very basis of competitive advantage may be changing, the motivation for business model evolution logically becomes strong. When this is combined with proactive leadership, the opportunity for AI based business transformation is more likely to be envisaged and acted upon.

Changes in the business environment include competitor actions, regulatory changes and evolving customer preferences and capabilities, which were particularly noticeable in some instances. AI was also able to be used in an incremental manner by some organisations to identify and respond to emerging market niches more effectively.

In the more radical cases, AI enabled business transformation can be used to reposition an organisation in its industry. One example is where AI builds onto a new or innovative digitalised platform that brings new value to customers, such as from customisation and personalisation of services, improved quality, or improved productivity and cost efficiency.
A further external factor was the extent to which an AI enabled business transformation became the right strategy to deal with external opportunities and threats. An example is the fear of industry disruptors such as digital pure plays, no-frills or low-cost start-ups that threaten a customer base and traditional revenue stream. Some of our case study organisations were motivated not so much by threat, but by proactive leaders who sensed that AI could deliver on potential opportunities, ranging from internal efficiency gains, through market share upsides from customisation or growth from new AI-based services or supports.

When it comes to AI being the means and indeed the weapon of choice for business transformation, the motivational factors and the strategic leadership and thinking required is well summarised by insights from two senior executives.

The first executive believes that companies that start with the mindset of ‘we need to do something with AI – what are some interesting use cases?’, are setting themselves up for failure. Instead, businesses should recognise that AI is fundamentally reshaping the economics of their value chains and removing barriers to delivering customer value. The focus should be on developing AI capabilities with urgency, exploring its potential thoughtfully, and implementing it with discipline.

The second executive suggests that AI should be considered as a foundational technology, much like others that have transformed industries throughout history. The real value does not come from surface-level applications, but from reimagining business models and leveraging the ripple effects – second and third-order changes that truly unlock its potential.

 

Danny Samson is professor of management and Stuart Black is an enterprise fellow at the University of Melbourne, and Alon Ellis is vice president of Capgemini Invent. Mr Samson can be contacted by email: d.samson@unimelb.edu.au. Mr Black can be contacted by email: stuart.black@unimelb.edu.au. Mr Ellis can be contacted by email: alon.ellis@capgemini.com.

© Financier Worldwide

BY

Danny Samson and Stuart Black

University of Melbourne

Alon Ellis

Capgemini Invent

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