From boardroom to break room, generative AI took this year by storm, stirring discussion across industries about how to best harness the technology to enhance innovation and creativity, improve customer service, transform product development and even boost communication.According to IDC, enterprises worldwide are expected to spend $307 billion on AI solutions next year, growing to $632 billion by 2028, at a compound annual growth rate of 29.0%. AI will have a cumulative global economic impact of $19.9 trillion through 2030 and will drive 3.5% global GDP in 2030, IDC predicts.
Yet, some companies and startups are still slow to adopt AI, sticking to experimentation and siloed projects even as the technology advances at a dizzying pace. That's partly because AI benefits vary by company, use case and level of investment.
Cautious approaches are giving way to optimism. Two-thirds of the respondents to Forrester Research's 2024 State of AI Survey believe their organizations would require less than 50% return on investments to consider their AI initiatives successful.
The next big thing on the horizon is agentic AI, a form of autonomous or reasoning AI that requires using diverse language models, sophisticated retrieval-augmented generation stacks and advanced data architectures.
NVIDIA experts in industry verticals already shared their expectations for the year ahead. Now, hear from company experts driving innovation in AI across enterprises, research and the startup ecosystem:
IAN BUCK
Vice President of Hyperscale and HPC
Inference drives the AI charge: As AI models grow in size and complexity, the demand for efficient inference solutions will increase.
The rise of generative AI has transformed inference from simple recognition of the query and response to complex information generation - including summarizing from multiple sources and large language models such as OpenAI o1 and Llama 450B - which dramatically increases computational demands. Through new hardware innovations, coupled with continuous software improvements, performance will increase and total cost of ownership is expected to shrink by 5x or more.
Accelerate everything: With GPUs becoming more widely adopted, industries will look to accelerate everything, from planning to production. New architectures will add to that virtuous cycle, delivering cost efficiencies and an order of magnitude higher compute performance with each generation.
As nations and businesses race to build AI factories to accelerate even more workloads, expect many to look for platform solutions and reference data center architectures or blueprints that can get a data center up and running in weeks versus months. This will help them solve some of the world's toughest challenges, including quantum computing and drug discovery.
Quantum computing - all trials, no errors: Quantum computing will make significant strides as researchers focus on supercomputing and simulation to solve the greatest challenges to the nascent field: errors.
Qubits, the basic unit of information in quantum computing, are susceptible to noise, becoming unstable after performing only thousands of operations. This prevents today's quantum hardware from solving useful problems. In 2025, expect to see the quantum computing community move toward challenging, but crucial, quantum error correction techniques. Error correction requires quick, low-latency calculations. Also expect to see quantum hardware that's physically colocated within supercomputers, supported by specialized infrastructure.
AI will also play a crucial role in managing these complex quantum systems, optimizing error correction and enhancing overall quantum hardware performance. This convergence of quantum computing, supercomputing and AI into accelerated quantum supercomputers will drive progress in realizing quantum applications for solving complex problems across various fields, including drug discovery, materials development and logistics.
BRYAN CATANZARO
Vice President of Applied Deep Learning Research
Putting a face to AI: AI will become more familiar to use, emotionally responsive and marked by greater creativity and diversity. The first generative AI models that drew pictures struggled with simple tasks like drawing teeth. Rapid advances in AI are making image and video outputs much more photorealistic, while AI-generated voices are losing that robotic feel.
These advancements will be driven by the refinement of algorithms and datasets and enterprises' acknowledgment that AI needs a face and a voice to matter to 8 billion people. This will also cause a shift from turn-based AI interactions to more fluid and natural conversations. Interactions with AI will no longer feel like a series of exchanges but instead offer a more engaging and humanlike conversational experience.
Rethinking industry infrastructure and urban planning: Nations and industries will begin examining how AI automates various aspects of the economy to maintain the current standard of living, even as the global population shrinks.
These efforts could help with sustainability and climate change. For instance, the agriculture industry will begin investing in autonomous robots that can clean fields and remove pests and weeds mechanically. This will reduce the need for pesticides and herbicides, keeping the planet healthier and freeing up human capital for other meaningful contributions. Expect to see new thinking in urban planning offices to account for autonomous vehicles and improve traffic management.
Longer term, AI can help find solutions for reducing carbon emissions and storing carbon, an urgent global challenge.
KARI BRISKI
Vice President of Generative AI Software
A symphony of agents - AI orchestrators: Enterprises are set to have a slew of AI agents, which are semiautonomous, trained models that work a
