Jitterbit’s Top Ten AI & Automation Predictions for 2026

By Bill Conner, Jitterbit President & CEO

Existing and emerging AI technologies continued to be a primary focus for us throughout 2025, bringing the launch of several new AI assistants and a suite of powerful new AI agents to improve automation, integration and orchestration.

This year, we’re continuing our dedication to helping customers leverage the latest AI developments and overcome emerging headwinds. Here are the top ten trends and shifts that we predict could be coming down the pike in 2026:

1. Integration Becomes Foundation of Enterprise AI Stack

As AI agents proliferate, the biggest barrier to enterprise adoption will no longer be model quality — it will be connectivity. AI systems create value only when they can read, write, update and trigger actions across business applications. In other words, AI without integration produces insights; AI with integration produces outcomes.

As a result, integration will become the foundational layer of the AI era, much like cloud infrastructure was in the 2010s.

Enterprises will demand platforms that provide:

• Secure access to systems through APIs, events and data pipelines
• Consistent authentication and authorization across agents
• Full governance and observability for AI-driven actions
• The ability to orchestrate multi-step processes end to end

In the AI-driven enterprise, middleware is no longer just plumbing — it has become the core of the operating model.

2. NLP Becomes the Primary Interface for Building Simple AI Agents

In 2026, NLP (Natural Language Programming) will sit alongside traditional low-code tools for everyday automation and agent-building. Instead of dragging boxes or writing script-style logic, business users will simply describe outcomes: “When a new lead comes in, qualify it, enrich it and notify the sales rep.”

AI agents will interpret intent, assemble workflows and adapt automatically as systems change. Low-code will still drive complex orchestration, but NLP becomes the default interface for routine tasks.

It’s the same shift we saw when search overtook directories and touch replaced physical buttons: NLP is becoming the new abstraction layer for automation.

3. The Market Value of Simple “Task Bots’ Collapses to Near Zero

Simple automation — such as moving files, sending notifications or syncing rows between apps — won’t justify its own market category anymore. Simple bots will fade quietly into the background.

Every major platform, from Microsoft and Google to Salesforce, ServiceNow and Slack, will bundle these AI-driven tasks directly into their core products. As a result, ‘microbots’ get fully commoditized, and vendors built on basic workflow automation lose pricing power.

The real winners will be the platforms that offer rich orchestration instead of single-task bots, deliver enterprise-grade governance and observability, and integrate deeply into business systems rather than just gluing apps together.

The shift mirrors how email hosting evolved from a paid service to an expected feature. Gartner supports this with their prediction that by 2026, 80% of simple workflow automation will be bundled features, not products.

4. Hardware Competency Re-emerges as Strategic Advantage

We’re entering a new era where hardware matters again. In the ’90s, competitive advantage came from breakthroughs in CPUs, storage and networking. In the 2000s and 2010s, the center of gravity swung to software such as SaaS, cloud, APIs and mobile. Now AI is pulling the industry back toward hardware dominance.

Countries are racing to secure GPUs as if they were strategic assets. Enterprises need specialized chips for inference and fine-tuning. Custom accelerators like NPUs, TPUs and ASICs are becoming real differentiators.

Even supply chain resilience is now a board-level concern. Governments are pouring billions into this shift: $52 billion through the US CHIPS Act, $47 billion through the EU Chips Act, and more than $140 billion in semiconductor subsidies in China. Training and running AI models at scale consumes extraordinary power and cooling, and datacenter expansion is hitting geographic, regulatory and energy-availability limits.

Electricity is becoming a key determinant of AI capability, and regions with surplus renewable energy are emerging as AI hubs. Data center operators now compete for limited transformer and grid capacity, and companies are beginning to optimize AI usage based on cost-aware scheduling, not just performance.

Throughout 2026, semiconductor constraints may continue to limit the availability and depreciation of AI hardware, but the real divide between leaders and laggards will be defined by connectivity rather than raw compute power. Organizations that can turn AI potential into tangible business value through seamless integration, APIs and automation will outpace those relying solely on processing capacity.

With manufacturing bottlenecks likely to persist, prioritizing practical, scalable solutions over hype will be essential. Even the most advanced AI models will be constrained by the systems feeding them, making architecture, connectivity, and integrated workflows the critical factors that determine which companies achieve lasting transformation and competitive advantage.

AI workloads are growing 10x every 18 months, far outpacing Moore’s Law — and owning or accessing the right compute, at the right moment, will be as critical as owning IP. Hardware capacity becomes the new cloud lock-in, and every major company will need the ability to understand, procure and optimize AI hardware.

5. Data Centers and Electricity Become the Real Bottlenecks

As AI adoption accelerates, the constraint shifts from algorithms to infrastructure economics. Training and running AI models at scale consumes extraordinary power and cooling, and datacenter expansion is hitting geographic, regulatory and energy-availability limits.

By 2028, electricity will become a key determinant of AI capability. As regions with surplus renewable energy become AI hubs, datacenter operators will compete for limited transformer and grid capacity, and companies will begin optimizing AI usage based on cost-aware scheduling, not just performance.

The next wave of innovation will be as much about energy management and sustainable infrastructure as it is about model architecture. Training a single GPT-4 scale model consumed an estimated 5-7 gigawatt-hours, enough to power 1,000 U.S. homes for a year.

6. Security and ROI Become Top Priorities for Enterprise AI

After the initial AI hype, enterprises will shift from experimentation to operationalization, and the conversation will change. Security concerns will take center stage: data leakage risks from LLMs, shadow AI usage by employees, legal exposure from unclear model provenance, and demands for vendor transparency around training data and model lineage. CISOs will insist on robust AI governance frameworks before allowing widespread adoption.

ROI will become the decisive factor. Companies will no longer green-light AI projects simply because they “feel innovative.” Success will require measurable productivity gains, operational savings or clear revenue outcomes.

AI will succeed only when tied to reduced process cycle times, lower labor costs for repetitive tasks, improved customer satisfaction and tangible revenue lift. As a result, AI as a whole will finally shift from “cool demos” to delivering quantifiable business value.

7. Natural Language Becomes the Next Great User Interface

Beyond programming agents, NL (natural language) is becoming the universal interface layer across applications. Where the last decade was mobile-first and API-first, the next decade is NL-first. Employees and citizens alike will interact with systems conversationally.

They might ask a platform to create dashboards showing monthly churn by region, draft contract amendments from last year’s templates, or build approval workflows for new vendor requests. This interface shift democratizes solution creation.

People who have never coded or used low-code tools will suddenly be able to build meaningful automations, apps and analyses simply by describing them. Organizations that embrace this democratization will see a surge of bottom-up innovation.

8. AI Governance Becomes Mandatory for Enterprise Deployment

As regulatory frameworks mature and AI adoption accelerates, enterprises will no longer treat governance as optional. Organizations will formalize policies and systems to ensure AI is deployed safely, ethically and compliantly — and this shift will be driven by rising concerns around data privacy, model transparency, decision accountability and regulatory exposure.

Governance frameworks will:

• monitor and audit AI actions across systems
• control access to sensitive data and prompts
• track model lineage, training sources, and version history
• ensure compliance with global and industry-specific regulations
• establish oversight for high-risk or high-impact use cases

AI governance becomes a prerequisite for scaling AI across the enterprise, not just to reduce risk, but to build organizational trust and unlock broader adoption.

9. Human-in-the-Loop Becomes Default Model for Enterprise AI Workflows

Even as AI systems grow more capable, enterprises will adopt hybrid workflows where humans remain central to oversight and decision-making. HITL (Human-in-the-Loop) processes will become standard for reviewing critical outputs, ensuring compliance, validating correctness and maintaining organizational trust.

HITL will be embedded in a variety of workflows:

• customer and employee communications
• procurement, legal and financial approvals
• document generation
• contract operations
• complex decision-making with regulatory consequences
• AI agents performing automated actions in core systems

This approach enables enterprises to deploy AI faster and more confidently, ensuring AI augments human expertise rather than replacing it. It also delivers efficiency without sacrificing trust, control, or accountability.

10. Small Language Models Become Essential for Enterprise AI

As enterprises pursue efficiency, privacy, and specialized capabilities, SLMs (small language models) will gain momentum. These compact models will be optimized for specific tasks, domains or industries — offering faster inference, lower cost and greater controllability than their massive counterparts.

SLMs will be especially valuable for

• On-device and edge applications
• Highly regulated industries requiring local processing
• Workflows that demand predictable performance and minimal hallucination
• Fine-tuned domain expertise in areas like legal, medical or financial services
• Organizations with limited compute budgets

Rather than relying on one gigantic model to handle everything, enterprises will adopt model portfolios: a combination of general-purpose LLMs and highly tuned SLMs that deliver targeted value with lower cost and risk. This represents a clear shift from “bigger is better” to “right-sized intelligence for the task.”