Strategy-first automation

Product developments on the AI side have been picking up pace. Anthropic, which just secured $30B Series G to expand AI products, promoted Claude Code at Davos this year – one of the most hotly discussed AI coding developments in the market. And this is just the latest in a long line of development milestones. OpenAI launched GPT‑5.2, designing a unified, multimodal AI model with advanced reasoning, long context, and persistent memory. This follows the release of Claude Skills API and Google A2A framework, along with powerful agents like ChatGPT Agent and ‘Deep research’, which can autonomously execute complex tasks: browsing, summarising, building slides, and completing orders.

 

We’re living in an age defined by rapid development and experimentation. IBM’s prediction for AI trends shaping the next 10 years says this much: "The future of AI is being defined by a shift toward both open source large-scale models for experimentation and the development of smaller, more efficient models to spur ease of use and facilitate a lower cost." But with a predominant focus by AI designers on encouraging product innovation and deployment, the critical piece of the puzzle to solve is how this all slots into industry.

 

 

Going back to the drawing board

The bottom line is that you can’t have AI used like an ornament – bells and whistles to add to existing structures, without a strategic purpose. We need to take these development cycles and consider how they successfully integrate into businesses, creating real ROI. Otherwise, the confidence in their efficacy will collapse.

 

In Box’s The State of AI in the Enterprise report, 60% of companies expect to achieve AI transformation within two years. And while 87% of companies have started using AI agents of any kind, 41% are using agents for advanced, fully autonomous operations that are delivering much higher productivity gains. This is a critical question to keep top-of-mind, especially as at least 30% of GenAI projects will be scrapped post-pilot, derailed by bad data, weak risk controls, rising costs, or fuzzy business value by the end of 2025, Gartner warns. Success in the future of industry rides on avoiding these AI pitfalls.

 

 

Where AI moves from promise to practice

OpenAI’s new GPT-5.2 model and ChatGPT agent are significant developments for enterprise AI – steps towards automating complex tasks from start to finish. For leaders, it’s important that excitement around automation is combined with a clear, all-around AI strategy. This marks a huge step up since GPT-3, an early breakthrough LLM that demonstrated general-purpose text generation at scale. Limitations drove later models: weaker reasoning, hallucinations, smaller context, no strong tool/agent capabilities.

 

Other major AI developers are doubling down on swift product cycles. Claude Sonnet 4.6 sees Anthropic prioritising a big push on agentic work: coding, tool use, long-context reasoning, and multi-step planning. It is a major upgrade to Anthropic’s “workhorse” model, pushing near-flagship performance at lower cost. On top of this, Claude Cowork demonstrates Anthropic’s keenness to replatform AI as a workplace agent, not just a chatbot, through offering integration with key workflow/ documenting tools.

 

 

Construct an AI strategy that works for you

OpenAI’s GPT-5.2, ChatGPT Agent and tools like Claude Sonnet 4.6 all point to a clear shift towards AI that can handle complex, end-to-end tasks. But while innovation is accelerating, enterprise adoption is still catching up. GenAI tools remain early in their maturity. They can make mistakes, hallucinate, and require oversight, with the burden of correction often falling back on the user. Even low error rates can compound across multi-step workflows, creating real risk for reliability and trust.

 

At the same time, demand is outpacing the ability to implement effectively. The challenge is no longer access to AI, but how to make it work in practice. AI does not deliver value as a bolt-on. It needs to be connected to enterprise content, supported by strong governance and security, and tailored to the specific business context. Without that, organisations end up with fragmented use cases rather than meaningful transformation.

 

The organisations seeing impact are taking a more deliberate approach. They are defining where AI fits, putting guardrails in place, and designing workflows around it from the start. Human oversight remains critical as systems become more interconnected, and as AI enables teams to operate across traditional boundaries. The result is a shift in how work gets done, with human effort increasingly focused where it drives the most value.

 

 

Being AI-first means creating a playbook

With all the promise of AI’s great impact, it’s important to unpack the specific areas where we are seeing AI alleviate the burden of menial work. Integrations in industry are demonstrating that enterprise IT is shifting from app-centric stacks to multiagent architectures, where fleets of AI agents work together across systems to achieve shared goals.

 

In the supply chain, AI is helping to monitor inventory, spot shortages, and trigger supplier orders, all without bespoke integrations. McKinsey has identified that technology leaders will deploy these capabilities in three main ways: through super platforms with built-in agents ready to plug in, AI wrappers that connect internal systems with third-party services securely, and custom agents fine-tuned on proprietary data for tailored use cases.

 

Together, these models mark a fundamental reimagining of how enterprise technology is built and operated. Yet before AI is even considered as a solution, you need to interrogate your core challenges. If you don’t have a clean process today, it’s very hard to generate impactful automation. When the process is ironed out, and you’ve needled out the ‘Why?’ for AI, consider long-term how multiple tools and departments work in concert.

 

 

Plotting out your AI-accelerated future

There is no ROI in AI for its own sake. Only in AI that works. AI maturity unfolds in stages, from augmenting decisions to automating processes, to orchestrating fleets of agents capable of running complex functions under human direction. Each step requires stronger data, tighter governance and clearer intent.

 

The real transformation is in structures. Enterprises that thrive will treat AI not as an add-on, but as an operating layer: redesigning workflows, reskilling teams and building trust into every automated decision. The shift to AI-first is as much cultural as technical, and those who achieve success will find ways to knit the two together.

 

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Yashodha Bhavnani is Head of AI at Box

 

Main image courtesy of iStockPhoto.com and HudHudPro