What Is Holding Businesses Back from AI Adoption?

Your company spent two million dollars on an AI project. The pilot looked strong. The demo worked. Then the results flatlined. You are not alone!

Most companies face AI adoption challenges. They see very little or almost no measurable return from their AI adoptions. Failure to reach scale leads to money down the drain.

The problem is not the model. The problem is people, process, and strategy. Although these issues are fixable. Let’s see how!

Why AI Adoption Is Essential

AI drives speed, accuracy, and better decisions. It removes repetitive work and frees your teams to focus on high-value tasks. Most companies adopting AI see a significant change in operational efficiency.

However, when companies make large shifts rapidly, they face AI adoption challenges. Pilot projects work, but scaling fails. Teams push back, and the systems block progress. Skills fall short. Data is unreliable to say the least. These and many such reasons are why companies struggle with AI adoption. Here’s more on the common challenges in AI adoption for businesses.

Barriers To Enterprise AI Implementation

1.Workforce Readiness

What is the role of workforce preparedness in AI adoption? Most teams do not have the skills to run AI at scale. Half of all businesses cite a lack of skilled talent as their top blocker. According to Statista, in 2025, the biggest barriers to AI adoption were the lack of skilled professionals, cited by 50% of businesses, a lack of vision among managers and leaders, cited by 43%, followed by the high costs of AI products and services at 29%.

Skills shortages show up in three ways:

1. You try to hire: The talent pool is small and expensive.
2. You try to upskill: Training takes time.
3. You rely on a few experts: If they leave, your project fails.

The fix is simple. Build a blended model. Hire where needed. When training your teams, create a culture of learning. Spread knowledge across teams.

2. ROI Uncertainty

Leadership wants clear returns. Few companies define them well. Many teams track with no clear outcome. They guess at goals, and they use vague metrics. Some AI projects take time to show impact. Early benefits are small and indirect. Many leaders expect fast results and lose interest before the project matures.

To improve results, companies must define one primary outcome, set clear timelines, and track progress with simple metrics.

3. AI Adoption Issues in Legacy Systems

How do legacy systems impact AI implementation? Many companies face integration issues. Old systems store data in incompatible formats. Since data lives in silos, infrastructure is slow. APIs fail to support real-time data. Integration becomes expensive. Your team struggles to connect modern tools with outdated systems.

The fix is a staged approach —modernize in small steps, consolidate data, and clean your core systems before scaling AI.

4.Lack of Clear Objectives

Many leaders approve AI projects without a clear goal. Teams pick use cases that sound interesting but solve no real business problem. Without clear objectives, the project drifts. No one knows what success means. Results are hard to measure.

The better way—start with one business problem, slow response times. Set a specific goal and develop around it.

5. Concerns Around Data Security

Executives worry about data exposure. These concerns are valid. Poor data governance creates risk. Companies often do not know where data lives or who uses it. Data quality issues cost the US economy over three trillion dollars a year.
Regulated industries face higher standards. One mistake creates legal and financial risk.

The fix— address security early. Set rules. Clean your data. Ensure to safeguard confidential data.

6. Absence of Trustworthy Partners

Many companies try to build AI alone. Others hire partners with no real experience. Both paths fail. AI requires skill, time, and structure. Most teams lack the bandwidth. Vendors with weak industry knowledge add more risk. The result is predictable. Wrong use cases. Wrong tech stack. Poor rollout. Projects that never scale.

Work with partners who know your industry and have delivered real outcomes. Ask for evidence. Look for teams that focus on people and process, not only tools.

How Leaders Move Forward: Your AI Adoption Playbook

What is the best strategy for successful AI adoption? Most leaders ask this question after stalled pilots and unclear results. An MIT report shows that 95% of generative AI pilots fail. Only five percent deliver fast revenue growth. The problems are known. The blockers are clear. What matters now is a plan you can act on. The next steps give you a simple path to stable adoption, clear value, and long-term progress. Each strategy focuses on one goal. Reduce friction and improve accuracy. Strengthen trust. Create a system your teams trust and use with confidence.

Strategy 1: Use the 30 Percent Rule and Keep Control

AI should take the repetitive work, but your people should make the decisions that matter. A simple split works. AI handles most repetitive activities. Humans handle the strategic parts that drive value. Examples include support, finance, and legal review. AI processes the bulk of the work. Humans own edge cases, decisions, and context.
This model improves trust. Companies achieve greater consumer trust percentages when they implement responsible AI along with human supervision.

What the 30 Percent Rule Tells You

AI handles repetitive work well. Humans handle judgment and strategy. In legal work, AI reviews most clauses. Lawyers focus on the few that matter. In finance, AI handles routine analysis. Humans handle portfolio decisions and client strategy. Automating the wrong tasks destroys value. Protect the human layer. It creates the critical insight your business needs.

Strategy 2: Always Keep a Human in the Loop

AI needs continuous human guidance. During training, humans label data and adjust outputs.
Before launch, experts test the system and fix errors. After launch, teams monitor decisions and report issues. This reduces bias and mistakes. It also builds internal confidence.

Strategy 3: Build a Clear Roadmap

Do not start with advanced use cases. Start small.
Phase 1. Minimize operational barriers and streamline routine activities. Utilize RPA, chatbots, and document handling. These quick wins build momentum.
Phase 2. Predict future outcomes. Use forecasting, segmentation, and recommendation models. These projects offer long term value.
Phase 3. Scale what works. Integrate with core systems. Build new business models.
Each phase supports the next. Set clear metrics for each phase and track them without excuses.

Strategy 4: Bring in AI experts who know what they are doing

Strong partners shorten your learning curve. Choose partners who know your industry. Ask for real case studies. Confirm they understand organizational change. Check their ability to work with your existing systems. A good partner brings a clear method. They guide you from assessment to deployment and support scaling.

How Fingent Can Help You Adopt AI

Fingent guides companies from confusion to clarity. Their model is simple and proven.

Stage 1. Reduce Friction
Fingent identifies repetitive processes. We deploy RPA, document processing, and chatbots. This frees your team to focus on high value tasks.

Stage 2. Predict Outcomes
Fingent builds predictive analytics, recommendation engines, and segmentation models. Our experts help you improve forecasting and customer insights. We strengthen your governance and data discipline.

Stage 3. Scale and Advance
Fingent expands successful use cases. We integrate with core systems. Additionally, we support long-term transformation and new business value.

CASE STUDY: The Sapra & Navarra Success Story

AI/ML Claims Management Solution

Industry – Legal/Finance

Key Metrics:

• Case Settlement Time: Reduced from years to 1-2 days
• Settlement Cost Reduction: Over 50% reduction
• Business Impact: Enabled expansion into new insurance domains

Solution: A light-touch workers’ compensation solution powered by AI and ML

Key Success Factors:

• Clear problem identification (reduced settlement time)
• AI augmenting human expertise (not replacing lawyers)
• Human-in-the-loop approach for strategic decisions
• Decrease in average total claim costs and claim cycle time

What Sets Fingent Apart?

We provide human oversight as a standard. We run validation loops and follow strong governance. We fix data issues with clear mapping, cleanup, and security.

We start small, but ensure big results. We focus on modernizing legacy systems and integrating AI without disrupting operations. And that’s not where we stop. Fingent supports cultural change and upskilling to help businesses build confidence in leveraging new-age technologies to their maximum benefit.

Discuss your ideas with us and hear our expert solutions tailored to your unique needs.