In 2025, the IT market experienced a volatile year, marked by the introduction of tariffs, an increase in H-1B visa fees, and an explosion in AI investment. The current fear is that subscription-based software companies may be replaced by AI solutions in the coming years. Although this fear is real and market conditions were uncertain at best, 2026 is still recording as a strong year. Major platforms such as Meta, OpenAI, and AWS, among others, are acquiring numerous companies and seem to favor the "buy instead of build" model in this era of accelerated development.

Information security solutions have become a highly sought-after commodity, with specialized services like DRaaS (Disaster Recovery as a Service) and MSS (Managed Security Services) at the forefront. Companies are showing distrust in delegating entirely to AI, especially when there are regulatory implications or direct customer service involved. In this context, companies must balance the cost of human consulting services—for interaction with internal or external clients or the confirmation of regulatory compliance—with the necessary investment in AI solutions.

The "black box" nature of many AI models may lead organizations to use established SaaS solutions for core functions and subsequently turn to AI to complement reporting and analysis. While AI will undoubtedly have a massive impact on the tech sector and the economy at large, niche software solutions and specialized services will continue to play a key role in the deployment, maintenance, and assurance of the reliability of the data that fuels these new systems and enterprises.

• Focus on AI-First Integration: Instead of viewing AI as a threat, embrace it as an accelerator. Design products with AI capabilities that augment—rather than replace—human value: assistants that boost productivity, generation of actionable insights, and secure automation of repetitive tasks.

• Differentiation through Specialization: Double down on vertical niches and use cases where industry-specific knowledge, business logic, and regulatory compliance are decisive. Generalist platforms will struggle to replicate this unique combination of expertise and trust.

• Prioritize Security, Explainability, and Compliance: Invest in interpretable models, AI audits, bias control, and data traceability. Offer regulatory guarantees and certifications as a competitive edge over "black box" solutions.

• Open Architecture and API-First Approach: Build interoperable products that allow clients to incorporate their own or external AI functions without losing control. APIs and open standards facilitate integration with larger platforms and mitigate the risk of obsolescence.

• Offer Hybrid Human-AI Solutions: Package services that combine automation with human oversight at critical junctures (customer service, regulatory decisions, quality reviews). This reduces client anxiety regarding full delegation to AI.

• Invest in Data and Observability: Ensure high quality, strict governance, and robust data pipelines. Trust in AI outputs depends on the integrity and traceability of the input data; provide monitoring and validation tools as part of the core product.

• Flexible Business Models and Measurable Value: Shift from fixed licensing to outcome-based or consumption-based models that demonstrate a clear ROI. Implement rapid Proofs of Concept (PoCs) and metrics that prove differential value against purely AI-based solutions.

• Talent Training and Retention: Upskill teams in MLOps, data engineering, and AI security. Foster hybrid roles (technical product owners and sector experts) capable of translating client needs into secure, high-utility functionalities.

• Continuous Innovation in UX and User Trust: Enhance the experience and transparency of AI usage (explaining limitations, providing controls, and offering override options). Trust and ease of use are decisive barriers to entry.