However, while everyone agrees the technology is a game-changer for business, the question being asked more and more in the boardroom is whether AI is truly ‘enterprise-grade’.

Business AI is not new

While hype about AI has reached unprecedented levels, the technology itself is not entirely new.

Nick Bostrom, in his book 鈥淪uperintelligence鈥�*, first published over ten years ago, provides an excellent summary of the progression of AI technology. This contemporary work captures the stages of AI – the inflated expectations; the plateauing; and the breakthroughs.

Enterprises have long relied on machine learning to power advanced analytics and predictive capabilities in a broad range of use cases – from manufacturing to finance operations to procurement and supply chain. These algorithms have equipped business leaders with information to drive greater operational efficiency.

AI has also been used extensively in traditional forms of algorithms, for example search engines, which defined an entire era of our technological development and transformed entire sectors, most notably the advertising industry.

Mounting issues with AI’s enterprise readiness

But what works on the web does not necessarily work in business. The Internet doesn’t care about authorisations. The C-suite does.

As concerns over privacy and data protection mount – especially in light of ongoing regulatory pressures – many companies have implemented restrictions over the use of open GenAI tools.

This is for good reason. Imagine an employee shares financial statements, vendor contracts or salary information with a GenAI tool that then reuses that information in answering prompts from other users.

A GenAI tool without an authorisation element is simply not enterprise-grade, and probably doomed to be confined to a single use case or department, limiting its ability to deliver value to the broader business.

Security issues also arise with the concept of data lakes, which combine enterprise and external data sources for AI purposes. Data lakes can be treacherous to enterprises, especially when data must be exported outside of large enterprise applications.

Here, a federated approach that leaves enterprise data at their origin and does not copy or transfer data is needed. Critically, organisations must maintain the semantic layer of the data, which can be the Achilles heel of every data lake project and, consequently, the GenAI models trained on that data.

Beware of hallucinations

However, the greatest danger in AI that is not enterprise-grade lies in its tendency to hallucinate.

Gen AI is an excellent algorithm that fundamentally learns by looking at what is available within its domain, usually the internet. Let鈥檚 be honest 鈥� you can鈥檛 trust every piece of information found on the internet anymore.

In an enterprise environment, CEOs are seeking the 鈥榮ingle version of truth鈥�. That means fact checking is important but begs the question: 鈥渨hich is the dataset on which I should train my Gen AI?鈥�. The simple truth is that business leaders cannot build products or develop innovations using models that make things up or use insights based off false or inaccurate information.

Here, application suite vendors have the upper hand. The business applications that power the world’s enterprises have a wealth of business data that can be mined by AI algorithms to produce accurate, relevant, and reliable insights. Vendors in this space also have significant expertise in business processes and contextualised data – the perfect sources for training effective GenAI.

There’s no doubt that enterprises will benefit from the power of AI in the coming years. Whether it is enterprise-ready comes down to individual systems and tools. While some already have enterprise-level capabilities, others may not yet meet all the requirements for reliability and security. 听Business leaders must take care to build out AI use cases that can deliver value to the business, rely on robust datasets, and live up to expectations. These guardrails will ensure business AI solutions that are relevant, reliable, and responsible.

*Superintelligence – Paths, Dangers, Strategies was written by author, Nick Bostrom, and first published in 2013 by Oxford University 麻豆原创

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The confluence of several disruptive factors 鈥� geopolitical conflict, rising inflation, economic uncertainty, increased regulatory pressure, and last, but by no means least, the impact of new technologies 鈥� will undoubtedly test leaders to the limit in the year to come.

Speaking with business leaders across the region, several common themes have emerged. From the urgent need to build greater resilience and reduce risk, to leveraging the power of AI and improving sustainability efforts while ensuring that investments drive value both now, and in the future – these are the interconnected trends that European business leaders will confront in 2024:

Trend 1: De-risking the enterprise

In an environment defined by volatility and geopolitical uncertainty, business leaders face increased risk across their operations. This is driving an acute need for operational and technological interventions to reduce risk and bring stability to the enterprise, while still safeguarding agility.

Europe’s regulatory landscape is becoming increasingly complex as policymakers try to keep pace with the disruptive impact of technology. The , for example, will establish strict rules and standards around the development and application of AI in business contexts. This includes guardrails for general purpose AI; a total ban on AI as it relates to citizens’ rights and democratic processes; and the right for consumers to launch complaints and demand meaningful explanations regarding decisions based on AI systems.

In addition, a wave of new regulations in trade and customs throughout the region will add compliance pressure on companies already reeling from ongoing challenges related to various elements of their supply chains. From 1 January this year, companies wishing to do business in Europe are subject to the EU Emissions Trading System that aims to establish Europe as the first climate-neutral continent; a truly admirable objective.

All this complexity requires extensive investment in sophisticated digital tools to provide greater visibility over the climate impact of the end-to-end supply chain, which brings me to my next point:

Trend 2: Supply chain resilience is not the same as agility

As if the continued ripple effects of the pandemic on global supply chains didn’t pose enough of a challenge over the last couple of years, business leaders have also had to contend with the ongoing geopolitical conflict. Be it re-routing of ships to avoid the Suez Canal, high-tech component shortages, or commodity price volatility on everything from food to energy – these factors, among others, create immense supply chain instability.

In response, forward-looking companies are seeking greater agility to respond to supply chain threats. A recent highlights the importance of technology in maximizing organizations’ chances at success with maintaining stable supply chains.

One of the key objectives of digital transformation within supply chains is the ability to improve end-to-end visibility. However, found that 43% of global organizations have limited to no visibility over the performance of their tier one suppliers 鈥� an astounding statistic.

Greater visibility over supply chain processes clearly also supports wider sustainability efforts. The same KPMG study found that only 5% of supply chain emissions stem from direct manufacturing; emissions from the broader supply chain are five to ten times greater.

Digital platforms can significantly improve enterprises’ ability to collect emission data and set appropriate targets for key suppliers to collectively drive improved sustainability outcomes throughout the supply chain.

In addition, organizations will increasingly leverage the power of AI to improve supply chain management, logistics, and procurement. In fact, half of supply chain organizations are expected to invest in applications that in the year ahead.

Trend 3: Unlocking AI’s true business value

On the topic of AI, the year ahead will undoubtedly see more companies leverage Generative AI and AI for business to drive innovation, efficiency, and productivity.

Unsurprisingly, that Trust, Risk and Security Management in AI Models will be one of the leading tech themes for the year ahead, built on advances in model monitoring, AI application security, and privacy.

However, European businesses may be more hesitant to unleash AI on their operations. found that business leaders in EMEA are far less convinced that their customers prefer to interact with AI models than their North American peers.

And considering the EU legislation already mentioned, European companies looking to incorporate AI in their business models or operating environments will need to build their use case with both compliance and privacy front and centre.

However, companies can unquestionably accelerate the value from their AI deployments by leveraging AI that is purpose built for business. Large cloud and software providers, like 麻豆原创, have invested significantly in building responsible AI into their core products. This means that customers can immediately benefit and unlock business value from their software investments.

2024 will be a pivotal year for many business leaders across EMEA 鈥� while daunting in many respects, also an incredibly exciting time to lead.

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