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Generative AI now the most frequently deployed AI solution in organisations

 960 640 Stuart O'Brien
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According to a Gartner survey conducted in the fourth quarter of 2023, 29% of the 644 respondents from organisations in the U.S., Germany and the U.K. said that they have deployed and are using GenAI, making GenAI the most frequently deployed AI solution. GenAI was found to be more common than other solutions like graph techniques, optimisation algorithms, rule-based systems, natural language processing and other types of machine learning.

The survey also found that utilizing GenAI embedded in existing applications (such as Microsoft’s Copilot for 365 or Adobe Firefly) is the top way to fulfill GenAI use cases, with 34% of respondents saying this is their primary method of using GenAI. This was found to be more common than other options such as customizing GenAI models with prompt engineering (25%), training or fine-tuning bespoke GenAI models (21%), or using standalone GenAI tools, like ChatGPT or Gemini (19%).

“GenAI is acting as a catalyst for the expansion of AI in the enterprise,” said Leinar Ramos, Sr Director Analyst at Gartner. “This creates a window of opportunity for AI leaders, but also a test on whether they will be able to capitalize on this moment and deliver value at scale.”

The primary obstacle to AI adoption, as reported by 49% of survey participants, is the difficulty in estimating and demonstrating the value of AI projects. This issue surpasses other barriers such as talent shortages, technical difficulties, data-related problems, lack of business alignment and trust in AI (see Figure 1).

“Business value continues to be a challenge for organizations when it comes to AI,” said Ramos. “As organizations scale AI, they need to consider the total cost of ownership of their projects, as well as the wide spectrum of benefits beyond productivity improvement.”

Figure 1: Top Barriers to Implement AI Techniques (Sum of Top 3 Ranks)
[Image Alt Text for SEO]

Source: Gartner (May 2024)

“GenAI has increased the degree of AI adoption throughout the business and made topics like AI upskilling and AI governance much more important,” said Ramos. “GenAI is forcing organizations to mature their AI capabilities.”

“Organizations who are struggling to derive business value from AI can learn from mature AI organizations,” said Ramos. “These are organizations that are applying AI more widely across different business units and processes, deploying many more use cases that stay longer in production.”

The survey found 9% of organizations are currently AI-mature and found that what makes these organizations different is that they focus on four foundational capabilities:

  • A scalable AI operating model, balancing centralized and distributed capabilities.
  • A focus on AI engineering, designing a systematic way of building and deploying AI projects into production.
  • An investment on upskilling and change management across the wider organization.
  • A focus on trust, risk and security management (TRiSM) capabilities to mitigate the risks that come from AI implementations and drive better business outcomes.

“AI-mature organizations invest in foundational capabilities that will remain relevant regardless of what happens tomorrow in the world of AI, and that allows them to scale their AI deployments efficiently and safely,” said Ramos.

Focusing on these foundational capabilities can help organizations mature and alleviate the current challenge of bringing AI projects to production. The survey found that, on average, only 48% of AI projects make it into production, and it takes 8 months to go from AI prototype to production.

Photo by Solen Feyissa on Unsplash

IT experts poll: Elon Musk is ‘wrong’ that no jobs will be needed in the future

 960 640 Stuart O'Brien
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Elon Musk’s claim that AI will make all human jobs irrelevant should not be taken seriously, according to a survey of tech experts conducted by BCS, The Chartered Institute for IT.

During an interview with UK Prime Minister Rishi Sunak for the AI Safety Summit last year, Musk said: ‘There will come a point where no job is needed — you can have a job if you wanted to have a job … personal satisfaction, but the AI will be able to do everything.’

But in a poll by BCS, The Chartered Institute for IT, 72% of tech professionals disagreed with Musk’s view that AI will render work unnecessary. Some 14% agreed (but only 5% ‘strongly’ agreed), with the rest unsure.

In comments, many IT experts said Musk’s statement was ‘hyperbole’ and suggested it was made to create headlines.

Those currently working in computing agreed that AI could replace a range of jobs, but would also create new roles, including oversight of AI decision making – known as ‘human in the loop’.

They also said that a number of jobs, for example hairdressing, were unlikely to be replaced by AI in the near future, despite advances in robotics.

BCS’ AI and Digital in Business Life survey also found AI would have the most immediate impact this year on customer services (for example chatbots replacing human advisers).

This was followed by information technology, then health and social care, then publishing and broadcasting, then education.

Leaders ranked their top business priorities as cyber security (69%), AI (58%) and business process automation (45%).

Only 8% of participants told BCS their organisation has enough resources to achieve their priorities.

Cyber attacks were most likely to keep IT managers awake at night in 2024 – this result has been consistent over the last 11 years of the survey.

Rashik Parmar MBE, Chief Executive of BCS, The Chartered Institute for IT said: “AI won’t make work meaningless – it will redefine what we see as meaningful work.

“Tech professionals are far more concerned about how ‘ordinary’ AI is affecting people’s lives today, for example, assessing us for credit and invitations to job interviews, or being used by bad actors to generate fake news and influence elections. The priority right now is to ensure AI works with us, rather than waiting for a Utopia.

“To build trust in this transformational technology, everyone working in a responsible AI role should be a registered professional meeting the highest standards of ethical conduct.”

The BCS poll was carried out with over 800 IT professionals, ranging from IT Directors and Chief Information Officers, to software developers, academics and engineers.

Photo by Arif Riyanto on Unsplash

Where does GenAI fit into the data analytics landscape?

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Recently, there has been a lot of interest and hype around Generative Artificial Intelligence (GenAI), such as ChatGPT and Bard. While these applications are more geared towards the consumer, there is a clear uptick in businesses wondering where this technology can fit into their corporate strategy. James Gornall, Cloud Architect Lead, CTS explains the vital difference between headline grabbing consumer tools and proven, enterprise level GenAI…

Understanding AI

Given the recent hype, you’d be forgiven for thinking that AI is a new capability, but in actual fact, businesses have been using some form for AI for years – even if they don’t quite realise it.

One of the many applications of AI in business today is in predictive analytics. By analysing datasets to identify patterns and predict future outcomes, businesses can more accurately forecast sales, manage inventory, detect fraud and resource requirements.

Using data visualisation tools to make complex data simpler to understand and more accessible, decision-makers can easily spot trends, correlations and outliers, leading them to make better-informed data-driven decisions, faster.

Another application of AI commonly seen is to enhance customer service through the use of AI-powered chatbots and virtual assistants that meet the digital expectations of customers, by providing instant support when needed.

So what’s new?

What is changing with the commercialisation of GenAI is the ability to create entire new datasets based on what has been learnt previously. GenAI can use the millions of images and information it has searched to write documents and create imagery at a scale never seen before. This is hugely exciting for organisations’ creative teams, providing unprecedented opportunities to create new content for ideation, testing, and learning at scale. With this, businesses can rapidly generate unique, varied content to support marketing and brand.

The technology can use data on customer behaviour to deliver quality personalised shopping experiences. For example, retailers can provide unique catalogues of products tailored to an individuals’ preferences, to create a totally immersive, personalised experience. In addition to enhancing customer predictions, GenAI can provide personalised recommendations based on past shopping choices and provide human-like interactions to enhance customer satisfaction.

Furthermore, GenAI supports employees by automating a variety of tasks, including customer service, recommendation, data analysis, and inventory management. In turn, this frees up employees to focus on more strategic tasks.

Controlling AI

The latest generation of consumer GenAI tools have transformed AI awareness at every level of business and society. In the process, they have also done a pretty good job of demonstrating the problems that quickly arise when these tools are misused. From users who may not realise the risks associated with inputting confidential code into ChatGPT, completely unaware that they are actually leaking valuable Intellectual Property (IP) that could be included in the chatbot’s future responses to other people around the world, to lawyers fined for using fictitious ChatGPT generated research in a legal case.

While this latest iteration of consumer GenAI tools is bringing awareness to the capabilities of this technology, there is a lack of education around the way it is best used. Companies need to consider the way employees may be using GenAI that could potentially jeopardise corporate data resources and reputation.

With GenAI set to accelerate business transformation, AI and analytics are rightly dominating corporate debate, but as companies adopt GenAI to work alongside employees, it is imperative that they assess the risks and rewards of cloud-based AI technologies as quickly as possible.

Trusted Data Resources

One of the concerns for businesses to consider is the quality and accuracy of the data provided by GenAI tools. This is why it is so important to distinguish between the headline grabbing consumer tools and enterprise grade alternatives that have been in place for several years.

Business specific language is key, especially in jargon heavy markets, so it is essential that the GenAI tool being used is trained on industry specific language models.

Security is also vital. Commercial tools allow a business to set up its own local AI environment where information is stored inside the virtual safety perimeter. This environment can be tailored with a business’ documentation, knowledge bases and inventories, so the AI can deliver value specific to that organisation.

While these tools are hugely intuitive, it is also important that people understand how to use them effectively.

Providing structured prompts and being specific in the way questions are asked is one thing, but users need to remember to think critically rather than simply accept the results at face value. A sceptical viewpoint is a prerequisite – at least initially. The quality of GenAI results will improve over time as the technology evolves and people learn how to feed valid data in, so they get valid data out. However, for the time being people need to take the results with a pinch of salt.

It is also essential to consider the ethical uses of AI.

Avoiding bias is a core component of any Environmental, Social and Governance (ESG) policy. Unfortunately, there is an inherent bias that exists in AI algorithms so companies need to be careful, especially when using consumer level GenAI tools.

For example, finance companies need to avoid algorithms running biassed outcomes against customers wanting to access certain products, or even receiving different interest rates based on discriminatory data.

Similarly, medical organisations need to ensure ubiquitous care across all demographics, especially when different ethnic groups experience varying risk factors for some diseases.

Conclusion

AI is delivering a new level of data democratisation, allowing individuals across businesses to easily access complex analytics that has, until now, been the preserve of data scientists. The increase in awareness and interest has also accelerated investment, transforming the natural language capabilities of chatbots, for example. The barrier to entry has been reduced, allowing companies to innovate and create business specific use cases.

But good business and data principles must still apply. While it is fantastic that companies are now actively exploring the transformative opportunities on offer, they need to take a step back and understand what GenAI means to their business. Before rushing to meet shareholder expectations for AI investment to achieve competitive advantage, businesses must first ask themselves, how can we make the most of GenAI in the most secure and impactful way?