Beyond the hype: AI and enterprise data

The prevalence of artificial intelligence (AI) in our everyday lives has drastically increased since Generative AI (GenAI) and third-party large language model (LLM) services like ChatGPT exploded onto the scene, converting sceptics to evangelists and transforming the way we interact with technology.

Sifting through the hype, nonetheless, it’s true to say that AI has been around for decades. Manufacturers, for example, have been using robotics since the 1970s, and a decade later, big companies started leveraging AI tools such as machine intelligence to deliver greater efficiencies.

However, with GenAI, we may find something truly worthy of the hype — with its potential to revolutionise businesses and force leaders to rethink the game — we need to remember we’re not starting off cold. LLMs are a subset of AI after all, and we can build on what we already know to realise the potential of what’s already here and what’s to come.

Trusted data — the key to balancing AI’s value and risk

As solutions and use cases for AI and LLMs evolve, so will the global market. According to Grand View Research, the global AI market was valued at $136.55 billion in 2022. With a compound annual growth rate (CAGR) of 37.3%, the market is expected to reach $1811.8 billion by 2030.

These growth rates are driven by AI’s applications across a broad range of industries, from education to automated vehicles and healthcare modernisation. Use cases for the technology abound, but they all have one thing in common — data.

While the figures in and of themselves are astronomical and reveal a growing focus on advanced algorithms, the reality is that GenAI and LLMs are only as good as the data they’ve been trained on, and they need the right context. For these models and AI to be successful, the data needs to be trusted.

When using trusted, democratised data, AI provides the means to improve productivity, increase revenue, reduce operational expenditure (OPEX) and enhance customer experiences. This is a pivotal time for business leaders to step outside of the hype bubble to look anew at an organisation’s data strategy and how it can be optimised for AI.

The elephant in the room by the name of data

From big data to cloud data and now hybrid data, accessing, understanding and managing data has become more complex than ever, and this may become a major barrier to organisations looking to leverage AI.

Smart businesses will be looking for solutions that establish a governed architecture catering directly to real-world hybrid deployments, ensuring teams have the freedom to move applications, data and users bi-directionally between the data centre and multiple clouds, regardless of where the data lives.

This, in turn, will provide the right organisational context to the data and greatly empower enterprise AI solutions, including GenAI and LLMs.

Embedding AI into your business for better outcomes

According to McKinsey, AI adoption has more than doubled in the last five years. The number of leaders adopting AI in at least one business area has increased from 20% in 2017 to 50% at the end of 2022, peaking in 2019 at 58%. Business areas that are benefiting include marketing and sales, supply chain management and risk modelling, to name a few.

To further showcase the prolific ease of use and potential of AI, Gartner found that ChatGPT has prompted 45% of executives to increase AI investment, while 70% of organisations are currently in exploration mode with GenAI.

However, across the board, leaders are not ignorant of the inherent risks that come with these SaaS AI models, including data sprawl, privacy and security issues, and contextual limitations. This is where data management shines. The right solution takes into account data sprawl across on-premise, public and private cloud environments; helps to mitigate data privacy risks by operating under clear guidelines of data usage; provides strong management controls for the entire data lifecycle; and leverages the cloud while maintaining full control over data assets.

Time to take a step into the real world

While there’s been much hubbub about how AI might be misused, we must not overlook how AI can be a powerful tool in solving some of the world’s pressing issues in health care, climate change, wildlife conservation and combating poverty and world hunger.

In fact, many of our customers are using AI for good, exemplifying the need to shift gears from thinking AI will replace humans to considering how we can maximise trust in our data and leverage the technology for huge societal benefits.

For instance, an insurance company used a hybrid data platform to take external images from space to understand the impact of the Californian wildfires. AI models have been trained to recognise the difference between an unaffected home, a partially devastated home and a fully devastated home.

The company was then able to complete an assessment and write up ACH payments the next day to claimants, with no timely site visits or manual labour required. Due to the severity of the situation, this transformed people’s lives overnight.

Another customer in health care is using AI and supervised machine learning powered by ‘deep learning’ algorithms to exponentially screen and spot diseases such as retinopathy with extremely high accuracy.

Only when the hype bubble bursts can we see the latest craze of AI for what it really is — the next step in a century-old journey and a huge potential to leverage our data like never before. It’s time to recognise the real-world benefits of powerful AI technologies fuelled by trusted and accurate data.

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