AI has initiated a period of hypergrowth and major transformation. It is evolving at an ever-increasing pace and offers great opportunities to improve efficiency and productivity, drive innovation and change the way we work.
This technology also brings risks to organisations, due to the speed of change, the growing complexity, cost of investment, data security and privacy threats, and scarcity of people with the skills to use and integrate technology.
Rapid digital transformation, especially AI and automation, but also M&A transaction and re-organisation, is forcing organisations to adopt technology at pace and make appropriate organisational changes to integrate that technology - or risk being left behind. New software is arriving at a bewildering speed: the likes of ChatGPT and other large language models are little more than 2 years’ old, yet barely a week goes by without yet another new launch. Only recently we've seen the emergence of Agentic AI – a probabilistic technology with high adaptability to changing environments and events.
As Laurent Gobbi, Global Tech Risk Leader, KPMG in France, explains, “The key trends I see in the technology space are automation, compliance and organisational changes. Automation because you need to replace manual tasks, compliance because you have more regulation coming and organisational changes because companies will have to adapt very quickly; you can't adopt these technologies effectively within a traditional business model.”
A complex, multi-layered technology stack presents a huge challenge, with many new, fragmented, cloud-based components coming onto the market, a constant stream of academic research fueling those technologies, and concerns over the safety, reliability and ethics of AI models and their use. Few if any organisations have the expertise to manage such complexity. Setting up your technology for success means coping with fragmentation and multiple suppliers and somehow bringing all these pieces together.
Misaligned technology and business goals. Technology investment is not only complex; it’s also very expensive and therefore a major commitment. If those responsible for making technology choices are not closely in tune with other parts of the organisation, then the platforms, solutions and AI may not support the business goals for innovation, productivity and insights.
Data security and privacy are a big concern, given the increasing volumes of intellectual property and personal data held by companies, and the ongoing battle with cyber criminals, hackers and bad actors. Ethical use of AI is also under scrutiny, to protect citizens’ rights. Without the right technology checks and balances, companies can be subject to regulatory penalties, which can damage their reputation and deter investors and consumers.
Lack of talent can hold back technology adoption and prevent companies from making the most of the enormous potential of AI and cloud solutions. The speed of innovation calls for a regular flow of new graduates knowledgeable in the very latest technologies; a narrow and finite pipeline that is not growing to satisfy demand.
The consumerisation of society has spilled over into the workplace, with internal clients demanding near-instant technology solutions. According to Laurent Gobbi, “Technology leaders should manage such expectations, by engaging with multiple stakeholders, including the lines of business, representatives from risk and control functions, and vendors. Together they can clarify detailed business needs, required data privacy and security controls, and compliance obligations. It’s vital to educate line of business to be very specific about their needs, to carefully align technology investment with strategic priorities.”
At the same time, given the complexity of the technology stack, more and more governance can be automated, to monitor performance and compliance, with continuous testing and assessment of technology components, activities and processes.
Technology teams should view themselves as drivers of innovation, keeping abreast of the latest developments. They also need to reinforce the data function, empowering the Chief Data Officer and treating data as a critical asset to be understood, managed and curated.
All of this calls for continuous upskilling to ensure that every employee is up to speed with the technology within and outside the organisation.
AI is fast becoming an integral part of every company and technology teams should achieve AI literacy, so they can help colleagues across the organisation to make AI work for them and start to approach their roles differently. Experiencing innovative AI solution is an important process, to discover the pros and cons of AI and really understand how it works. And finally, they should implement clear guardrails that drive responsible, ethical use of AI, making sure that models are based on reliable information and weeding out biases or incorrect conclusions and insights.
The first step towards trusted AI involves making an inventory to avoid ‘shadow AI’, which, when combined with robust governance, can better control how AI is used across the organisation.
With technological developments moving so quickly, companies can’t afford to fall behind competitors. At the same time, they can't risk making expensive investment mistakes; nor can they put their reputations on the line through unethical use of AI and/or cybersecurity.
Companies are advised to seek expert guidance and resources to navigate the complexities of technology risk and embrace the opportunities presented by AI.
