The AI Forecast: What to expect in the next 12-18 months
Until recently, the business world has mostly experimented with AI and tried to determine its use cases and ROI. ChatGPT made headlines. Midjourney flooded the internet with surreal art. Startups raised billions overnight, and tech giants restructured their roadmaps around AI advancements.
Now, businesses of all sizes are exploring how to leverage AI to improve operations and reduce operational costs.
Over the next 12 to 18 months, enterprise use cases will shift from pilots to production. Small and mid-sized businesses will leverage advanced agentic AI that will make today’s algorithms feel crude.
This article outlines the technologies that are maturing now and the subtle but massive shifts they’ll create in how we live and work.
The Expansion of Agentic AI
In 2025, the world is bound to experience a shift toward AI systems that are intelligent enough to plan, reason, act, and iterate autonomously.
What Is Agentic AI?
Agentic AI refers to systems that can break down goals into multi-step processes and take action across digital environments. What is different is these agents don’t wait passively for instructions. Rather, they exhibit behaviors more akin to human collaborators. They reason about objectives and help revise their strategies.
The shift hinges on capabilities like:
- Long-term planning and memory
- Tool use and application switching
- Goal-seeking behavior with feedback loops
- Independent navigation of software environments
- Learn More: Building Proactive, Agentic AI Applications
Learn More: Building Proactive, Agentic AI Applications
Milestones That Signal the Shift
One of the most talked-about agent releases in 2025 is OpenAI’s Operator. It’s an AI assistant that can control your computer to complete tasks for you. For example, it can write code or book your travel without you having to click anything yourself.
It opens apps, uses your browser, interacts with interfaces, and carries out steps just like a person would. You can tell it what you want, and it will figure out how to do it using your device.
This marks a shift from AI being something you interact with, like asking chatbot questions, to something that actually acts on your behalf across different tools and platforms.
Google’s Project Mariner is a browser-based AI agent developed by Google DeepMind. Instead of giving you a single answer in a chat box, Mariner can browse the web for you, like a human assistant.
For example, if you show it a picture of some cookies and ask it to “find a matching recipe,” Mariner will go online, find the recipe, and even add the ingredients to your grocery cart. If it gets stuck—like not knowing which kind of flour to pick—it can figure out the next step on its own, like hitting the browser’s back button to check the original recipe again. Then, it continues from there.
Mariner shows how agents are starting to move beyond just giving suggestions. They’re learning how to complete multi-step tasks online and problem-solving along the way.
From Assistants to Autonomous Collaborators
OpenAI CEO Sam Altman gave a simple example of what this kind of AI could do. Imagine asking your agent to book a dinner reservation. A smart agent wouldn’t just check one place; it might contact hundreds of restaurants at once to find the best fit.
But he also described a bigger idea: agents that feel like smart coworkers. These agents would not just do small tasks but take on longer projects, use tools, solve problems, and check in only when needed. Altman said this kind of assistant could “go off and do a two-day or two-week task really well” and come back with a solid result.
Inference-Time Compute & Thinking Models
Earlier AI models quickly generated responses to prompts, but often returned wrong information or “AI hallucinations.” Modern models take time to think. They break problems into steps and work through them.
This is called inference-time compute. It means that the model spends more effort during its response to reason things out. For simpler prompts, the model might answer quickly. For harder ones, it can take more time and use more internal “thinking” to improve accuracy.
Two examples of this are OpenAI’s o3 model and Google’s Gemini 2.0 Flash Thinking. These models are trained to reason step-by-step during runtime, evaluating options, testing ideas, and choosing the best path forward. This process is often called chain-of-thought at runtime.
AI in Discovery and Research
A leading example of how AI is helping scientists is AlphaFold. DeepMind’s protein folding model solved one of biology’s biggest challenges and even earned its creators a Nobel Prize. AlphaFold’s success has sparked a wave of interest in how artificial intelligence trends are influencing scientific research and breakthroughs across a variety of fields.
In 2025, we’re seeing more models and datasets focused on materials science, chemistry, and biology. Meta and Hugging Face, for example, released tools like LeMaterial, which clean and organize massive materials datasets to speed up research. OpenAI has also given researchers early access to its o1 model to see how it can support advanced studies.
What’s different now is that these models can simulate experiments and even help generate new hypotheses if required. In some labs, AI is starting to act like a junior researcher, suggesting what to test next or summarizing results.
In the near future, companies like Anthropic are imagining what they call the “virtual biologist”—a system that could eventually do most of what a human scientist does, from literature review to experiment design. We’re not there yet, but 2025 is shaping up to be the year AI becomes a true research partner.
Robotics and General Intelligence
Robots have evolved far beyond their initial use in remote operations and are now taking on more versatile tasks that were once unimaginable.
Training in Virtual Worlds
Simulation has become a key part of robotics development. Tools like Genie 2 from DeepMind can generate interactive 3D environments from just a text prompt or image. They can practice a wide range of actions, like walking, grabbing, and so on before they’re deployed.
Foundation Models for Robots
In the same way that large language models (LLMs) can handle many types of questions, new foundation models are also being trained to handle many physical tasks. One example is π0 (pi-zero) from the startup Physical Intelligence. Trained across different robots and scenarios, this model aims to give machines the ability to generalize folding laundry, packing boxes, and more without being manually reprogrammed for each job.
Real-World Progress
Companies like Boston Dynamics and Figure are showing how these ideas work in practice. Boston Dynamics continues to refine their robots, which are now capable of dynamic motion and object handling. These systems are still early-stage, but they’re becoming more capable with each iteration.
As physical AI improves, robots will be able support everything from elder care to warehouse logistics. But this also raises big questions. How do we manage job displacement? What new skills will be needed? And how do we make sure these technologies are safe and aligned with human needs? Those conversations will be just as important as the tech itself.
Power Moves and Strategic Pivots
Major corporations are making strategic moves to secure their positions in the AI era.
Google’s AI-First Decade Materializes
Google’s decade-long commitment to an AI-first approach is coming to fruition through significant advancements across its product ecosystem. The introduction of the Gemini ecosystem is a testament to this evolution. Gemini is a powerful AI system that enhances Google’s products and services by providing tools that empower developers and businesses to innovate.
In the cloud computing arena, Google has integrated Gemini into its Vertex AI platform, offering a unified AI development environment.
Microsoft’s Strategic Investments in AI Startups
Microsoft has adopted a multifaceted strategy by forming alliances and investing in emerging AI startups to bolster its technological portfolio. A notable example is its $16 million investment in Mistral AI, a Paris-based startup specializing in foundational AI models. This partnership aims to bridge the gap between pioneering research and applications, with Mistral’s models being integrated into Microsoft’s Azure platform.
In parallel, Microsoft is significantly expanding its AI capabilities through Copilot, an AI assistant embedded in Microsoft 365 tools like Word and Excel. It represents the company’s commitment to integrating AI directly into everyday business applications.
Nvidia Faces Emerging Competitors
Nvidia, a dominant force in AI hardware, is encountering challenges from companies like AMD and Amazon. These firms are investing heavily in AI chip development, aiming to capture a share of the burgeoning AI hardware market. The competition is driving innovation and could lead to more diverse and specialized AI processing solutions.
AI in Defense and Ethical Considerations
The integration of AI into defense and national security has prompted significant ethical debates. Governments and private contractors are exploring AI applications for surveillance and autonomous systems. While these technologies offer enhanced capabilities, they at the same time raise concerns regarding autonomy, and the potential for misuse.
Synthetic Data and Training Bottlenecks
One of the biggest challenges in building large AI models today is the availability of training data. Most high-quality public datasets have already been used. Companies are turning to synthetic data—AI-generated examples that simulate new world scenarios to keep improving model performance.
Alongside synthetic data, two techniques that stand out are:
- Chain-of-thought supervision: This helps models learn the answer and the reasoning path behind it.
- Functional verification: Especially useful in code generation, this tests whether an AI-generated solution actually works, improving quality without needing massive new datasets.
There has also been a shift in how model improvements are made. Instead of releasing massive new models every year or two, labs like OpenAI are pushing smaller, faster updates that focus on reasoning ability. Their o1 to o3 progression happened in just three months. It was driven by improvements at inference time rather than just bigger model size.
Generative AI Evolves from Images to Worlds to Films
Generative AI’s evolution has been nothing short of astonishing. What began as the ability to generate simple images from text prompts is expanding and opening new opportunities for industries across the globe.
Right now, AI is already making waves in scriptwriting and scene creation. It helps writers come up with new ideas for plots and characters, sometimes even suggesting improvements based on what audiences like. In pre-production, AI helps with casting and location scouting by looking through tons of data to suggest the best options for a film.
Once filming begins, AI will step in to help with visual effects (VFX), like using AI to adjust an actor’s expressions after filming or even to de-age them. AI tools, like FaceDirector, are already helping directors perfect these scenes. It’s also used to speed up the editing and color grading process, making post-production quicker and cheaper.
By 2025, AI is expected to play a huge part in film marketing and distribution. Studios will use AI to predict what kinds of movies audiences want to see, making it easier to target the right people. Plus, AI can help create interactive experiences where the audience can engage with the film in new ways.
These advances in generative AI will make it possible for smaller studios, independent filmmakers, and even non-filmmaking businesses to create professional-looking film and video at a fraction of the cost. With AI making film production more affordable and efficient, anyone with a good idea can create high-quality video without needing millions in the budget.
Preparing for the Next 12-18 Months
AI is quickly becoming a crucial part of how businesses operate, and every industry will have its own AI revolution to look forward to.
As AI becomes more autonomous, businesses face important questions: How will you use AI to improve operations and reduce costs? How can AI drive innovation within your company?
At Taazaa, we specialize in helping businesses understand and implement AI strategically. From automating workflows to providing actionable insights through data, we build custom AI solutions designed to meet your goals.
Talk to our team, and let us help you transform your business with robust, customized solutions.
