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AI readiness requires value as IT investments surge to $172.3 billion – ARN

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“The top approach that’s used today is human review, but the human in the loop equation is collapsing on itself. AI can make mistakes faster than we humans can catch them, or worse, facts themselves can be distorted.”

While AI agents are at the top of the hype cycle, currently 14 per cent of CIOs in Australia report that their organisations are already adopting AI agents.

“An additional 42 per cent are expecting to adopt them within the next 12 months. But not all agents are created equal,” said Plumber. “86 per cent of local IT leaders [are] focused on conversational agents.

“Conversational agents are ready for conversations, but if you need them to make decisions, and you should, then conversational agents are not ready. Decisions are necessary for autonomous multi agent systems.”

Agents, as experts, can create real world value as long as you’re clear about what you need from the technology, noted Plumber. However, these technologies aren’t cheap, and the true costs have to be carefully considered.

Losing track of costs

“The first thing to know is that CFOs are losing track of what is being spent on AI projects,” he said. “They know the cost on day one [but] they don’t know the cost on day 100. Here in A/NZ, 65 per cent of organisations are barely breaking even or even losing money on their AI investments.

“If previous tech introductions had upfront transition costs, AI has a transition mortgage that you just keep paying.”

For example, when an ERP is first implemented, there are several cost considerations, which range from licencing the software and training staff on one kind of work. With AI, the work and the training keeps switching contacts.

“First you learn to summarise emails and then it helps you do data analysis,” said Plumber. “Then you’re creating compelling videos. You use one model before lunch and a different one after. You have to train your people, give them experiential knowledge, connect them with peers and show them what good looks like, because most people are just discovering what they can do.”

However, Plumber noted they’re not being trained on what they should do. This where the bill for an AI implementation costs an average of $1.9 million per company, and that’s just on day one.

“Then you need to invest heavily in AI training and literacy programs because for every 100 days of implementation, AI has 25 more to train staff,” he said. “Worse yet, change management, that costs another 100 to 200 days.

“That’s up to 200 per cent more effort and next on the bill is ancillary costs. For every AI tool that you buy, there will be 10 ancillary costs that you didn’t anticipate.”

The spiralling costs includes managing access credentials for autonomous agents, acquiring new data sets to ground AI, managing multiple models, or packing an accuracy survival kit.

“There’s a hidden transition cost to all of that,” said Plumber. “What do you do for every AI tool you buy? Anticipate 10 hidden costs, conduct an analysis and decide which cost you will fund.

“The last thing you want to be is the unintended owner of a negative ROI business case, and you still need to select the right AI vendors for your needs.”

If choosing an ERP vendor looks like getting married, then choosing an AI vendor is like getting married, having triplets and moving to a different country; it’s complicated, said Plumber.

“Just like kids who touch everything with their tiny little hands. AI is touching everything; … when marrying an AI vendor, their models are like your children that need to be fed the right data to grow constantly re-educated and grounded,” he said. “Those children take on your ethics — the ethics of the vendor that you marry. It’s not just about the kids.

“You do have to pick the right country to move to in the AI race [as] those vendors are starting to resemble countries to live in. We call them digital nation states; a vendor that controls land, power, water, talent and capital to rival those of actual nations.

“Large vendors spend more on AI infrastructure per quarter than 47 per cent of the world’s annual GDP [gross domestic products] in countries.”

Mullery noted when planning a massive rollout of AI to your enterprise, bet on the major hyperscalers.

“These are players like Microsoft, Google, Amazon, [and] Alibaba,” she said. “They are superpowers, sometimes with government support, and always with massive ecosystems and AI infrastructure if you want industry specific use cases, look to the many startups and those who partner with industry leaders.

“The combination of innovation and domain knowledge comes with access to new markets, customers and data, and this is more like a developing country.”

Lilia Guan travelled to the Gartner Symposium and IT Expo as a guest of Gartner.



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AI engineers are being deployed as consultants and getting paid $900 per hour

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AI engineers are being paid a premium to work as consultants to help large companies troubleshoot, adopt, and integrate AI with enterprise data—something traditional consultants may not be able to do.

PromptQL, an enterprise AI platform created by San Francisco-based developer tooling company Hasura, is doling out $900-per-hour wages to its engineers tasked with building and deploying AI agents to analyze internal company data using large language models (LLMs).

The price point reflects the “intuition” and technical skills needed to keep pace with a rapidly-changing technology, Tanmai Gopal, PromptQL’s cofounder and CEO, told Fortune

Gopal said the company hourly wage for AI engineers as consultants is “aligned with the going rate that you would see for AI engineers,” but that “it feels like we should be increasing that price even more,” as customers aren’t pushing back on the price PromptQL sets.

“MBA types… are very strategic thinkers, and they’re smart people, but they don’t have an intuition for what AI can do,” Gopal said.

Gopal declined to disclose any customers that have used PromptQL to integrate AI into their businesses, but says the list includes “the largest networking company” as well as top fast food, e-commerce, grocery and food delivery tech companies, and “one of the largest B2B companies.”

Oana Iordăchescu, founder of Deep Tech Recruitment, a boutique agency focused on AI, quantum, and frontier tech talent, told Fortune enterprises and startups are competing for senior AI engineers at “unprecedented rates,” and which is leading to wage inflation.

Iordăchescu said the wages are priced “far above even Big Four consulting partners,” who often make around $400 to $600 per hour.

“Traditional management consultants can design AI strategies, but most lack the hands-on technical expertise to debug models, build pipelines, or integrate systems into legacy infrastructure,” Iordăchescu said. “AI engineers working as consultants bridge that gap. They don’t just advise, they execute.”

AI consultant Rob Howard told Fortune he wasn’t surprised at “mind-blowing numbers” like a $900-per-hour wage for AI consulting work, as he’s seen a price premium on projects that have an AI component while companies rush to adopt it into their businesses.

Howard, who is also the CEO Innovating with AI, a program to teach people to become AI consultants in their own right, said some students of his have sold AI trainings or two-day boot camps that net out to $400 or $500 per hour.

“The pricing for this is high in general across the market, because it’s in demand and new and relatively rare to find, you know, people who are qualified to do it,” Howard said.

A recent report published by MIT’s NANDA initiative, revealed that while generative AI holds promise for enterprises, 95% of initiatives to drive rapid revenue growth failed. Aditya Challapally, the lead author of the report and a research contributor to project NANDA at MIT, previously told Fortune the AI pilot program failures did not fall on the quality of the AI models, but the “learning gap” for both tools and organizations.

“Some large companies’ pilots and younger startups are really excelling with generative AI,” Challapally told Fortune earlier this month. Startups led by 19- or 20-year-olds, for example, “have seen revenues jump from zero to $20 million in a year,” he said. 

“It’s because they pick one pain point, execute well, and partner smartly with companies who use their tools,” he added.

Jim Johson, an AI consulting executive at AnswerRocket, told Fortune the $900-per-hour wage “makes perfect sense” when considering companies have spent two years experimenting with AI and “have little to show for it.” 

“Now the pressure’s on to demonstrate real progress, and they’re discovering there’s no easy button for enterprise AI,” Johnson said. “This premium won’t last forever, but right now companies are essentially buying insurance against joining that 95% failure statistic.”

Gopal said PromptQL’s business model to have AI engineers serve as both consultants and forward deployed engineers (FDEs)—hybrid sales and engineering jobs tasked with integrating AI solutions—is what makes their employees so valuable.

This new wave of AI engineer consultants is shaking up the consulting industry, Gopal said. But he sees his company as helping shift traditional consulting partnership expectations and culture. 

“The demand is there,” he said. “I think what makes it hard is that leaders, especially in some of the established companies… are kind of more used to the traditional style of consultants.” 

Gopal said the challenge for his company will be to “drive that leadership and education, and saying, ‘Folks, there is a new way of doing things.’”

Fortune Global Forum returns Oct. 26–27, 2025 in Riyadh. CEOs and global leaders will gather for a dynamic, invitation-only event shaping the future of business. Apply for an invitation.



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AI is introducing new risks in biotechnology. It can undermine trust in science

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The bioeconomy is entering a defining moment. Advances in biotechnology, artificial intelligence (AI) and global collaboration are opening new frontiers in health, agriculture and climate solutions. Within reach are safe and effective vaccines and therapeutics, developed within days of a new outbreak, precision diagnostics that can be deployed anywhere and bio-based materials that replace fossil fuels.

But alongside these breakthroughs lies a challenge: the very tools that accelerate discovery can also introduce new risks of accidental release or deliberate misuse of biological agents, technologies and knowledge. Left unchecked, these risks could undermine trust in science and slow progress at a time when the world most needs solutions.

The question is not whether biotechnology will reshape our societies: it already is. The question is whether we can build a bioeconomy that is responsibly safeguarded, inclusive and resilient.

The promise and the risk

AI is transforming biotechnology at a remarkable speed. Machine learning models and biological design tools can identify promising vaccine candidates, design novel therapeutic molecules and optimize clinical trials, regulatory submissions and manufacturing processes – all in a fraction of the time it once took. These advances are essential for achieving ambitious goals such as the 100 Days Mission, the effort to compress vaccine development timelines in response to future emergent pandemics within 100 days, enabled by AI-driven tools and technologies.

The stakes extend beyond security. Without equitable access to AI-driven tools, low- and middle-income countries risk falling behind in innovation and preparedness. Without distributed infrastructure, inclusive training datasets, skilled personnel and role models, the benefits of the bioeconomy could remain concentrated in a few regions, perpetuating inequities in health security, technological opportunity and scientific progress.

Building a culture of responsibility

Technology alone cannot solve these challenges. What is required is a culture of responsibility embedded across the entire innovation ecosystem, from scientists and startups to policymakers, funders and publishers.

This culture is beginning to take shape. Some research institutions are integrating biosecurity into operational planning and training. Community-led initiatives are emerging to embed biosafety and biosecurity awareness into everyday laboratory practices. International bodies are responding as well: in 2024, the World Health Organization adopted a resolution to strengthen laboratory biological risk management, underscoring the importance of safe and secure practices amid rapid scientific progress.

The Global South is leading the way in practice. Rwanda, for instance, responded rapidly to a Marburg virus outbreak in 2024 by integrating biosecurity into national health security strategies and collaborating with global partners. Such exemplars demonstrate that with political will and the right systems in place, emerging innovation ecosystems play leadership roles in protecting communities and enabling safe participation in the global bioeconomy.

Why inclusion and equity matter

Safeguarding the bioeconomy is not only about biosecurity; it is also about inclusion. If only a handful of countries shape the rules, control the infrastructure and train the talent, innovation will remain unevenly distributed and risks will multiply.

That is why expanding AI and biotechnology capacity globally is so urgent. Distributed cloud infrastructure, diverse training datasets and inclusive training programmes can help ensure that all regions are equipped to participate. Diverse perspectives from scientists, regulators and civil society, across the Global South and Global North, are essential to evaluating risks and identifying solutions that are fair, secure and effective.

Equity is also a matter of resilience. A pandemic that spreads quickly will not wait for producer countries to supply vaccines and treatments. A bioeconomy that works for all must empower all to respond.

The way forward

The World Economic Forum, alongside partners such as CEPI and IBBIS, continues to bring together leaders from science, industry and civil society to mobilize collective action on these issues. At this year’s BIO convention, for example, a group of senior health and biosecurity leaders from industry and civil society met to discuss the foundational importance of biosecurity and biosafety for life science, to future-proof preparedness and innovation ecosystems for tomorrow’s global bioeconomy and to achieve the 100 Days Mission.

The bioeconomy stands at a crossroads. On one path, innovation accelerates solutions to humanity’s greatest challenges: pandemics, climate change and food security. On the other path, the same innovations, unmanaged, could deepen inequities and expose society to new vulnerabilities.

The choice is ours. By embedding responsibility, biosecurity and inclusive governance into today’s breakthroughs, we can secure the foundation of tomorrow’s bioeconomy.

But responsibility cannot rest with a few institutions alone. Building a secure and equitable bioeconomy requires a shared commitment across regions, sectors and disciplines.

The bioeconomy’s potential is immense. Realizing it safely will depend on the choices made now. Choices that determine not just how we innovate, but how we safeguard humanity’s future.

This article is republished from World Economic Forum under a Creative Commons license. Read the original article.



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