Every company should be preparing for the landscape shifts that AI will bring in 2026. AI has already transformed entire industries, and its rate of acceleration is paving the way for even more advancement in 2026.
We work with AI every day and have to stay ahead of the progressions to stay competitive. We see trends, help companies (including ourselves) unlock its value, and build tech that’s driving the next wave of AI innovation.
Here’s what I think 2026 is going to hold for AI, increasing its impact on technology, businesses and society. These predictions are largely driven by two technologies combined: AI agents and AI-fueled coding. The power of these technologies together is democratizing AI, putting world class AI power into even more hands!
Fine-tuned SLMs will become the most-used models by enterprises.
Fine-tuned small language models are built for specific purposes and trained on focused data, providing high accuracy for their specialized tasks. They’re breaking the old adage: “Between good, cheap and fast, choose two.” These SLMs can provide all three benefits compared to their large language counterparts, often performing very comparatively with the larger, more generalized models in terms of accuracy and outperforming with speed and costs.
It’s why businesses are increasingly relying on them. The large language and reasoning models will often handle the master control of an agentic workflow, but the purpose-built SLMs very adequately deliver the required accuracy and efficiency when trained for their dedicated job within the agentic workflow.
Across the board, businesses will understand the importance of their own data in driving AI value. Fine-tuned SLMs are key to unlocking that value in mature agentic solutions.
AI-fueled coding will be the next major development methodology, reducing some development cycles to just minutes.
AI tools have been helping developers for years, handling repetitive tasks, generating boilerplate code and debugging programs. AI-fueled coding will be the next big methodology, bringing the spirit of agile coding into its next evolution. This will tangibly redefine the software development cycle, shortening development timelines, increasing production-grade output and enabling teams to focus on higher-level problem solving.
With AI-fueled coding, developers will start to wear multiple hats in the lifecycle, from product owners to architects, reducing cycle times and time to operation. As AI gets better, the best cases will see products or apps built in basically one shot, with very few human edits.
We’ve used AI-fuel coding ourselves to build an internal curated data product in 20 minutes, when it would have taken 6 weeks without AI. And importantly, our AI-fueled coding framework is trained to adhere to our rigorous code discipline for quality, security, and compliance.
Non-technical teams can get in on the development process now too, using plain language prompts to build software prototypes. Then AI-fueled coding can turn it into a full product, with true production grade code, within hours instead of weeks.
Businesses will begin building on-demand apps, supported by AI agents.
Most business applications have traditionally required long development cycles, continued investment, and constant maintenance. AI-fueled coding dramatically accelerates software development cycles, making it feasible for a company to build on-demand apps!
Autonomous agents can even independently adapt to new requirements, making redevelopment faster than traditional app cycles. Businesses can respond faster to changing needs, experiment with new solutions, and pivot away from legacy apps that require long-term investment.
Traditional apps won’t completely disappear…yet. The ability to launch and iterate on-demand functionality, in a fraction of the time, leveraging agentic AI will enable a more agile and cost-effective choice for immediate business challenges than traditional models in many situations.
Private high capacity, low latency fiber networks will connect enterprises to consolidated compute centers.
Computing relies on connectivity. With AI, complex computing needs to be faster than ever before. Companies are re-evaluating their cloud strategies to make the most out of AI, but they will also be looking at their connectivity.
We’ll see an increase in private high-speed fiber networks built for major enterprises who are investing in AI. This will connect businesses to their cloud and compute centers with the fastest connectivity speeds and dedicated pathways.
When they have these pathways, we’ll also see cloud companies consolidate their cloud centers close to where private networks exist. Cloud centers could likely move closer to the premises, in a new era of cloud, hybrid-cloud and on-prem networking.
Telcos will offer more AI services, like fine-tuning, to business customers.
There are few industries that deal with data on the scale of telecommunication companies. We’re well-versed in training large and small models, and we work with major cloud and AI software providers, giving us a strong foundation in AI services like fine-tuning.
To be clear, we’re not announcing anything available from AT&T. But we have mastered fine-tuning models on our network data, and have a long history in how we manage, process and secure massive datasets. I believe the foundation and the market demand is there to extend telco services out of core connectivity and cloud hosting, into productized AI services.
By helping business customers tailor their AI needs, telecom companies can open up new revenue streams and expand their already essential role in the AI-powered business landscape.
Metrics for AI accuracy, cost and speed will become the focus across every business sector.
With the democratization of development tools comes the spread of AI business metrics. AI is shifting from a novelty into a necessity in every industry, and every department. But it’s not enough to use AI tools. They have to use them well, with measurable AI results. Accuracy is what drives value, and optimization is what’s needed for Generative and Agentic AI to measure up across any use case.
What are the most important measures of AI? Speed, accuracy and cost. Every organization, and every department – from marketing to human resources – will start to track return on investment, reliability and scalability for their AI tools.
AI will be the common denominator, and a shared language, across the entire business world.
Preparing for the Future
AI is an integral part of every day, creating new possibilities and opportunities for our personal and professional lives. These are only predictions. Nothing is written in stone, and it’s possible that something new and completely unexpected will pop up and change the game entirely. But we have a lot of AI experts at AT&T.
We’re always building for the future, and this is the world that AT&T is preparing for.
