Generative AI is revolutionising industries, but widespread adoption faces challenges in infrastructure, data quality, and ethical use
It’s clear that Generative AI has reached a critical mass, representing one of the most profound technology revolutions of our time.
With various accounts of people using ChatGPT for work hacks, from creative ideation, designing marketing collateral to generating slick videos, it’s evident AI is set to transform the way we work and live.
If you’re of a certain vintage (like yours truly), you may even be brought back to the heady days when e-commerce and social media took off with the promise to deliver the innovations and experiences that we now take for granted.
There’s no doubt that Generative AI is a game-changer for all industries, but also no guarantee that everyone will benefit equally from its full potential and impact.
In March, MIT Technology Review Insights (MITTR) launched a research report in partnership with Telstra, which investigated how global organisations are implementing – or planning to implement – Generative AI technologies, along with the barriers to effective deployment.
Over three-quarters of businesses surveyed (76 per cent) were already working with Generative AI, but only nine per cent had adopted the technology widely. The most common use case for Generative AI was automating non-essential tasks – a low-to-modest-gain but minimal-risk usage of the technology.
Hype or reality?
What should we make of this stark contrast between these findings and the exciting vision of an AI-powered world we hear so much about?
History shows that any transformative technology takes time to bear fruit. Even the PC and the internet took more than a decade to fully drive the change and growth that was promised. This lag stems from the time needed for technologies to become truly pervasive across business and society.
Generative AI will follow a similar path.
Businesses are at the coalface of shaping technology adoption and will be pivotal in seeding AI’s potential throughout society. But to assume this role and unlock that potential requires them to be data-driven, AI-fuelled, as well as lead in terms of responsible AI adoption.
The MITTR study highlights that a major mindset shift needs to happen for these factors to become the norm for business. In terms of IT, a significant perceptions gap exists today between early AI adopters and other respondents.
Fewer than 30 per cent of respondents deemed their IT infrastructure to be conducive to the rapid and successful adoption of Generative AI. However, early adopters were found to have less confidence in their IT than other respondents, with more than six in 10 saying their available hardware was, at best, modestly conducive. This compared with 50 per cent of other respondents who answered similarly.
The need for robust IT and data infrastructure
The above finding suggests that a large proportion of businesses underestimate the requirements for the effective deployment of Generative AI. That’s a concern as these technology assets are necessary to develop and run the AI from which organisations seek to benefit.
What do these requirements look like?
While there is no one-size-fits-all solution, it’s clear that successful AI adoption requires a well-designed data architecture, sufficient computing power, and robust connectivity to nurture novel applications and business models that will improve efficiency and profitability.
Companies need to understand the necessary IT requirements and accelerate the transformation of legacy tech infrastructures to address deficiencies, or risk falling short of their Generative AI ambitions.
However, IT assets that support extensive, high-quality Generative AI tools and platforms remain rare. Appropriate hardware, either in-house or outsourced, is a prerequisite of extensive Generative AI adoption. The choices are complex and require planning. Executives, however, often fail to grasp the degree of the requirement.
Good data is also an IT asset often in short supply but fundamental to enterprise deployment of Generative AI. Large volumes of quality data and storage remain basic requirements for effective deployment.
As the world becomes increasingly digitised and human-to-machine interactions flourish, being able to process data to drive informed, real-time or near real-time business decisions is paramount. When implemented successfully, this proficiency will be a game-changer for most organisations, and will distinguish leaders from followers.
Responsible and ethical AI
To balance leveraging AI’s potential while reducing its potential risks, organisations cannot overlook the responsible and ethical development and deployment of the technology. Principles such as privacy, security, contestability, and accountability are critical, and should be supported by other frameworks and controls.
Becoming an AI-fuelled organisation is a whole-of-business approach and must be infused in the organisation’s culture. Organisations should ideally have a single, dedicated body representing different business functions to provide advice and approve measures relating to AI development and deployment.
At Telstra, we take this very seriously and hold ourselves to a high bar. We’ve co-developed a series of ethics principles and standards with the Australian government, and partnered with other telcos and businesses around the world to establish ethics frameworks.
Internally, we’ve also set up robust governance policies and guardrails, including the formation of a Risk Council for Data and AI (RCAID) from across Telstra’s business. Any AI systems (including third-party systems) with significant stakeholder impact must be reviewed and either approved by RCAID or escalated.
To uplift our people’s understanding and skills, we’ve also set up a data and AI academy to create opportunities for them to learn and work with AI tailored to different cohorts: leaders, data professionals, and the broader business.
Telstra is now using AI to improve half of our key processes, including to automatically detect and resolve fixed services faults, and to solve customer issues faster. Through Telstra’s Cleaner Pipes initiative in Australia, we’re blocking millions of calls, text messages and incoming scam and potentially unwanted emails from reaching our customers each month.
As organisations accelerate their adoption of AI, the road ahead is unknown, but what’s clear is that this calls for a paradigm shift. Making the leap from early adoption to becoming truly AI-fuelled requires an unwavering conviction to doing what’s right, along with the agility to flex and seize opportunities as they arise.
Organisations that master this will find themselves ahead of the curve as leaders in responsible AI adoption, unlocking better outcomes for their people, customers, and society.
Source: e27
