2026 AI Tech Predictions: The Gap Between AI Experimenters Vs. Those Who Operationalize It Is Growing; Hesitant Technologists Risk Being Left Behind

By Assaf Melochna, President & Co-Founder at Aquant

Gartner reports that 77% of service and support leaders feel executive pressure to deploy AI and 75% report increased AI budgets. Organizations in service, support, and operations began experimenting with intelligent agents, early agentic systems, and workflow automation to reduce costs and improve customer experience. Yet for many, adoption choked as pilots were slow to scale, tools were poorly integrated, and leaders underestimated the organizational changes required to convert AI enthusiasm into measurable ROI. As a recent MIT study found, only 5% of custom GenAI tools succeeded, while the remaining 95% failed to deliver measurable results. 

• A successful business will value AI not as a tech project, but as a strategic investment.
• The gap between organizations merely experimenting with AI and those successfully operationalizing it will widen, leaving tentative technologists in the dust. 
• 2026 will be the year AI finally proves its business value. The companies that thrive will be the ones that treat AI as infrastructure, not novelty.

Looking ahead to 2026, companies will need to move beyond the novelty phase and toward holistic, operationalized AI integration. The divide between organizations merely experimenting with AI and those embedding it effectively will widen, leaving tentative adopters behind. Reflecting this shift, Gartner predicts that the typical leader plans to add five new full-time-equivalent roles in the next 12 months to manage and scale these AI investments.

The businesses that succeed will be supported by the right data foundation, strong change leadership, and a commitment to measurable outcomes. In short, a successful business will value AI not as a tech project, but as a strategic investment. Do this and you’ll move from pilot to profit.

Drawing from our past decade of experience working with top equipment manufacturers and service providers and helping them scale AI across their operations l predict these six major shifts will define the next year of AI adoption.

1. Retrieval-Augmented Conversation (RAC) Will Become the New Standard for Knowledge and Guidance

Today’s AI assistants often function like advanced search engines: they pull information, generate a single answer, and stop there. In 2026, that won’t be enough. 

Retrieval-Augmented Conversation (RAC), AI that holds multi-step conversations, asks clarifying questions, and guides users through tasks, will represent the next major leap in AI development. RAC systems synthesize data across multiple sources in real time and remain engaged until the issue is resolved.

For service organizations, RAC will transform performance: contact center agents will resolve complex issues without escalating; customers will receive more accurate answers without navigating convoluted portals; and decision-makers will gain higher-quality data that fuels continuous improvement. RAC will shift AI from information provider to operational teammate, making expertise accessible to every individual, regardless of skill level.

2. Companies Will Consolidate AI Tools to Eliminate Chaos and Strengthen ROI

Over the last two years, organizations scrambled to acquire many task-specific AI tools: tools for data management, for call summarization, for analytics, and more. The result is overlapping capabilities, integration gaps, and a lack of measurable outcomes.

In 2026, this “AI tool sprawl” will come to an end. With ongoing budget pressure and rising expectations, companies will consolidate toward a smaller number of powerful platforms that deliver meaningful results. The shift will move organizations from sporadically deploying AI in hopes of a quick fix to deploying AI with purpose and intention. 

Similarly, platforms that offer robust, frictionless integrations will increasingly outpace those that don’t. Companies are prioritizing solutions that allow every system to connect and communicate, creating a unified flow of data across the business.

3. Voice Will Become the Interface of Service Work

Voice AI will evolve from an assistive feature to a primary interface, especially for field and frontline teams. The next wave of voice systems will go far beyond transcription, they will understand intent, tone, and context in real time, acting more like a knowledgeable colleague than a digital assistant.

For technicians, this means hands-free access to knowledge and guided troubleshooting without switching tools. For contact centers, it means shorter handle times and more personalized interactions. For customers, voice-based experiences will feel more natural, more human, and more resolution-driven. 

4. AI Agents Will Become Self-Building

In 2025, agentic AI went mainstream, but most solutions still required significant engineering lift: specialized teams, custom code, and months of handholding just to get the first workflow running. 

Next-generation AI agents will be created through low-code and no-code platforms designed for business users, not technical experts. Instead of relying on engineering teams, organizations will build, deploy, and iterate agents themselves. As companies gain the ability to create and maintain agents internally, deployments will become faster, cheaper, and more aligned with real operational needs.

5. Hiring Criteria Will Prioritize Digital Fluency Over Deep Domain Expertise

In 2026, AI will act as a skill equalizer, rapidly narrowing the gap between novice and expert performance. Intelligent copilots, voice-driven guidance, and conversational RAC systems will provide real-time coaching, troubleshooting guidance, and step-by-step task support.

Because AI will operationalize institutional knowledge, organizations will begin hiring based on learning agility, communication skills, and digital fluency rather than decades of hands-on experience. This shift represents one of the most consequential and least discussed impacts of AI: redefining what expertise looks like and who has access to it.

6. The Shift from Generic AI to Domain Intelligence

Generic AI platforms can’t interpret the specialized data trapped in service logs, claims narratives, schematic drawings, and compliance notes, missing critical insights buried in service-specific language and context. By 2026, winning organizations will deploy domain intelligence layers that understand each function’s unique expertise, transforming siloed knowledge into enterprise-wide insights where service data informs product design and claim patterns strengthen fraud detection. 

Companies embracing this shift will see 30-40% operational cost reductions and dramatically improved decision accuracy. Those sticking with generic AI will remain data-rich but insight-poor, watching competitors turn identical datasets into competitive advantages through specialized intelligence.

The Future of AI

2026 will be the year AI finally proves its business value. The companies that thrive will be the ones that treat AI as infrastructure, not novelty. Service organizations that embrace this integrative mindset will not have to worry about keeping pace, they’ll be at the front, setting it. 

About Assaf Melochna
Assaf Melochna’s is the President and Co-Founder of Aquant. He is an expert in service and has business and technical expertise in enterprise software. Assaf started Aquant with the vision of helping service companies transform the way they deliver service through data and AI.