Key Challenges for Businesses in Implementation AI Successfully

Artificial intelligence (AI) is showing promise in personal devices and targeted applications, but many businesses are finding that implementing and scaling AI across the enterprise is more challenging than anticipated. High costs, inadequate data, and a lack of skilled talent are major obstacles that businesses must navigate to successfully implement AI. Analyst and author David Linthicum warns that many companies may be facing a "downturn" in AI buying as they confront these complex issues and adjust expectations.

Linthicum emphasizes that businesses must address these key challenges upfront to realize AI’s potential benefits. Here are four main obstacles enterprises should prepare for:

1. Hitting the "Data Wall"

Many businesses discover that their data quality and organization are inadequate for successful AI implementation. According to Linthicum, the challenge isn’t the AI technology itself but rather the “bad data” many companies have accumulated over decades. Incomplete or disorganized data sets prevent AI from delivering accurate results and useful insights. Fixing this requires not only time and effort but also a significant investment—sometimes millions of dollars. For businesses looking to adopt AI, securing data quality and accessibility is a crucial, initial step. Linthicum explained, "There's no easy fix for this, you're going to have to stop what you're doing, loop back, and fix your data. For many of these organizations, that particular problem hasn't been addressed for the last 20 or 30 years....Those are tough conversations to have."

• What This Means for Businesses:

  Before launching AI initiatives, companies need to assess and clean up their data to ensure it’s accurate, organized, and ready for AI processing. Preparing for these "data readiness" investments can reduce friction later in AI adoption.

  For cleaning up data, businesses can start by conducting a data audit to identify inconsistencies, outdated information, and any missing data across their systems. This process often involves setting up data governance protocols to ensure data is standardized, complete, and properly categorized. Companies may also use tools for data deduplication (removing duplicates) and data validation (ensuring accuracy).

  By establishing clear data standards and conducting regular audits, businesses create a solid, reliable foundation that AI models can process effectively, leading to more accurate results.

2. Financial Sticker Shock

The high cost of AI implementation is a barrier for many organizations. Compared to earlier tech shifts like cloud computing or mobile apps, AI demands much larger investments. Linthicum notes that AI systems often require specialized processors, extensive training data, and a host of additional resources. This makes AI two to three times more expensive than traditional IT environments, which can be a hard sell for executives concerned about ROI.

• What This Means for Businesses:

  Executives must be ready to justify the costs of AI, not only in terms of initial setup but also ongoing maintenance, data tuning, and model updates. Understanding these expenses upfront helps build realistic expectations and ensures that budgets align with AI goals.

  Given the high upfront costs and ongoing expenses, ROI may not be immediately visible, so it's essential to set realistic expectations with company boards and stakeholders. By framing AI as a long-term investment, businesses can better manage expectations and gain support for phased, incremental returns.

3. Lack of Strategic Direction

Linthicum points out that many companies jump into AI projects without a clear strategic plan. A successful AI deployment depends on having a well-defined roadmap, which includes aligning data, identifying use cases, and understanding how AI will integrate with existing processes. Without this, companies risk fragmented AI initiatives that fail to deliver business value.

• What This Means for Businesses:

  Developing a strategic plan that aligns AI projects with business objectives is essential. Companies should identify high-impact use cases and ensure data readiness aligns with AI goals. Strategic planning will help businesses use AI as a competitive advantage, enhancing customer experience, improving efficiency, and a successful deployment.

4. Lack of Skills and Expertise

Successful AI implementation requires more than basic tech skills; it needs expertise in data science, model tuning, performance benchmarking, and ethical considerations specific to AI. Linthicum notes that AI development is far more complex than traditional software development, requiring specialized knowledge that is still in short supply. "And I'm not talking about the certification training around learning one cloud provider's AI platform," Linthicum said. Companies will need to invest in real training or hire experts to bridge this skills gap.

• What This Means for Businesses:

  Organizations should focus on upskilling existing teams or hiring talent with the right AI expertise. Building a team with a deep understanding of data science, AI ethics, and model optimization is crucial to maintaining competitive advantage and ensuring effective AI implementation.

Moving Beyond Chatbots: Implementing AI for Real Productivity Gains

For businesses aiming to use AI to its fullest potential, real implementation goes beyond chatbot functionalities like ChatGPT. To achieve meaningful productivity and efficiency improvements, companies need to develop AI systems that can automate repetitive, time-consuming tasks, such as data entry, report generation, marketing, and even customer support workflows. This requires integrating AI across various functions, adapting it to specific operational needs, and ensuring it can handle complex, routine tasks at scale.

• What This Means for Businesses:

  To realize AI's potential for streamlining operations, businesses should plan for AI applications that support or fully automate mundane tasks, freeing up employees to focus on higher-value work. This approach can lead to measurable productivity gains, driving efficiency across departments and enabling teams to leverage AI as a core operational tool, not just a supplementary feature.

Preparing for AI as a Strategic Differentiator

Linthicum highlights that businesses have no historic parallel to the resources required for successful AI adoption. Companies that take the time to clean their data, strategically plan use cases, secure the right talent, and align budgets with AI goals are more likely to succeed. When executed properly, AI can be a powerful differentiator, enabling businesses to provide better customer experiences, improve productivity, and lower costs.

To fully realize the benefits of AI, business leaders should prepare for these foundational steps. By addressing these challenges head-on, companies can create a sustainable AI strategy that positions them ahead of competitors and leverages AI as a valuable business tool.

Editor’s Note: This article was created by Alicia Shapiro, CMO of AiNews.com, with writing, image, and idea-generation support from ChatGPT, an AI assistant. However, the final perspective and editorial choices are solely Alicia Shapiro’s. Special thanks to ChatGPT for assistance with research and editorial support in crafting this article.