Sundar Pichai’s speech at the AI Action Summit in Paris provided an unfiltered look at how artificial intelligence is reshaping the industrial landscape. The Google CEO positioned AI as not just a technological evolution but a seismic shift in the way businesses operate. While some comparisons have been made to previous transformations, the rise of personal computing, the proliferation of mobile technology, AI’s potential impact eclipses them all.
This is not hyperbole. The evidence is already visible. Pichai pointed to the precipitous drop in the cost of processing AI models, where the price per million tokens has declined by 97 per cent in just 18 months. The economic ramifications of such a shift are profound, particularly for industries reliant on complex, data-heavy operations. This is a moment of transition, where executives must not only acknowledge AI’s potential but act decisively to harness it.
The ecosystem for industrial AI
Industrial leaders are not passive observers in this transformation. AI is no longer a proof-of-concept or confined to tech-heavy sectors, it is becoming a core element of competitive advantage across manufacturing and supply chain management. Pichai was clear: AI is a general-purpose technology that will shape every part of the economy, including industrial operations.
The infrastructure to support AI at scale is already taking shape. Google has invested in a network spanning over two million miles of terrestrial and subsea fibre, ensuring that the backbone of AI is robust enough to meet the demands of industry. Tensor Processing Units (TPUs), now in their sixth generation, have tripled their carbon efficiency in just two iterations. These advances matter because the deployment of AI in manufacturing requires computational resources that can handle real-time analysis, predictive maintenance, and process optimisation without compromising sustainability goals.
These elements converge in AI models such as Gemini, which are built to process multimodal information – text, images, video, audio, and code. The implications for industrial applications are far-reaching. Whether integrating visual data from production lines with predictive analytics or automating quality control with AI-powered anomaly detection, the possibilities are tangible.
From experimentation to execution
Pichai made it clear that AI’s trajectory is moving from experimentation to execution. Industrial enterprises can no longer afford to treat AI as an isolated initiative or a series of disconnected pilot projects. It must be embedded into the fabric of operations.
This shift is most apparent in the way AI is augmenting human decision-making. Rather than replacing expertise, AI is acting as an enabler, reducing complexity, bridging gaps in knowledge, and breaking down barriers in accessibility. Pichai offered a practical example of deep research capabilities, where AI can synthesise vast amounts of information into concise, actionable insights.
In a manufacturing context, this means an AI system could analyse years of production data, identify inefficiencies, and propose optimisation strategies within minutes. Engineers and plant managers gain the ability to assess operational challenges in real time, armed with data that would have taken weeks to compile manually. This is the transformation AI promises: the ability to act on insights at a speed and scale previously unattainable.
Scientific discovery and industrial impact
Beyond operational efficiencies, AI is accelerating scientific breakthroughs with direct industrial applications. AlphaFold, Google DeepMind’s AI model for protein structure prediction, is a case in point. By unlocking solutions that were once computationally impossible, AI is reshaping material sciences, pharmaceuticals, and energy innovation.
Pichai noted that AlphaFold is being used to develop new malaria vaccines, cancer treatments, and even enzymes that can break down plastics. The crossover to industry is clear: these innovations influence everything from the development of sustainable materials to new approaches in bioengineering. Meanwhile, quantum computing, a field Google is investing heavily in, is expected to unlock new efficiencies in battery design, fusion energy, and materials science, further strengthening the industrial AI revolution.
The economic impact of these breakthroughs cannot be overstated. Isomorphic Labs, an Alphabet subsidiary, is using AI to improve drug discovery success rates while reducing costs. In France, pharmaceutical giant Servier is leveraging Google’s cloud AI technologies for similar applications. These advances are not theoretical, they are actively reshaping industrial processes today.
Preparing for the AI-driven future
For all its potential, AI presents its own set of challenges. Pichai acknowledged the technology’s limitations, from accuracy and factual reliability to ethical concerns around deepfakes and misuse. However, his broader point was clear: the biggest risk is inaction.
AI is already altering the workforce, with the World Economic Forum estimating that most jobs in Europe will soon be augmented by generative AI. While seven per cent of jobs may face automation, the impact of augmentation will be six times greater. This represents an opportunity, not just a challenge. Industrial enterprises must proactively prepare their workforce, ensuring that AI literacy and digital skills are embedded into organisational strategies.
Governments and businesses alike must also invest in AI infrastructure. Google’s $75 billion capital expenditure in 2025 is part of a broader trend among major technology players, reinforcing that AI adoption is not a question of if, but how quickly. Industrial leaders who fail to engage with AI at scale risk falling behind in productivity, efficiency, and competitiveness.
This is not a moment for hesitation. AI is reshaping industries at a pace that demands strategic action. Executives must move beyond viewing AI as a standalone initiative and integrate it into the fabric of their operations. As Pichai concluded, history may look back on this as the beginning of a golden age of innovation. The only question is whether today’s industrial leaders will seize the opportunity before them.
