AI and automation in IT consulting: transforming business operations in 2025

AIOps: AI-powered IT operations

The AIOps market is expected to reach approximately $16 to $18 billion in 2025 and is projected to grow to over $36 billion by 2030, driven by enterprises struggling with complex hybrid cloud environments, escalating observability data volumes, and the pressure to reduce operating costs while improving service resilience. AIOps applies machine learning and advanced analytics to IT operations data, automating the detection, diagnosis, and resolution of infrastructure and application issues that would overwhelm human operators.

At its core, AIOps platforms ingest data from monitoring tools, log management systems, and ticketing platforms to identify patterns, correlate events across systems, and surface actionable insights. Rather than reacting to individual alerts, AIOps identifies the root cause of cascading failures, predicts potential outages before they occur, and can trigger automated remediation actions. According to OpsRamp, 87% of organizations report that their AIOps tools have successfully delivered value, reflecting growing confidence in these platforms.

Predictive maintenance represents one of the most impactful AIOps use cases. By analyzing historical failure patterns, system metrics, and environmental data, AI models can predict hardware failures, capacity exhaustion, and performance degradation days or weeks in advance. This shifts IT operations from a reactive break-fix model to a proactive prevention model, significantly reducing downtime and extending infrastructure lifespan. For businesses relying on physical infrastructure, whether data centers, manufacturing equipment, or retail systems, predictive maintenance alone can justify the investment in AIOps.

Implementing AIOps requires a mature data foundation. Organizations need comprehensive monitoring coverage across their infrastructure, well-structured log aggregation, and clean CMDB data. Starting small with a specific use case, such as automated alert correlation or capacity forecasting, is more effective than attempting a broad AIOps deployment. As the system learns from your environment and your team builds confidence in its recommendations, you can progressively expand its scope.

Automated security: SOAR platforms and beyond

The security orchestration, automation, and response (SOAR) market reached approximately $1.8 billion in 2025 and is projected to grow to $5 billion by 2035, reflecting the critical role automation plays in modern cybersecurity. SMEs, holding 51.6% of the SOAR market share, are the fastest-growing adopters, driven by the reality that smaller organizations often lack dedicated security teams and must rely on automation to manage investigations, contextualize alerts, and execute response actions at the speed that modern threats demand.

SOAR platforms integrate with existing security tools, including SIEM systems, endpoint detection and response (EDR) solutions, threat intelligence feeds, and vulnerability scanners, to orchestrate coordinated response workflows. When a potential threat is detected, the SOAR platform can automatically enrich the alert with threat intelligence data, correlate it with other recent events, assess its severity, and execute predefined response playbooks. This might include isolating a compromised endpoint, blocking a malicious IP address, creating a ticket for the security team, and notifying affected stakeholders, all within seconds of detection.

Beyond SOAR, AI is enhancing security across the entire threat lifecycle. Machine learning models in network detection and response (NDR) platforms establish baselines of normal behavior and flag anomalies that signature-based tools would miss. AI-powered email security solutions detect sophisticated phishing attacks by analyzing writing patterns, sender behavior, and contextual inconsistencies. And user and entity behavior analytics (UEBA) platforms identify insider threats and compromised accounts by detecting deviations from established behavioral patterns.

For organizations in Belgium and across the EU, automated security is becoming not just a best practice but a regulatory expectation. The NIS2 directive requires organizations in critical sectors to implement state-of-the-art security measures, and regulators increasingly expect automated detection and response capabilities as part of a mature security posture. Cloud-based SOAR platforms offer the scalability and lower implementation complexity that makes these capabilities accessible even to mid-sized organizations.

Intelligent process automation vs. traditional RPA

While robotic process automation (RPA) transformed business operations by automating repetitive, rule-based tasks with an average ROI payback of less than 12 months, intelligent process automation (IPA) represents the next evolutionary step. IPA combines RPA with artificial intelligence, machine learning, natural language processing, and computer vision to handle complex, judgment-based processes that traditional RPA cannot address. Businesses adopting IPA achieve twice the productivity gains of those using only RPA, with operational cost reductions of 25 to 50%.

The key distinction is in how each technology handles complexity. RPA excels at structured, predictable tasks like invoice processing, data migration, and report generation. When a process involves unstructured data, such as reading and interpreting emails, contracts, or handwritten forms, or requires judgment calls, such as assessing credit risk or categorizing support tickets, RPA falls short. IPA fills this gap by adding cognitive capabilities: NLP to understand human language, ML to learn from data and improve over time, and computer vision to process visual information.

Practical IPA use cases in enterprise environments include intelligent document processing that extracts and classifies information from varied document formats, customer service automation that understands intent and context rather than just matching keywords, predictive analytics for supply chain optimization, and automated compliance monitoring that interprets regulatory changes and assesses their impact on business processes. By 2025, hyperautomation is expected to impact one-fifth of all business processes, combining RPA, AI, ML, and business process management.

The rise of no-code and low-code automation platforms is democratizing access to these capabilities. Business users can now build automation workflows that incorporate AI capabilities without extensive programming knowledge, accelerating adoption while reducing implementation costs. For IT consulting firms, this shifts the focus from building custom automation solutions to advising on platform selection, governance frameworks, and change management strategies that ensure automation delivers sustainable value.

LLMs in Business and the EU AI Act

Large language models have rapidly moved from experimental technology to core business infrastructure. The use of generative AI in business doubled to 67% in 2025, with enterprise applications spanning customer support automation, knowledge management, software development, content generation, and document processing. Companies like JPMorgan Chase use LLMs for fraud detection, Walmart for inventory optimization, and UnitedHealth for claims automation. Nearly 90% of technology leaders plan to increase their AI budgets, driven by the rise of agentic AI, and by 2026, 40% of enterprise applications are expected to feature embedded AI agents.

For European businesses, the EU AI Act adds a critical compliance dimension to AI adoption. The regulation entered into force on August 1, 2024, with a phased implementation timeline. Since February 2, 2025, prohibited AI practices are enforced and all organizations must ensure AI literacy among their staff. From August 2, 2025, governance provisions for general-purpose AI models take effect. The full regulation becomes applicable on August 2, 2026, with penalties reaching up to 35 million euros or 7% of global annual turnover for violations of prohibited practices.

The Act adopts a risk-based framework with four categories. Prohibited practices include subliminal manipulation, exploitation of vulnerabilities, social scoring, and individual criminal profiling. High-risk AI systems, such as those used in recruitment, healthcare, critical infrastructure, and law enforcement, face extensive documentation, testing, transparency, and human oversight requirements. Limited-risk systems require transparency measures like informing users they are interacting with AI. And minimal-risk systems, which cover most business applications, face no specific obligations but are encouraged to adopt voluntary codes of conduct.

Organizations should begin by conducting an AI inventory to catalog all AI systems in use or development, classifying each by risk level. Establish an AI governance framework that defines roles, responsibilities, and processes for AI oversight. Ensure AI literacy training for all staff involved in AI deployment or decision-making. For high-risk applications, implement the required conformity assessments, technical documentation, and human oversight mechanisms. Working with consultants who understand both the technical and regulatory landscape is essential for navigating these requirements efficiently.

A practical AI adoption roadmap

Successful AI adoption in business follows a structured approach that balances ambition with pragmatism. The roadmap begins with an assessment phase where you identify high-impact, low-risk use cases that can demonstrate value quickly. Common starting points include automated IT monitoring and alerting, document processing and data extraction, customer service chatbots for routine inquiries, and internal knowledge base search using retrieval-augmented generation. These use cases typically deliver measurable ROI within three to six months.

The foundation phase focuses on building the data infrastructure and governance frameworks that enterprise AI requires. This includes establishing data quality standards, implementing data cataloging and lineage tracking, defining AI ethics guidelines, and creating the technical infrastructure for model development and deployment. Organizations that skip this phase often find that their AI initiatives stall when they cannot access clean, well-governed data at scale.

The scaling phase expands successful pilots into production systems and introduces more sophisticated AI capabilities. This might include deploying AIOps across your infrastructure, implementing intelligent automation for complex business processes, or integrating LLM-powered capabilities into customer-facing applications. At this stage, ensure you have robust monitoring for model performance, bias detection, and drift, along with clear processes for model updates and incident response.

Throughout all phases, maintain a responsible AI framework that addresses transparency, fairness, accountability, and privacy. For EU-based organizations, this framework should be explicitly aligned with the EU AI Act requirements. Regular audits of AI systems, documentation of decision-making processes, and channels for stakeholder feedback are essential components. Remember that AI adoption is a continuous journey, not a destination. The organizations that succeed are those that build learning cultures, invest in ongoing training, and remain adaptable as the technology and regulatory landscape continue to evolve.

How Shady AS can help

At Shady AS SRL in Brussels, we help organizations navigate the rapidly evolving AI landscape with confidence. Our consulting services span the full AI adoption journey, from identifying high-value use cases and assessing EU AI Act compliance requirements, to implementing AIOps platforms, intelligent automation workflows, and LLM-powered business applications. We combine deep technical expertise with practical understanding of European regulatory requirements to ensure your AI initiatives are both innovative and compliant.

Whether you are exploring AI for the first time or looking to scale existing initiatives across your organization, our Brussels-based team provides the strategic guidance and hands-on implementation support you need. We understand the unique challenges facing Belgian and European businesses, from multilingual requirements to GDPR and AI Act compliance, and we tailor our approach to your specific industry and operational context. Contact Shady AS SRL today to schedule an AI readiness assessment and discover how artificial intelligence can transform your operations while maintaining the governance and compliance standards your business demands.