Tech Explained: 2026: The Year Supply Chain Teams Take Back Control of Their Software in Simple Terms

For years, organizations have adapted themselves to fit the limitations of their software, not the other way around. Systems designed to drive efficiency often end up dictating how people work, stifling what makes each organization unique. Whether in supply chain operations or customer service, too many teams find themselves working for their technology rather than with it.

Now in 2026, this mindset must change. The pace of business – from shifting customer expectations to evolving global networks – demands technology that adapts at the speed of the organization, not the speed of a software release cycle. The winners will be those who use technology, particularly AI, to amplify their unique strengths and ways of working rather than forcing conformity to rigid systems.

So, how should leaders rethink their relationship with enterprise software in the year ahead? How can they break free from standardized workflows and instead build digital environments that enhance agility, creativity, and organizational performance?

MIT report and conditions for success

In August 2025, an MIT study revealed that 95% of generative AI pilots at companies deliver zero measurable return on investment. Coupled with comments about the AI bubble, the report’s release triggered the tech-heavy Nasdaq Composite index to drop by 1.4%, its largest single-day decline in some time.

This was a classic case of people, namely investors, taking headlines at face value.

But if you actually read the full report, it’s not as clear cut. The study also highlights how there are clear conditions for success with Gen AI and AI. The issue lies in people and organizations understanding how to integrate AI tools so that they fit into their workflows and processes.

However, the study did attribute most failures not to the quality of the AI models themselves, but to a “learning gap,” organizations failing to adapt their systems, processes and cultures to effectively integrate the new tools. But with supply chain teams, for instance, this overlooks how many are tied to complex processes and regulatory requirements that have to remain in place. In actuality, the real issue is how agile the AI software is and how quickly it can be implemented.

Many companies are bound by large monolithic systems that are quite rigid, meaning they’re not configuring them for the business. Instead, they’re almost configuring the business to these systems, and that’s why they’re going to struggle. Subsequently, they find it hard to keep up with newer AI deployments and solutions and get less success from those. So, configuring and tailoring solutions to the business is key. It’s not one-size-fits-all.

An adaptable, vendor-driven approach

Another point of interest from the study was vendor strategy. In particular, the fact that purchasing platforms was more successful than building them internally: buying AI tools succeeded around 67% of the time, compared to one-third of the time when they’re built in-house.

This success rests on choosing the right vendor, the right product, and then adopting it strategically. A good vendor will understand the business and tailor its products to what you need. The idea should be to start narrowly and focus the product on a particular task or use case and then scale out from there rather than building something which is generic.

There’s always a bit of a tension between what the product does, what the company’s expertise is, and the uniqueness of the business. This is particularly prominent in supply chains and some of the traditional approaches used for software and information systems supporting the sector. Overcoming this tension and forming a balance involves finding a product that is scalable and secure but also adaptable at the same time.

The MIT study has shown that people need to rethink the way they’re defining software to solve their supply chain problems, especially with new technologies like agentic AI. There needs to be a different way of using software in supply chains – one that’s more focused and lightweight.

A different way of doing software

The speed at which AI is developing is unprecedented. If supply chain teams are to keep up, they need software that is highly adaptable. One key thing to look out for is extensible software. Through system capabilities that utilize principles of interoperability, transparency, and security as first principles means teams can integrate new features and capabilities without making major changes to the platform.

Integrations are of course a fundamental part of this. Software should easily connect with existing and critical supply chain management systems like ERP, MRP, TMS, CRM, and supplier and inbound information systems. This enables it to access and house all relevant data in one location. The better data AI has to work with, the far more successful it will be, and centralized, real-time data is crucial to supply chain management.

In terms of strategy, it’s more effective to start with a single AI application and then add more applications over time that are able to optimize different parts of the business. For example, a supply chain team might want to really focus on avoiding stockouts and prioritize using AI to optimize their inventory levels. The company then has a solid foundation to expand AI capabilities to other tasks and teams and incorporate newer tech like agentic AI.

A new software mindset for 2026

The change in pace in business has often prompted companies to adapt their processes to fit their software, rather than the reverse. But this change should be seeing software adapt to supply chain teams’ processes and empower them to take control over its development. This is the mindset shift that must take place in 2026.

As the MIT study shows, “failure” comes from how AI is being adopted. What companies need is an agile, targeted and lightweight approach to using software and AI in supply chains that maximizes their impact. The teams that take on this alternative approach will be the ones who scale AI effectively and build technology that works for them, not against them. This will result in supply chains that are more flexible, adaptable, and resilient, and capable of producing more value both for the organizations in that supply chain, and the end customer.