AI investment priorities 2026: From experiments to measurable business value

Analysts at Forrester define 2026 as a “year of reckoning” and a time for “pragmatic reset.” This shift in sentiment is a direct response to the “overly enthusiastic AI ambitions” and operational missteps of 2025.
This year, the investment focus shifts exclusively to services that guarantee tangible, measurable, and safe business outcomes. Trust and customer value are placed above temporary hype.

The need for operational resilience and risk management

Nearly nine out of ten organizations use AI regularly, yet most companies remain in the early pilot stages. Almost two-thirds of respondents report that they have not begun scaling AI at the enterprise level. And only 39% of companies report any impact of AI on their EBIT (earnings before interest and taxes).
This low profitability, combined with growing regulatory pressure, is driving business leaders to demand guaranteed results. In 2026, the most in-demand services are not those focused on developing new, unproven models, but those that ensure reliable, large-scale AI operations. This includes, above all, operational management (MLOps-as-a-Service) and strategic consulting that help companies minimize legal and operational risks and win the “race for trust”.

Hyperautomation as the key to End-to-End efficiency

In 2026, the automation process evolves radically from simple routine tasks to hyperautomation. This dominant trend involves integrating Robotic Process Automation (RPA) with other intelligent technologies such as AI, Machine Learning (ML), process mining, and advanced analytics. These services aim to deliver scalability, adaptability, and intelligent decision-making to significantly enhance operational efficiency.
A critical component of hyperautomation is Intelligent Document Processing (IDP). Powered by OCR and NLP technologies, IDP enables AI to process and interpret complex, semi-structured, and unstructured data — such as invoices, contracts, and medical records. This removes the barrier that previously limited RPA to simple, strictly defined tasks, paving the way for automating entire end-to-end (E2E) workflows without the need for constant human intervention.

The shift to agentic systems and E2E automation

AI agents are gaining the ability to plan, make complex decisions, and execute multistep workflows. Enhanced by LLMs, these systems can handle errors and business logic. Demand for this service is highest in industries where speed and data analysis are critical. This includes the financial sector (advanced fraud detection systems, underwriting), manufacturing (supply chain management automation), and healthcare.

Democratization and regulatory demand

For small and medium-sized enterprises (SMEs), which often face high initial investment barriers, demand is growing for RPA as a Service (RaaS). This subscription-based, on-demand model allows organizations to scale automation flexibly and reduces the need for significant infrastructure investment. Thanks to RaaS and Low-Code/No-Code platforms, the benefits of intelligent automation are becoming accessible to a wider range of organizations.

The increasing autonomy of agentic systems in mission-critical transactions, especially in financial services, raises concerns about reliability and auditability. This inevitably makes the demand for hyperautomation services inseparable from built-in governance requirements.

Additionally, consulting firms provide tailored services that help organizations select optimal AI solutions, ensure proper integration, and guarantee ROI.

Scaling LLM-based chatbots

Customer service has become the number one priority for generative AI adoption among CEOs. LLMs and generative AI are transforming traditional rule-based chatbots, giving them the ability to conduct natural, contextual, and human-like conversations.

This technological leap delivers tangible ROI through mass automation. Automating routine tasks, such as creating FAQs, can reduce daily human workload by an average of one hour.

Critical demand for AI operations optimization services (AI Talent)

As AI is integrated, organizations will need to restructure their teams. Forrester predicts that 30% of enterprises will create parallel AI functions that mirror human roles. This creates demand for new categories of services, including managers for “training and coaching” AI agents, as well as specialists to resolve failures (unblock AI when it falters).

Regulatory imperative for chatbot transparency

The demand for conversational AI services is directly linked to the need for regulatory compliance. The EU AI Act’s Transparency Rules take effect in August 2026.

• Conversational AI compliance: Chatbot integration services must provide mechanisms that clearly inform users when they are interacting with a machine (disclosure obligation).
  
• Generative content labeling: Providers of generative AI, especially for video and text, must ensure that their content is identifiable and, in the case of deepfakes or public-facing text, clearly and visibly labeled.

Multimodality as the new content standard

In 2026, generative content services will be dominated by multimodal AI, capable of working simultaneously with text, images, video, and audio.

The key commercial service in highest demand is orchestrating complex projects, such as generating a full video advertisement from a single prompt. This dramatically lowers production barriers, reduces time-to-market from weeks to hours, and drives explosive growth in high-quality, personalized content. Corporate applications include:

• Media and entertainment: Automated scriptwriting, storyboarding, music composition, and generative video pipelines. For example, Netflix uses generative models to automate previsualization.
  
• Marketing and sales: Using multimodal AI to analyze consumer behavior and sentiment to create hyper-personalized products and targeted marketing strategies.

Specialized LLM integration services and industry solutions

A successful LLM integrator in 2026 must possess not only technical expertise but also deep industry knowledge.

The most in-demand services are custom AI solutions in key sectors where AI promises transformation:

• Finance: Automation of fraud detection, risk analysis, and investment analysis.
  
• Healthcare: Early disease detection, personalized treatment plans, and accelerated drug discovery.
  
• Manufacturing: Monitoring production processes from raw materials to finished products with minimal human oversight.

“Build vs. Buy” strategy and proprietary data

One of the most important tasks of AI consulting is helping enterprises navigate the “buy vs. build” strategy. This choice is no longer binary and largely depends on the availability of proprietary data, which remains the most decisive factor in AI implementation.

Organizations with exclusive, high-quality data (e.g., in insurance or industrial automation) invest in Custom AI Development as a strategic asset that creates and strengthens competitive advantages. On the other hand, companies with limited or standardized data benefit more from Model-as-a-Service (MaaS) integration, which enables fast, measurable results at lower cost.

Thus, consulting demand is twofold: supporting the creation of “competitive moats” through custom development and enabling rapid, cost-effective deployment via MaaS.

MLOps: a critical factor for scaling

MLOps (Machine Learning Operations) is the top priority for enterprises entering 2026. Gartner notes that nearly 80% of ML projects fail to move beyond the experimental phase due to the lack of proper infrastructure for deployment, monitoring, and model reliability. As ML systems become more complex and make critical decisions in finance, healthcare, and manufacturing, companies urgently need robust MLOps platforms.

MLOps-as-a-Service provides the structure required to build, deploy, and maintain ML models with consistency and reliability. Key demand in 2026 will focus on:

• Explainable AI (XAI): Demand for systems capable of transparent decision-making is growing, driven by auditors, regulators, and business stakeholders. MLOps services must integrate XAI as a core production capability to maintain model trust and detect harmful drift.
  
• Organizational governance: With increasing regulatory pressure and risks, MLOps becomes essential for ensuring automated, auditable policy enforcement, enabling teams to bring AI systems to market faster while remaining compliant.

Model-as-a-Service (MaaS) as the new AI consumption economy

Model-as-a-Service (MaaS) represents a key shift in how AI is consumed, lowering entry barriers for businesses of all sizes. MaaS provides cloud access to pre-trained ML models via API with flexible pay-as-you-go pricing.

The benefits of MaaS drive high demand, as this model:

• Speeds Time-to-Market: Eliminates the labor-intensive and resource-heavy processes of developing and training models from scratch.
  
• Reduces complexity and costs: Frees developers from managing hosting, scaling, and versioning.
  
• Democratizes AI: Makes AI integration accessible, particularly benefiting midmarket companies seeking resource optimization.

AI energy management

The growing demand for AI and the deployment of large models places significant strain on energy systems, leading to higher energy bills and potential resource shortages. This creates a new strategic demand for AI Sustainability Consulting.

Companies aiming to reduce costs and increase resilience require services that help integrate sustainability metrics (e.g., Scope 3 indirect carbon emissions) into AI architecture. This includes practices such as carbon scheduling and the development of protocols that determine when and how to use AI to optimize energy consumption.

What do we have in summary?

In 2026, there will be a clear shift from unmanaged experimentation to investments focused on measurable ROI, risk reduction, and operational resilience. The most in-demand AI services are those that not only offer new capabilities but also guarantee reliability and regulatory compliance.

Key areas of demand are focused on:

• Operational efficiency through hyperautomation (RaaS and Agentic RPA), integrating IDP for end-to-end automation.
  
• Customer experience (CX) transformation through LLM chatbots, where success requires significant investment in organizational adaptation and data management.
  
• Fundamental reliability through MaaS and MLOps, which are critical for moving ML models from pilots to production and ensuring their transparency (XAI).
  
• Mandatory compliance driven by the EU AI Act’s Transparency Rules taking effect in August 2026, creating immediate demand for generative content labeling services and disclosure functionality for chatbots.