Artificial intelligence keeps creating opportunities for a wide variety of businesses and purposes, but a coin always has two sides. Strategic investments in this emerging technology could deliver significant competitive advantages. But the wrong bets could open the door to data privacy concerns, legal liabilities, and ethical challenges. As the pressure to find fast ways to boost business keeps increasing, it is no wonder that decision-makers are actively seeking guidance in navigating these complexities.
“As undeniable as the opportunities are, business leaders still wrestle with various challenges in scaling AI across their organizations with full confidence,” Riku Ahlroth, Software Leader at IBM Finland.
“While AI’s capabilities are widely recognized, there are still several ethical issues to deal with.”
Ahlroth refers to issues such as explainability – whether human users understand a set of of processes and methods allows human users to comprehend and trust the results and outputs created by machine-learning algorithms – and bias – whether an AI system produces unfairly skewed content or shows partiality.
A CEO Study carried out by IBM on decision-making in the age of AI shows that generative AI still lacks trust among decision-makers. Safety and ethical aspects keep raising concerns.
“Putting the right governance structures in place can help,” says Ahlroth. “When AI activities are monitored, directed, and managed responsibly, reliability will rise.
The good, the bad, and the governance
Data must be governed to track lineage, understand quality, and control access to sensitive fields. Model bias, explainability, and robustness must be constantly measured, and consistency, transparency, and compliance must be ensured throughout the process.
“Trustworthy AI is about trusting the data, the models, and the process,” Ahlroth sums up. “That way, everyone can consume data with confidence, control the risks smartly, and create reliable AI lifecycles from finding and preparing data through building sufficient models all the way to deploying and monitoring performance.”
As self-evident as the role of governance sounds, common acceptance is still to be established.
For the AI enthusiasts that have caught the first wave and riding it at full speed, the dilemma is obvious. With all this governance in place, the cure must work – but doesn’t the patient die at the same time, as the indefinite, albeit uncontrolled, opportunities of AI are so heavily restricted?
Even when it comes to AI, the lunch is never free. To get access to the opportunities, certain obligations must be properly addressed.
The good news is, even that does not have to be complicated. Deep down, it is about restructuring the operating model, streamlining deployment, and ensuring that the business benefits are realized. While an open-source platform provides a solid foundation, an expert who can manage it – and the process – in a controlled way, also when it comes to commercialization, is required to ensure smooth progress.
“At the end of the day, adequate governance leads to better efficiency, improved control, deeper understanding and, eventually, better results,” Ahlroth emphasizes. “When both obligations and opportunities are understood, new business can be created.”
Compared to the potential benefits, the cost is downright minimal. In the long run, governance works to everyone’s advantage.