Companies looking to implement AI into their systems are facing doubts and challenges — from data silos and fragmentation to the lack of transparency in AI operations. As AI continues to evolve and become more central to business operations, having a platform that addresses these fundamental challenges becomes increasingly critical.
Artificial intelligence has shifted from promise to business reality.
But adoption is not simple. Many organizations still face the same obstacles: fragmented data, rigid integrations, unclear operations and costs that spiral out of control.
These challenges explain why so many AI projects stall before they ever deliver value.
The AI craze comes with a set of challenges: Is it hype or is it true?
AI adoption is no longer optional. Companies are under pressure to modernize, optimize efficiency, and stay competitive in a market that is increasingly AI-driven. Yet research from Gartner shows that 80% of AI projects fail due to data availability issues.
The problem isn’t AI itself, of course. It lies in the lack of structured, accessible data and the complexity of bringing existing systems together. Traditional approaches demand replatforming, costly migrations and years-long projects. That path creates more barriers than benefits.
But there's a different approach: addressing AI challenges at their source. Instead of forcing businesses to rebuild their infrastructure or rely on black-box solutions, modern solutions can provide a transparent, low-code integration platform with AI-augmented capabilities.
These platforms ensure data is AI-ready without demanding full system overhauls. It makes AI practical, manageable and scalable, while keeping businesses in control.
The 10 common AI challenges (and how to tackle them)
1. Data silos and fragmentation
The challenge: AI systems require access to comprehensive, integrated data from across the organization. However, most businesses have data scattered across multiple systems, like legacy ERPs, modern cloud applications, databases and file systems, that don't communicate effectively with each other.
How Frends solves it: Frends iPaaS acts as a universal connector, seamlessly integrating data from disparate sources in real-time. Rather than forcing businesses to migrate all their data to a single platform, Frends allows each system to remain the master of its own data while ensuring AI applications can access comprehensive, structured information from across the organization. This approach eliminates data silos without disrupting existing operations.
2. Complex integration development
The challenge: Traditional integration development requires extensive programming knowledge and can take months to complete. This creates bottlenecks that slow down AI implementation and limit the ability to adapt quickly to changing business needs.
How Frends solves it: Frends introduced LLM Code Generation, allowing users to describe desired processes in plain language and automatically generate the necessary code. Now, with the latest release, Frends 6.1, customer have access to the Intelligent AI Connector that can be embedded into any process and workflow. This democratizes integration development, enabling business users and junior developers to create complex integrations that previously required senior programming expertise.
3. Repetitive manual configuration tasks
The challenge: Setting up AI workflows often involves extensive manual configuration, data mapping and system connections — repetitive tasks that are time-consuming, error-prone, and require specialized knowledge.
How Frends solves it: Frends allows users to set up, manage, and map data integrations by simply describing the task in natural language. With the Intelligent AI Connector, every existing or new workflow can be enhanced by AI, with access logs documenting every step of the process. The platform automatically handles the technical configuration, reducing repetitive work and speeding up deployment across departments.
4. Lack of transparency in AI operations
The challenge: Many AI platforms operate as "black boxes," making it difficult for organizations to understand how decisions are made, audit processes or troubleshoot issues. This lack of transparency creates compliance risks and limits trust in AI systems.
How Frends solves it: Frends provides 100% full transparency on AI actions, with complete visibility into the underlying code and logic. Even when AI generates code or configurations, users can access and audit the complete C# codebase. This transparency ensures compliance, enables troubleshooting and builds confidence in AI-driven processes while maintaining the ease of low-code development.
5. Difficulty adapting to changing business rules
The challenge: Business requirements change frequently, but traditional AI implementations are often rigid and require extensive reprogramming to accommodate new rules or processes.
How Frends solves it: Frends incorporates Decision Model Notation (DMN) to create flexible, rule-based logic that can adapt to changing business needs without manual coding. Business users can update decision rules directly within the platform, allowing AI processes to evolve with the organization without requiring technical intervention or system downtime.
6. High development costs and extended timelines
The challenge: AI projects often exceed budget and timeline expectations due to the complexity of integration, the need for specialized skills, and the iterative nature of AI development.
How Frends solves it: By combining low-code development with AI capabilities, Frends dramatically reduces both development time and costs. Organizations can leverage existing models rather than building custom solutions, while the visual development environment eliminates much of the complexity associated with traditional AI integration projects. This approach enables faster time-to-market and more predictable project outcomes.
7. Operational monitoring and management complexity
The challenge: Once AI systems are deployed, they require continuous monitoring, performance optimization, and maintenance, tasks that often require specialized expertise and can become resource-intensive.
How Frends solves it: The system can automatically detect issues, optimize performance, and even resolve common problems without human intervention. This reduces the operational burden on IT teams while ensuring AI systems continue to perform optimally.
8. Documentation and knowledge management
The challenge: AI workflows and integrations often become complex over time, but maintaining accurate documentation is time-consuming and frequently neglected, leading to knowledge gaps and maintenance challenges.
How Frends solves it: LLM Documentation automatically generates comprehensive, up-to-date documentation for all processes and integrations. This ensures that knowledge is preserved, makes it easier for teams to understand and maintain AI workflows, and reduces the time spent on manual documentation tasks.
9. Skills gap and resource constraints
The challenge: Many organizations lack the specialized AI and integration expertise needed to implement and maintain AI systems effectively, creating resource constraints that limit AI adoption.
How Frends solves it: Frends' low-code approach and AI-powered development capabilities enable existing IT staff to work with AI systems without requiring extensive retraining. The platform's intuitive interface and natural language processing capabilities mean that business users can participate in AI development, reducing the dependency on specialized technical resources.
10. Data quality and preparation
The challenge: AI systems require high-quality, properly formatted data to function effectively. However, data preparation and cleansing are often time-consuming manual processes that can delay AI implementation.
How Frends solves it: Frends iPaaS includes built-in data validation, transformation and enrichment capabilities that automatically ensure data meets required standards as it flows between systems. This automated data preparation reduces manual effort while ensuring AI systems always work with clean, properly formatted information.
The Frends advantage: Making AI accessible and effective
What sets Frends apart in addressing these AI challenges is its comprehensive approach that combines:
- Transparency: Complete visibility into AI processes and underlying code
- Accessibility: Low-code development that democratizes AI implementation
- Flexibility: Hybrid deployment options that work with existing infrastructure
- Intelligence: AI capabilities that accelerate development and operations
- Integration: Seamless connectivity across all systems and data sources
Rather than requiring organizations to choose between AI capabilities and operational control, Frends provides both. This approach enables businesses to implement AI solutions quickly and cost-effectively while maintaining the transparency and control needed for enterprise environments.
The result is a platform that doesn't just solve individual AI challenges but creates a foundation for ongoing AI innovation and growth. Organizations using Frends can start with simple AI implementations and gradually expand their capabilities as they gain experience and confidence, all while maintaining the flexibility to adapt to changing business needs.
As AI continues to evolve and become more central to business operations, having a platform that addresses these fundamental challenges becomes increasingly critical. Frends iPaaS provides that foundation, enabling organizations to harness the power of AI without getting trapped by its traditional implementation challenges.
Learn all about Agentic AI and the future of integration
Watch now the webinar “From Automation to Autonomy: What Agentic AI means for the future of Integration” with Asmo Urpilainen, CTO at Frends: