Headlines about the power of generative artificial intelligence (gen AI) are everywhere. But business leaders are asking, is it really that game-changing, or just a fad? And if it is a game-changer, how can you make the most of it?
In the first episode of our second season of Kaspersky’s podcast Insight Story, I speak with experts Shagun Sachdeva (India,) Project Manager for Disruptive Tech at business intelligence service GlobalData Plc and Karen Quinn (UK,) Senior Director, Brand and Corporate Communications at financial software providers, Finastra.
What is generative AI?
TechTarget defines gen AI as any “artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data.” Some applications rely on Natural Language Processing so users can ask for outputs using everyday speech.
Well-known examples of gen AI include OpenAI‘s chatbot ChatGPT and Google Bard, but current and potential business uses are near endless.
Shagun thinks many businesses already understand its importance. “A recent GlobalData Plc survey found more than 50 percent of businesses expect gen AI will tangibly disrupt their industry in the next five years. 30 percent are already using gen AI tools.”
How are businesses using gen AI?
“Business leaders are having more sophisticated conversations about how gen AI can go from an experiment to giving strong returns on investment,” says Shagun. “Whether it’s chatbots engaging with customers, sharing financial tips and customizing financial plans based on individual spending habits. It’s also generating loan options based on a user’s credit profile.”
Finastra plans to develop gen AI tools for customers in future, but they’ve started in-house with GENAI (X,) a gen AI-based system now rolled out to their 8,000 employees.
We look at gen AI from a human empowerment angle – giving people back time. These tools give space to reflect, imagine and create rather than just do, do, do.
Karen Quinn, Senior Director, Brand and Corporate Communications, Finastra
“We use it for things like understanding and reviewing contracts, predicting behaviors and optimizing workflows,” says Karen.
She believes that although only being used internally so far, their gen AI tools are benefiting customers. “We can only work with the data we have to make financial decisions. Anything that can generate new scenarios, pull in more data sources or enable federated learning can only benefit.”
Is gen AI a threat to jobs?
Shagun acknowledges employees worry AI might replace them. “Some of that anxiety may be justified – a Goldman Sachs report in March 2023 said AI could replace 300 million full-time jobs.” But getting to know gen AI better may help overcome that concern. “Lean in to the technology. Education and training is key. Start with courses like prompt engineering.”
Karen refers to a quote from economist and professor Richard Baldwin, “AI won’t take your job. It’s somebody using AI that will take your job.” In other words, it’s not the technology itself that’s a threat to jobs but failing to explore it and take full advantage of it.
She advises looking at the benefits of gen AI broadly. “This is not an efficiency play. People will become more productive, but hopefully that means work is more rewarding.”
Getting gen AI right
While generative AI offers much possibility, it also raises concerns around ethics, bias and data privacy. It is largely unregulated today and developing AI models can be resource-intensive.
Karen highlights Finastra’s caution. “We rigorously tested the products, then launched a full-scale training program and disabled other tools. They’re still going through testing, like panels to try and break them and make sure no sensitive data gets shared with the wrong audiences.”
She continues, “In brand and communications, there are copyright issues. If you’re using text-to-image prompts, where is it drawing data from? We need to be very careful.”
Materials used to train AI are often copyrighted, and whether people or businesses can copyright outputs of gen AI is subject to legal challenges in many countries, with some courts – including in the US and Italy – ruling no, at least in the case of visual arts.
Balancing gen AI optimism with realism
She also recommends businesses pay attention to copyright. “Disputes have arisen between artists and AI companies over the value of human creativity.”
Data privacy and security also rate highly for Shagun. “Large language models are trained on vast swathes of internet data. There’s no data protection embedded in these systems by design or default. Training data can fail to include women, older people or marginalized groups – that’s an ethical challenge.”
Security and generative AI
Amin Hasbini is Kaspersky’s Head of Research Center for its Global Research and Analysis Team (GReAT,) Middle East, Turkey and Africa. He says many employees are already using freely available AI tools, potentially without their employers knowing. “In a 2023 Kaspersky study, we found 57 percent of workers are using generative AI to save time. This raises many security questions. What kind of data are they putting into it? Is it intellectual property, code or documents to summarize? That’s a major concern.”
Cybercriminals are also using generative AI to help them fool people with more realistic fake websites. In 2022, Kaspersky’s anti-phishing system blocked more than 500 million attempts to access fraudulent websites – a doubling of attempts in the previous year.
It’s likely cybercriminals are already using generative AI chatbot ChatGPT’s ability to produce convincing texts to create automated spearphishing attacks – phishing targeted at specific people.
To protect against generative AI-based threats, Amin advises, “It starts with awareness. Employees need to know their organization’s boundaries around putting data into AI websites. Businesses need enterprise cybersecurity solutions that allow monitoring and control of devices, systems and data used within the organization.”
Gen AI’s future potential
What kinds of applications for gen AI will we see businesses pursuing in the near future?
Shagun believes what businesses do today will decide the future of gen AI. “The world will look completely different by 2030. Global Data Plc estimates the AI market will be worth around 900 billion by 2030.
Gen AI will transform all aspects of our lives. This is a make-or-break time for industry leaders.
Shagun Sachdeva, Project Manager for Disruptive Tech, GlobalData Plc
But that doesn’t mean carelessly diving in. “Gen AI is no magic bullet. While the enthusiasm around it is justified, prudence is imperative. We need responsible innovation,” says Shagun.
Karen imagines gen AI helping to make markets fairer. “Gen AI has incredible potential to overcome inefficiencies, like small-to-medium business (SMB) access to trade finance. Gen AI might be able to generate or automate some of the processes for onboarding smaller companies into global trade, opening them up to a wider audience.”
She also thinks gen AI could help solve some of the problems it’s been known to introduce. “It can help identify bias through explainability and ensure we close feedback loops that cause biases. It could also look at efficiencies in supply chains, contracts and communications and marketing. It can take out some of the drudgery and enrich the processes.”
“We’re on the cusp of understanding what gen AI can do,” Karen says. “Who knows what the future brings? This could be game-changing in ways we can’t even imagine.”
Like Karen and Shagun, as a creative marketer, I’m drawn between mostly optimism and a little fear about what gen AI will bring. As I wrote following a gen AI talk for marketers where I heard about Karen’s exceptional AI program at Finastra, I’m optimistic we can do it right.
Having the training, technologies and policies to bring the whole organization on the journey, we can use this tech to free us by shifting mundane tasks down to these somewhat smart bots. But in that shift, we may narrow opportunities – particularly for talent entering our industries – to refine their craft and learn the difference between average and distinctive output. Refined talent that generative AI lacks. For now, anyway.