The rules for choosing a tech partner have fundamentally changed. As AI agents automate coding and accelerate development cycles, the vendors who survive won’t be the ones typing code the fastest. They will be the ones who know what to build, why it matters, and how to make it work inside a real business environment.
During a vendor showcase, a Fortune 500 CEO stood up and asked a question that froze the room: “If AI agents can write code, fix bugs, and even deploy apps, why am I still paying you?”. It was blunt and a little uncomfortable, but it captured the tension shaping the global outsourcing landscape. If AI can automate development, then what is the real value of an IT vendor today?
For decades, the industry has treated “more developers writing more code” as a competitive advantage. But AI already produces usable code at a speed and scale that breaks that traditional logic. Around 12,000 employees, or approximately 2% of Indian Tech Giant - TCS's global workforce, have been laid off. The layoffs at TCS are occurring within a broader trend of the Indian IT sector adjusting to the increased adoption of AI and automation.
At the same time, despite this acceleration, AI projects are failing more than ever. Only 25% of AI initiatives meet their intended goals, according to IBM’s CEO Study. MIT’s Sloan Management Review reports that 95% of generative AI pilots never lead to measurable financial returns. Even the World Economic Forum notes that companies are deploying technology first and only afterward asking what it is supposed to achieve - a backwards approach that almost guarantees failure.
This is the real transformation unfolding beneath the surface: the world is overwhelmed with AI pilots but lacks AI outcomes. Companies do not need more coders. They need partners who understand how to guide AI, manage risk, and convert abstract potential into tangible business performance. In this new world, the outsourcing partners who win in 2026 will not be the fastest coders, but the clearest navigators.
There’s a deeper problem that many leaders do not see until it is too late. Anyone can purchase a ChatGPT license or plug in an off-the-shelf AI feature. But not everybody is able to align that feature with enterprise security requirements, embed it into legacy systems, build compliant data pipelines, ensure auditability, and maintain reliability under real workloads.
Industry researchers from NCPERS have pointed out that many AI “systems” simply feature APIs wrapped in marketing language. The complexity is not the model itself, but the architecture, governance, data verification, reliability, and long-term maintainability surrounding it. Even when organizations attempt AI pilots, they encounter a second major bottleneck: the context gap. AI models move incredibly fast, but they only perform well when given precise instructions. When requirements are vague, AI produces poor solutions at a faster speed.
This is why the best vendors in the AI era begin not with coding, but with understanding.
This new era demands a different approach to selecting a vendor. A future-ready partner is one who aligns incentives with outcomes, who approaches delivery with a product mindset, and who can balance rapid innovation with disciplined engineering.
The traditional “Time & Materials” model is fundamentally incompatible with AI. When vendors are paid for time, they are rewarded for working slowly. When AI accelerates development, the vendor’s financial incentives clash with the client’s need for speed. This misalignment becomes a strategic risk.
A modern partner embraces performance-led engineering, where gains from AI are shared with the client rather than hidden behind hourly billing.
This shift isn’t theoretical. When Synodus partnered with a Fortune 500 financial company, the goal was not to log more hours but to deliver a measurable transformation. The team built an automated credit scoring system that reduced assessment time from 10 days to just 2 days. It is a clear example of a vendor focusing on outcomes, not effort.
In the AI era, code is cheap, but clarity is rare. A product-mindset engineering team does not treat a project as a collection of feature requests. They treat it as a product that must solve a real problem for real users. Long before any coding begins, they analyze the problem, the user's behavior, the expected value, and the potential risks. This approach prevents the most expensive mistake in modern engineering: building the wrong thing very efficiently. Product-mindset teams also use AI earlier in the lifecycle, not to generate code but to visualize ideas, validate assumptions, and refine direction.
As Bill Claxton, Co-Founder & Operations Director of NextID, shared, his team initially struggled with the highly specialized language required to issue NFTs on the Tezos blockchain and lacked a clear workflow for the new feature they envisioned. “Synodus engineers showed us how the job could be completed with SmartPy, a high-level language with easy-to-use Python syntax,” he said. “They also drafted clear application flow diagrams that helped us understand how to implement 2FA authentication and other critical security components. Being able to see the design visually made all the difference.”
This is exactly why product-mindset engineering matters. Instead of rushing into development, the best vendors bring clarity to complexity long before a single line of code is written.
The third crucial sign is the vendor’s ability to operate within the Two-Lane Model: moving fast in one lane while remaining safe and compliant in the other. This is where many AI experiments collapse.
Consider a common scenario faced by early-stage startups. With AI-assisted development tools, they quickly produce an MVP that looks functional and impressive. Investors love it, prospects love the demo, and the team feels confident. But when the time comes to integrate that MVP into real systems with real data, real security, and real users, the entire structure breaks. The code cannot scale, the architecture is unstable, and compliance requirements are unmet. What looked like a breakthrough became a setback. This happens because AI moves faster than engineering discipline. Without a partner who knows where AI fails, when to experiment, and when to apply strict engineering rigor, businesses risk building products that collapse the moment they encounter the real world.
This balance between innovation and reliability is where Synodus distinguishes itself. The company brings together domain-fit engineering teams, banking-grade security and certification (including ISO 27001), and a dedicated innovation lane exploring AI and blockchain technologies. They move fast where speed is safe, and slow where stability is mandatory.
The role of the outsourcing partner has changed. The era of order-taker vendors is over. Leaders now need navigators who are capable of helping them avoid wasted budgets, protect them from shadow AI risks, and converting early experiments into measurable performance. Choosing an AI-ready vendor is no longer a procurement decision, but a strategic safeguard against failure.
To support leaders preparing for this shift, Synodus is hosting a webinar on January 6, 2026, titled AI in Software Delivery: From Pilots to Performance. The session, led by Tim Kitchens (CEO, Coding the Future with AI) and Cong Nguyen (Founder & CEO, Synodus), will unpack the Two-Lane Model, Product Mindset Engineering, and the performance-led delivery systems that distinguish true AI-ready partners from those in the past.