A silent, general nervousness is creeping into boardrooms and top management. It is not a question of whether or not to invest in artificial intelligence but why, despite the huge investments, the transformation promised seems to evade one. The bitter reality many are having to face is that better technology is not a competitive moat anymore. The most advanced algorithm, which was implemented without a consistent human integration approach and process redesign is simply a costly computerised artefact. The most challenging aspect of this stage of AI implementation, is not technological but organisational.
The AI magic black box hype has long since passed. Its potential in theory, hyper-personalisation, predictive maintenance, accelerated R&D, is realised by the leaders nowadays. Nevertheless, the distance between pilot projects and value-creating operations on the scaled level is stubbornly large. More compute power or more massive datasets do not fill this chasm. It is bridged by the fact that a fundamental change in stance is needed, one which is no longer of looking at AI as a discrete project, but rather of considering AI as a dynamic and learning system within an organisation.
The initial lesson to learn is to blow up the fantasy of the smooth integration that is pushed by the vendor. Ready-made AI systems have the potential to be deployed quickly, but they tend to fail as soon as they come into contact with the reality of your business processes, data silos, and cultural inertia that are unique and messy, as well. A churn prediction model would be of no use when the customer success team does not know what protocol to follow in taking action on its alerts. The failure of an inventory optimisation engine occurs when the procurement department is based on old supplier contracts, with relationship basis. It is an isolated technology that is rejected by the business system. The code is not the problem, therefore, but the assumption that technology does not depend on the human and procedural ecosystem in which it is supposed to be improved.
The second lesson involves the strategic shift of pure automation to intelligent augmentation. The first motivation of AI was to replace repetitive tasks. It is a valid initial point, yet the exponential value becomes unlocked by the AI enhancing the intricate human decision-making. Take an example of a financial analyst. Data aggregation, when automated, saves hours, however, when enhanced by an AI that simulates the outcome of scenarios on-the-fly using real-time geopolitical and market indicators, it changes the role. Its objective would no longer be to cut down on the number of heads, but the objective now would be to increase human knowledge. This requires a careful breakdown and rebuilding of workflow. The question you need to ask yourself is: where can the AI scale pattern recognition to offload the human intelligence to the finer details, morality, creativity and exception handling? This is a leadership requirement rather than an IT activity.
Thirdly, we will need to address the data myth. Most organisations have created massive data pools, but are data-poor in regard to the contexts that count. The emerging imperative is data fluency, or the capacity of an organisation to not just store information, but to curate, interpret and activate information in a responsible manner. AI model is as good as the data it is trained on. Partisan information gives partisan results. Isolated information results in piecemeal intelligence. The strategic emphasis should consequently shift as much as it can to infrastructure and governance, to collection and curation. This implies defining a clear data ownership, quality metrics, and standards of ethics. It involves the creation of data literacy among functions in such a way that a marketing manager is able to sensibly request a model and be aware of its constraints. In the absence of this basic fluency, AI projects are erected on sand.
So, what should an organisation do to be out of the fallacy and into the fluency? The process of the change in investment portfolio is the commencement of an application. Use it not only to invest in technology licensing but also in three areas that are vital: Process Archaeology, Change Integration and Hybrid Talent.
Begin with Process Archaeology. Write every detail of the target process in excruciating detail before you write a single line of code. Determine the decision points, data inputs, human handoffs and desired outcomes. This map demonstrates the points at which AI can introduce value, but more importantly, the processes that already exist have to be modified to accommodate it.
Second, change integration should be formalised. Any AI implementation should have a change management track that is equal to the technical one. This team can conduct training, communication, workflow redesign and adoption friction measurement. The measure of their success is not the model accuracy, but the user interaction and the increase in the business performance.
Lastly, develop Hybrid Talent. The most useful teams are ones that have data scientists and domain experts the supply chain veteran and the machine learning engineer. This amalgamation makes solutions sound and operationally viable. Invest in upskilling initiatives that produce so-called translators that can fill the gap between business issues and technological provisions.
In the future, competitive advantage will not be based on proprietary models, which will be commoditised to a large extent, but on the inimitable combination of your information, your redesigned processes, and your augmented workforce. The emergence of the AI-optimised organisation will manifest itself, in which AI will be integrated into the working fabric seamlessly, continuously learning and evolving.
At the same time, the aspect of ethics and governance will cease being a compliance checkbox but rather a fundamental strategic differentiator. Risk management and brand trust will not be interested in any explainable AI, auditable decision trail, or solid bias mitigation. The organisations who inculcate transparency and ethical supervision into their AI systems at the foundational level will find the environment of regulation easier to manoeuvre and gain higher levels of trust in the stakeholders.
It is a marathon of change in organisations and not a sprint of technology acquisition. It requires that leaders see past the glitter of the algorithm to the more challenging, more human-focused task of redesigning their operating model. When AI fails, it is not often a failure of the machine intelligence, but a failure of the boardroom strategy.
Second AI project is not based on an RFP regarding technology, but on a whiteboard session about one critical business process. Mapping It, testing it, and inquiring where human and machine intelligence could work together to produce a product neither could do alone would be of interest. That would be the step out of the fallacy of investment and into the actual change.
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