If your AI strategy still looks like 2024, you’re already too late

The global economy has already moved past the great AI experiment of 2023–2024. Until 2022, AI was just a buzzword and was limited to individual adoption. But by 2025, AI tools will have become an integrated part of our daily lives in some form. We are using AI to polish our emails, suggestions on social platforms, to develop images, summaries of meetings, etc. Adoption of AI among companies has grown even stronger. Most enterprises have pivoted their transformation journey to accommodate AI in their processes.

By 2026, the cost of raw intelligence is expected to drop significantly. Research says that 90% of a company’s tech value won’t come from the software it owns but from the proprietary data it uses to train AI. Within the next two years, a single SME will be equipped with AI workflows that will eliminate the need for traditional departments of specialists at the back end.

As we progress in 2026, I am predicting some of the following AI trends. Sources of these predictions are work experience, market research and data from research firms and quotes made by industry legends.

Foundational-level performance of all models will be the same

Two years ago, when the score and quality output of ChatGPT, Llama, Gemini and Claude were distinctly apart. But as we are getting further in our AI journey, this gap has narrowed. The cost of running models is getting cheaper due to advancements in technology.  While all models are getting smarter in absolute terms, models have started generating similar and adequate performance for all basic tasks. As we go down further, what will differentiate those open or closed models now would be their parity of comparison in their specialized area.

In a nutshell, models won’t look distinct anymore; they are more of a commodity from the perspective of usability, performance and elementary tasks. Battle has gone to the next level, “point of comparison” (PoC) between the models. The likes of Gemini are likely to leverage their reach and tight integration across their ecosystem, and OpenAI will still enjoy a mindshare advantage in the minds of consumers. Claude and Anthropic will have a different fanbase of developers due to their specialized ability in code productivity.

So which model will win? Well, no one in all areas. While all AI tools will provide fundamental features of everyday AI, each AI tool will accelerate in its own territory. Advantage, however, lies with those who will have tight integration across various ecosystems with ease.

AI workflow >> AI agents

In the year 2025, agentic AI was perhaps the most celebrated topic in tech communities. Organizations lined up to embed autonomous agents in their everyday processes, even tried to force-fit where there was absolutely no need. The ideas were conceptualized around agentic AI, PoC happened and then “death by PoC.” Projects were not scaled to production. The reason? AI agents are costly to scale, unpredictable and require niche skills to implement, not to mention the pain of integration with other agents.

The massive leap from AI chatbots to agentic AI skipped a crucial middle step in between – “AI workflow.” AI workflows are not totally autonomous, but instead of unthinkingly fully automating entire processes that may result in unexpected outputs, AI workflows rely on step-by-step automation and use human-in-the-loop to ensure AI processes carefully execute every step. According to McKinsey, “No more than 10% of organizations report scaling AI agents in any individual function.”

ChatGPT reports that 20% of enterprise workflow is happening through custom or project. While fad chased agent AI in social media, the development was happening with AI workflows. The phrase “fully autonomous agent” creates unrealistic expectations, and we have still not fully solved the problem of data security. This looks like we are not exactly staring at “Year of the Agents,” but with the advancement we are making toward it, it will for sure become “The Decade of Agents.”

At the same time, integrating AI workflows is still fetching more impactful results in a shorter time than developing agents. While this may not remove the requirement of a human in between, it still saves 50-70% of person-hours at the back office.

Erasure of the technical barrier

Being a consultant, I had to depend on the marketing team to templatize my proposals in the correct format. Being an author, I must wait until the media team designs images that fit for my articles. These back-office jobs require specialized skills. It takes time to do that kind of work and requires a lot of back-and-forth communication.

The arrival of AI is shifting this trend. We are already witnessing the automation of many such jobs. With the proper context and prompt, GenAI tools are generating media, slides, documents, music, etc. Technical workers are no longer waiting for back offices to execute their tasks like reviewing spreadsheets, writing scripts, creating dashboards, grammar correction and programming support. User-friendly AI tools are enabling everyone to execute these specialized jobs by themselves.

This trend is only going to amplify in 2026. AI in the capacity of an equalizer is narrowing the skills gap between technical and non-technical users. In other words, if your value in an organization depends on only technical skills, your advantage is shrinking every day. The salesperson who used to rely upon an analyst for custom BI reports for his next sales pitch can do it himself.

Context >> Prompt engineering

Prompt engineering was a popular learning skill till 2024. There were videos, courses and experts specializing in this precise concept. The skill was mostly about what to give as input to AI and how to provide that input so that AI can respond most logically. It was around the time when ChatGPT was updated to upload external content and integrate with external providers. This was like giving mini reference material to AI. Even then, I didn’t think this would last forever. Of course, because if AI itself were so intelligent, then why do we need to provide context in a specific manner?

AI has now grown to a level where it can learn vague instructions, writing patterns, previous conversations, uploaded files, etc., with ease. This makes the precise science of prompt engineering less valuable today.

However, for AI to give more personalized answers, providing the right content is essential. Almost all AI tools now accept input files and media to perform tasks. Better file input would result in narrowing the fact gap. AI models can learn everything from the internet —  news, articles, website information, music library, sports box scores — but they know nothing about your personal requirements.

Models must be told the proper context, such as that you want your presentation for a board meeting, sheet-wise data for the technical head of department, or the email you are replying to, or what your manager requested yesterday. This is where the likes of Google, Microsoft and Meta have an advantage. If your organization is operating entirely on Microsoft Workspace, Copilot has access to personalize context emails, documents and calendar. Hence, it can provide a much faster and more precise response to your request. On the downside, this is also where vendor lock-in becomes a significant concern.

Sponsored content coming to AI

It takes about 10 times more money to print a newspaper than the actual selling price. So, how do printing presses sustain? It’s no secret: Advertising. A very similar analogy can be framed here. At this point in time, despite running an average subscription at 20 USD per month, most AI tools are not making any profit. The AI companies created a paradox: If they increase the price of their models, very few people will have access to those models and the learning data for those models will also be low in quantity. So how will they eventually succeed?

In 2026, sponsored ads are expected to cover the cost of models. Yes, it’s true that, like newspapers, AI cannot promote products within the advice or output of an AI prompt, as incentive-motivated outcomes will significantly lower the trust level. But ads can still appear in some other format on the page without disrupting the original idea of intelligence.

So, if you happen to see a Nike sneaker promotion appearing while you’re checking ChatGPT about leg pain you’re experiencing, you may assume that it was inevitable. No one likes ads, but it is the only way for AI companies to offer costly models accessible to everyone, from students, developers, and non-profits, without breaking their wallets.

Arrival of robotic AI

So far, we have witnessed AI robots only in sci-fi movies. The version of AI that we mainly use today, however, is in the form of software or a chatbot. But 2026 can mark the arrival of AI-powered hardware devices specialized to perform specific tasks.

In particular, in small sections of industries, it’s already happening. Waymo provides AI-enabled self-driving taxi services, and it has significantly reduced the accident rate by 96%. As per Walmart, China has deployed more industrial robots than the rest of the world combined. Robots may not necessarily take the form of a humanoid, but other formats could be significantly more visible going forward. Another critical aspect of investing in such hardware would be that, in time, they become more intelligent. An automated car will become safer to drive as it learns more data, driving patterns and rules.

AI governance officers as a job title

AI is vast and massive, but it is also confusing when it comes to handling data. Security officers have always been on the edge when it comes to blind exposure of data that AI models demand. There are very limited instruments in place that can control granule-level access to data once exposed to an AI system. In addition to ever-changing and complex regulatory compliance, shadow AI proliferation, misconfigured resources, access control, model poisoning and a whole other array of threats are emerging to AI systems.

This will demand a new security profile in the IT department. AI governance officers will be responsible for data and models security. Following the full enforcement of the EU AI Act and similar other global frameworks, there is a demand for transparency over data being fed into AI. Stanford’s HAI reports that “trust and safety” is now the fastest-growing department in Fortune 500 companies.

The human-centric premium

Technical expertise was born, grew, and advanced in last 50 years. The craftsmanship of software developers refined the world. However, we are at the juncture where expertise is being reset. And I already touched on how only core technical expertise will gradually become redundant as AI is likely to remove technical barriers. This should be a valid reason for resources to upgrade their skills.

When the technical barrier is removed, human-centric skills will take the stage. We are officially exiting the era of generalists. Those who can perform in multiple disciplines alongside technical expertise would be the most qualified resources. For example, someone who has the intuition to apply AI-produced output in a real-world system will become more valuable. In the world of infinite content and code, soft skills and the ability to build relationships, negotiate, the art of storytelling, problem solving, etc., will become a non-commoditized asset.

Conclusion

Year 2026 will take a significant leap into AI, and it won’t be defined by how fast technology moves but by how smartly individuals and organizations apply it to real-life requirements. The winner of the year 2026 won’t be a company with a brilliant 10-year plan, but those who are willing to learn, unlearn and relearn faster than machines. Here are my pointers for individuals and companies who are looking forward to embrace changes coming to AI this year.  

Strategies for individuals

• Select AI tools based on your specific task fit instead of model intelligence, benchmark score and model ranking.
• Start your AI workflow journey now by converting your recurring AI prompts into AI workflow automation while keeping human judgment as a gatekeeper at various stages.
• Grow beyond core technology by developing human-centric skills such as negotiation, storytelling, design thinking, and problem framing, as these will become differentiators post-shirking of the technology barrier.
• Organize files, labels and permissions deliberately so AI systems can use a clean context to produce accurate output.
• Validate AI inputs for possible commercial bias, especially in free and ad-funded AI tools.
• Build skills in emerging AI domains such as AI security, governance and robotics, where sudden demand might arise.

Strategies for companies

• Stop competing on AI models and focus on proprietary training, user experience and deep integration with your organization’s ecosystem.
• Identify and leverage AI to automate execution-heavy back-office tasks so that the team can work on higher value outcomes.
• Create your intelligence ecosystem to rightly balance fragmented AI adoption and portability to avoid vendor lock-in.
• Prepare marketing strategies to include AI platform optimization for potential sponsored discovery models.
• Invest early in AI robotics as a future service line and operational differentiator in your industry.
• Establish AI service, governance and ethics practices as part of your security team. 

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