The decline of assistive AI: why the future of software engineering is context-focused?
According to recent Gartner projections, by 2028, most companies will abandon merely assistive artificial intelligence in favor of outcome-focused workflows. And this paradigm shift will not just be a technological trend, but a survival necessity for businesses and for software engineering.
Still according to Gartner, humans will begin to supervise intelligent systems that perform tasks on their behalf. AI will cease to be just a layer of enhancement and efficiency and will begin to orchestrate business processes. Humans will stop merely executing and become “agent managers,” overseeing AI outcomes.
In this era of AI orchestration, control of business context represents economic power. And that's why we go further and believe that humans will become something closer to “agent tutors,” because they provide this context, monitor the process, and correct the course when necessary, end-to-end.
What is the impact of these predictions on software engineering?
Business context is becoming the control plane for AI execution. To achieve the results predicted by Gartner, AI needs to be fed this context through a structured collective consciousness flow, ensuring that no relevant knowledge is left out of the final solution.
Expanding this analysis to software engineering, by transforming the holistic business context into structured data, capable of being interpreted by AI, we ensure that the final solution truly generates value, not being generic, but specific to that pain point and need, a method we call integral design.
Structured methods of learning and innovation become the link between technology and impact. By combining universal modeling processes, creativity, and deep understanding of context, with LLMs and generative agents, what is sought is not indiscriminate automation, but applied hyperintelligence.
The impact of legacy systems on the future
Another Gartner prediction indicates that, by 2030, companies that add complementary AI to legacy applications, instead of redesigning them for automated execution, will face a margin compression of up to 80%.
As the market accelerates its digital transformation, legacy systems become both a basis for business continuity and a challenge for the adoption of new technologies essential for innovation. Other figures state that:
53% of companies in Brazil still struggle to integrate legacy systems with new technologies;
60% to 80% of IT budgets are allocated to maintaining these systems.
We know that legacy is not just old code; it is a barrier that limits scalability, increases operational risk, drains teams' innovation capacity, and does not keep pace with business speed. Given these figures and the risk involved for business continuity, it becomes fundamental for companies to consider legacy modernization as a priority.
For more reflections on the next waves of AI, read these two articles written by Romulo Cioffi, CIOO and Chief AI Officer at Squadra:
👉 AI Hangover: What's Left After the Hype Party
👉 The Agentic Twin as New Intelligent Organizational Infrastructure
