A subtle but vital shift is under way in how artificial intelligence is embraced and treated in most large businesses and organisations.
If AI has not moved from an experimental stage to widespread use and the debate over whether to implement AI has not been replaced by an urgency to deploy it as rapidly as possible in a responsible and sustainable manner, your organisation may be falling behind the curve.
Implementing AI is not simply a technology or process issue, contrary to the misconception that may exist amongst many leaders across business and government. Successful implementation brings about a significant transformation of an organisation. This includes changes to its talent models, governance structures and the way employees will interact with one another (and potentially with AI agents).
Whether in banking, mining, manufacturing or government, few organisations manage to make the leap from isolated AI pilots to widespread enterprise impact. Rarely is the cause exclusively technological. The harder issues involve alignment of leadership, readiness of talent, maturity of governance and enterprise culture.
Successfully scaling AI requires a clear organisational vision, the willingness to challenge the status quo and a deliberate strategy to close both capability and mindset gaps.
From strategic intent to execution
Most businesses already have very strong strategic intent for AI (both traditional and generative AI. Senior management understands the potential value of AI in improving efficiency, gaining insights and achieving a competitive advantage. But we know that intent and strategy are two different things, and bridging the gap between the two – so that execution can be clearly mapped – is in a critical phase.
The gap happens when vision fails to translate into appropriate plans and measures of success. The vision for AI must become directional. Put another way, this means that the state the organisation is aiming for is well understood, along with how AI contributes to the business’s overarching purpose. However, just as important is how that vision is communicated throughout the company, creating a shared understanding of the vision.
The process of transforming through AI begins with strong leadership, however, it should never end there. All leaders must have a common understanding of the “art of the possible”, the inherent risk associated with AI (hallucinations, bias, data privacy) and the investment that will be needed for an AI initiative to be successful.
Start with the end in mind. The next step in developing an AI strategy and an adaptable acceleration plan is to clearly identify what success looks like, using both leading and lagging indicators, and then develop a system to continually assess progress towards achieving the identified success factors. Without establishing clear success factors before starting AI initiatives, those efforts will very likely end at the proof-of-concept phase, delivering minimal value to the organisation.
Redefining roles and closing the capability gap
The change that AI adoption brings will inevitably reshape the talent landscape of a business, government and economy. New roles are emerging. Machine learning, AI solution architects, AI product managers, ML operations specialists and even AI business analysts who can bridge business needs with technical feasibility are in demand. In many cases, these roles did not exist just a few years ago.
This needs to also be augmented by AI native change agents (or champions) that will be able to lead, guide and coach the organisation on this journey as part of their current role.
But the scope of AI’s transformative impact on businesses makes a hiring strategy purely focused on external skills unsustainable in the long term. Upskilling the current workforce is critical. One non-negotiable step is developing AI awareness throughout all levels of the business (from front-line employees right up to executive leaders) so everyone can understand how AI works, the areas where AI can add the most value and the potential risks associated with its use.
Then, a focused set of learning pathways for technical and non-technical AI skills and competence for those responsible for designing, building and supporting these solutions at scale.
For this transformation to be successful, it must be undertaken with a big picture understanding that AI does not replace existing jobs. Rather, it transforms them. As such, many of today’s roles in human capital, operations, finance and product development will shift from being transactionally focused on executing tasks to providing more strategic and advisory services to support business objectives. AI is bringing change and it’s up to the people in these roles and leaders directing the business to leverage this change for our society’s benefit.
Automation can create space for human judgment, creativity and relationship-driven work, but only if organisations intentionally redesign roles to take advantage of it.
Enabling scale without creating bottlenecks
Governance has had a hard time historically – its checks, balances and compliance requirements are easily criticised as an obstacle to innovation, but, in reality, effective AI governance is the best way to grow with confidence and if “built in”, will provide a competitive advantage.
With the number of AI use cases expanding rapidly, organisations are looking for ways to break down their siloed thinking and implement standardised processes while also creating common guidelines regarding data, risk, cost and compliance.
Businesses have responded to this challenge by setting up a centralised, centres of enablement for AI. They can be helpful, but can also quickly become bottlenecks if not properly developed. A better alternative is a “hub and spoke” or a federated “centre of enablement” type of structure. Under this model, central teams define principles, frameworks and standards, while federated change agents within business units apply them locally.
This model avoids creating a single point of dependency and allows AI capability to be distributed across the organisation. It also ensures governance evolves alongside innovation rather than lagging behind it.
Crucially, governance should be positioned as a competitive advantage. The best decision-makers regarding how much to spend on AI will be those who identify their risks and regulatory liabilities early, so they can invest correctly and avoid costly mistakes down the line.
From fear to augmentation
The most underappreciated element of an organisation’s ability to transform through AI is its culture. Without a doubt, AI creates uncertainty, and uncertainty creates resistance. Fear of losing a job, becoming irrelevant or losing control is common for employees as AI enters or evolves in an organisation.
If these concerns are not addressed directly, adoption will stall, regardless of how advanced and promising the technology is.
Central to AI’s success is change management. This cannot be seen as a supporting activity or a nice-to-have. It’s imperative that organisations create psychological safety, clearly communicate why AI is being introduced and explain what it means for individuals and teams. AI should be framed not as a replacement for human work, but as an augmentation of human capability.
This shift requires leaders to actively model new behaviours. Managers increasingly need to lead hybrid teams made up of humans and AI agents, rethinking performance, accountability and collaboration. New roles may emerge focused on managing human-machine interaction and AI culture, an area that is still evolving and poorly understood.
Ultimately, organisations that succeed are those that align minds, hearts, and hands. People must understand the vision, believe in it and see how it translates into their daily work. Working with a trusted, experienced partner who can anticipate misalignments and help to make proactive corrections before an issue arises is therefore vital.
Moving forward with intent
Transformation through AI is not a destination but an ongoing process, greatly influenced by the speed of technological change. Organisations that continue to apply fixed multi-year change planning to achieve their AI goals will fail miserably and expensively.
Flexible operating models that evolve as quickly as AI technology are how AI adoption and evolution take place now, though the extent of this shift will depend on the nature and scale of the AI transformation in a particular entity.
Those leaders, businesses and governments that establish a clear vision for their AI programmes, invest strategically in their people, develop the necessary governance structures, and prioritise their culture and change impacts will enable their entities to go beyond experimentation and deliver sustainable value from AI. In doing so, they will not only adopt AI more quickly than others, but they will also create a more resilient, future-proof economy.
About iqbusiness
iqbusiness is Africa’s future-focused management and digital growth enabler, founded on over 26 years of experience. Led by some of the continent’s best thinkers and doers, our purpose is simple: to grow people, business, and Africa as one.
Established in South Africa in 1998, iqbusiness integrated into Reunert ICT in 2023 and merged with +OneX in 2024. Together, we offer a full-service suite of digital services, managed services, digital consulting and management consulting across the continent.
Our scale and unique capabilities mean we unlock exponential value and global growth for our clients. Renowned for our tenacious GESHIDO energy, and determination to deliver projects of excellence and meaning, we are a proud level 1 B-BEE contributor and hold B-Corp, Conscious Companies and Top Employer status. For more information, visit www.iqbusiness.net.
- The authors are Biase De Gregorio, partner and head of AI strategy and enablement and Maud Botten, partner and head of people, change and performance at iqbusiness
- Read more articles by iqbusiness on TechCentral
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