The bizarre interview between Elon Musk and Rishi Sunak at the UK government’s AI Safety Summit earlier this month did little to champion the idea that humans are the solution to a potential artificial intelligence (AI) problem.
It did, however, suggest that AI will put an end to the need to work – not that either Musk, the richest person in the world according to the Bloomberg Billionaires Index (on 10 November 2023), or Sunak, reputedly the UK’s wealthiest ever prime minister, have anything to worry about.
It was also suggested that AI has the potential to be the “most disruptive force in history”. For most people and organisations, this is not news. Since the unveiling of ChatGPT 12 months ago, GenAI has emerged as a game-changer. Coupled with the increased consumerisation of enterprise applications, 2023 has to go down as a seminal year.
It’s certainly a time of rapid change in software development and application features. GenAI is now the recognisable face of AI, inspiring automation and changing the way businesses think about their relationship with software, data and business processes.
A good example of this is at Vodafone, which recently teamed up with IT consultancy Embracent to solve a slow onboarding problem for new joiners. The result is a GenAI-driven application called Sherpa, which now manages the process, including identity verification, generating and sending offer letters, and coordinating calendars and contacts of new colleagues. What is key here is that this wasn’t a dumbing down to fit the technology – it was a re-engineered experience, with GenAI doing all the grunt work.
“A popular belief about AI is that if you can dream it, you can do it,” says Mark Lockton, CEO of Embracent. “While some realities might be a while off, today there are very practical ways you can improve or even re-invent underlying business models and processes to deliver significantly enhanced experiences, both for customers and employees.”
The research backs this up, with Gartner recently claiming that by next year, 40% of enterprise applications will have conversational AI embedded, up from less than 5% in 2020, and that by 2026, generative design AI will automate 60% of the design effort for new websites and mobile apps.
Finding applications for GenAI is still in its infancy, but momentum is certainly building towards the tech being embedded across a whole range of enterprise functions.
For Ramaraju Indukuri, advisory consultant and AI practice leader at tech consulting firm NashTech, while the likes of Amazon have got us all “accustomed to customised content”, there is still a bit of a lag in enterprise apps. Indukuri suggests a lot of this is down to data challenges, but says things are starting to change.
Mary Mesaglio, Gartner
“We’re seeing a growing number of organisations upping their investment in using a combination of traditional machine learning and generative AI to provide a meaningful interpretation of metrics,” says Indukuri. “For instance, we’re working with a social media marketing firm to interpret complex campaign metrics and summarise them for a small business owner.”
At the recent Gartner IT Symposium/Xpo in Barcelona, the analyst firm went further, saying that AI ambition and AI-ready scenarios must be a top priority for CIOs over the next 12 to 24 months. That requires a shift in thinking.
As Mary Mesaglio, distinguished vice-president analyst at Gartner, says: “GenAI is not just a technology or just a business trend. It is a profound shift in how humans and machines interact. We are moving from what machines can do for us to what machines can be for us.”
Or in the words of Gabriela Vogel, senior director analyst at Gartner: “Machines are evolving from being our tools to becoming our teammates.”
Accelerating use cases
The wave of enterprise software brands announcing GenAI capabilities in recent months is a clear indication of this, promising users improved data insights to feed creativity, personalisation or business decision-making, for example.
The likes of Amazon Web Services (AWS), IBM, Microsoft, Oracle, SAP, Salesforce and ServiceNow have all recently announced GenAI plans, ranging from AI virtual assistants to database management and cloud analytics.
In fact, use cases are evolving all the time. Oracle’s announcement in September was a good example of this, with its GenAI strategy spread across three main areas: infrastructure, models and services, and within applications. However, it admits this is only the start and sees GenAI capabilities helping its users reach new goals in everything from medical diagnoses and financial market analysis to analysing conversations, creating text and images, and writing code.
In a blog post, Ben Ufuk Tezcan, principal program manager for AI Platform at Microsoft, adds to this list, suggesting that “through scenario simulations and robust data analysis, generative AI assists in guiding strategic decision-making and optimises business processes”.
He talks about supply chain management and resource allocation, with GenAI identifying inefficiencies and proposing solutions to streamline workflows. “It’s also a boon for reducing administrative and repetitive tasks, freeing employees for high-value strategic work, and thus improving overall productivity,” he adds.
For Don Schuerman, chief technology officer (CTO) at Pegasystems, it fits with how every organisation is looking for an edge. Customers want AI to help drive efficiencies and productivity, but they want to do so in a way that minimises risk. With major enterprise suppliers embedding GenAI into so many application features now, the opportunities for GenAI to have a major impact over the next 12 months increase dramatically.
“A good example that we are seeing is using GenAI to generate low-code workflow applications,” says Schuerman. “We’ve seen really strong interest in this because enterprises see this as a way to accelerate what they’re doing. But they still have the ability for humans to override and change and reduce stuff the GenAI does, so they feel like there’s a safety catch built into this approach.”
This fits with what some users are seeing. While Schuerman adds that there is plenty of experimentation, embedding GenAI into existing ways of working is going to have a profound impact – the sort of impact that AI has always promised but not yet really delivered.
Peter Wood, founder and CTO at Spectrum Search, a Web 3, blockchain and crypto-focused recruitment firm, agrees. “In my experience, spearheading developments in AI and leveraging its power in enterprise applications has been a game-changer,” he says.
“Major tech giants like Google, Microsoft and Salesforce are making significant headway. They’re integrating GenAI into their platforms to create more intuitive user experiences, predictive analytics and automated decision-making capabilities.”
What is the rationale?
“Businesses crave efficiency and insights, and GenAI serves as the bridge,” says Wood. “At Spectrum Search, for example, we’ve harnessed AI and ML [machine learning] to revolutionise the client and candidate experience. We’ve noted the trend of borrowing elements from consumer interfaces and games to ensure user-friendliness. This blending makes enterprise platforms more engaging and familiar, easing the learning curve for users.”
This is important, and is one of the key attractions of GenAI. It dovetails nicely with trends in enterprise gamification, a consumer-like approach to application user experience (UX).
For Alexandre Pham, vice-president of EMEA at marketing analytics app maker Adjust, gaming app design uses basic psychology to keep users engaged and focused on their goals, and therefore helps to engage users, especially the younger, digital-native cohort.
“Progress bars, milestones and interface personalisation, for example, lead to elevated levels of user engagement,” says Pham. “By incorporating these elements, enterprise apps can create a more stimulating and enjoyable app experience and expect to see comparable results to gaming apps – most notably, improved user engagement, increased retention and higher user satisfaction.”
Alexandre Pham, Adjust
As Schuerman says, this is not new, but becomes even more interesting when GenAI is thrown into the mix. He admits he has already been having conversations with a banking data scientist about this, to help with workflow and productivity.
“The same model can apply to business processes, or engaging with customers,” says Schuerman. “AI plays a role in doing this because AI can detect intent – what are you trying to do? – and get you to the thing that actually drives that intent faster.”
A key thought here is perception and preparedness. Are there similarities with the early days of the world wide web, when there was a rush to self-build websites? The challenges of training large language models (LLMs) suggest an off-the-shelf approach will work best.
According to a survey from Infinum, 73% of firms admit to being ill-prepared for the integration of GenAI into their operations.
Embracent’s Lockton suggests this is a data issue. “Without accurate, reliable and accessible data, the value of any AI initiative will be inevitably compromised,” he says. And he’s right.
It’s not really mentioned in the pitch by many of the enterprise software suppliers currently parading their GenAI features. Enterprises do need clearer guidance on this to ensure there’s no FOMO (fear of missing out) on the GenAI opportunity here. This, of course, involves the old favourite of removing silos to ensure a connected enterprise capable of accessing and managing all relevant data.
Who said being a CTO was easy? Perhaps there’s an AI that can help with that.