Tackling Enterprise AI Implementation Challenges
AI is the tech world's equivalent of that fancy new kitchen gadget everyone raves about. It's sleek, futuristic, and promises to transform how we do things.
Everyone's talking about it, investors are throwing money at it, and CEOs are convinced it's the key to unlocking the next level of success.
Many enterprises dive head first into AI implementation and then the challenges gradually unfold. This makes them withdraw from further talks about AI implementation. It's not that they don't see the potential, but the road to getting there seems long and bumpy.
To help you stay ahead I have curated some of the common challenges enterprises face during AI implementation from the least to most critical challenges. We will also cover how to tackle them to keep you ahead of the curve.
Challenges of AI implementation
Tool Selection and Integration
Most enterprises have a complex web of interconnected software and systems. A significant challenge enterprises face is selecting the right tools and seamlessly integrating them with these systems.
Technical skill Gap
Many enterprises have had their IT personnel for several years and most of them might not have the skills to implement AI projects successfully. This can be quite discouraging for enterprises. If the aim is to implement AI automation so that they can save cost, they wouldn’t want to spend an average of $100,000 per year to hire a new IT person who is AI proficient.
That raises another question, would training the existing team be a better idea? While that may sound more cost-effective, how long will it take to train the IT team to come up to speed with AI implementation and adoption?
Drowning IT Department
IT departments in most enterprises are already overwhelmed. They are constantly battling everyday issues, there's simply no bandwidth left to dedicate to the swift setup and maintenance of AI systems.
Slow Development Cycles
Unlike the sleek, agile startups, large enterprises are burdened by layers of bureaucracy, complex approval processes, and a culture that prioritizes stability over speed. Testing, iterating, and refining are crucial parts of any AI implementation project. However, in an enterprise setting, these steps get slowed down by long approval processes and the fear of failure. This creates a vicious cycle. The fear of failure leads to excessive caution, which in turn slows things down further. This prevents rapid adaptation, which is essential for successful AI implementation.
Data Quality and Availability
AI systems learn from the data they're fed. If that data is inaccurate, incomplete, or inconsistent, that is the kind of output you will get. Many enterprises have data scattered across different departments and systems, locked away in silos. This makes it difficult for AI projects to access the data they need to function effectively. It's like having all the ingredients for a meal locked in different cabinets – you can't cook anything until you get them all together.
The combination of poor data quality and limited accessibility creates a major roadblock for AI implementation for enterprises.
Organizing workflows
This is a critical challenge enterprises face in AI implementation. It’s one thing to have a nice lineup of AI tools but how do you orchestrate the flow to ensure maximum efficiency?
There are many complex processes involved in enterprise operations. Aligning roles and responsibilities within the AI workflow, and ensuring clear communication, is crucial but can be challenging in large organizations.
Regulatory Concerns
Regulatory bodies are struggling to keep pace with the speed of AI evolution. Without clear regulations, it's difficult for enterprises to know what's allowed and what's not. This can lead to delays, added costs, and a general sense of frustration for enterprises trying to implement AI responsibly.
How To Tackle These AI Implementation Challenges
IT Bottleneck
Focus on AI projects that can be implemented using pre-trained AI models. This reduces the need for in-house model development and training, freeing up valuable IT resources. No-code or drag-and-drop automation tools like Activepieces also allow you to connect various cloud services and automate workflows reducing pressure on IT teams.
Technical expertise
Many no-code platforms offer a vast library of pre-built components and templates specifically designed for AI tasks. These pre-built elements act as building blocks, allowing users to assemble workflows without needing to write code from scratch. This allows your team to leverage existing domain expertise while minimizing reliance on technical resources.
Slow development
Within each sprint, prioritize the development of the most critical features first. This ensures that even if the entire project isn't completed within a sprint, you'll still have a functional prototype with core functionalities. Drag-and-drop automation platforms automate tasks, such as data cleaning, model training, and visualization. This enables quick prototype creation, allowing you to test your AI implementations and gather early user feedback.
Data quality and accessibility
Define data quality metrics relevant to your specific AI project. This could include accuracy, completeness, consistency, and timeliness. Regularly monitor these metrics to identify and address any data quality issues. Track the origin, transformation, and movement of data throughout your organization. This helps you to understand how data is used. Utilize no-code platforms to automate the process of extracting data from various sources and integrating it into a central repository. This eliminates manual data manipulation and reduces the risk of errors. Tools like Activepieces and Boomi specialize in providing data integration solutions specifically for enterprise workflows.
Regulatory concerns
Create a roadmap outlining the steps you need to take to comply with relevant regulations. This roadmap should be updated regularly to reflect changes in the regulatory landscape. Automate your AI workflows by selecting no-code platforms that offer built-in features that can help you comply with regulations. These features may include data anonymization tools, audit trails, and user access controls.
Tool Selection and Integration
Evaluate your existing IT infrastructure and identify the systems your chosen AI tools need to integrate with. This includes enterprise resource planning (ERP) systems, customer relationship management (CRM) platforms, and data warehouses. Many no-code automation platforms offer pre-built connectors that simplify the process of connecting your AI tools with existing systems. These connectors act as bridges, allowing data to flow seamlessly between different applications.
Organizing workflows
To enjoy the efficiency of AI implementation you need to properly orchestrate the workflow to ensure seamless collaboration between your AI tool, existing systems, and various departments company-wide.Use drag-and-drop automation tools to create visual representations of workflows. Team members can monitor the status of tasks, identify bottlenecks, and collaborate effectively on resolving issues. A well-organized workflow is a dynamic entity. As your AI project evolves, you may need to adapt and refine your workflows. No-code platforms offer the flexibility to adjust workflows on the fly, ensuring they continue to meet the needs of your project.