Reveal調査レポート:2025年のソフトウェア開発の主な課題

イントロダクション:AIの成長の痛みと今後の道

2025 年のReveal Survey の結果は、AI がもはや未来ではなく、現在であるという明確な変化を裏付けています。誇大広告は終わりました。AIは運用上の必要性です。導入は広く行われていますが、実行力は依然として最大の課題です。

今日、企業が直面している最大のリスクは何か?AIの実行、セキュリティ、および人員戦略を習得した競合他社に遅れをとっています。まだアプローチを洗練させようと奮闘している人々は、客観的に見て時間がありません。

私たちの調査によると、多くのCTOがAI導入の複雑さを過小評価しています。その結果は?彼らが予想していたものとは違いました...

明確なAI戦略がなければ、企業はAIの導入に失敗したからではなく、AIがもたらす課題に対応できなかったためにリスクにさらされます。

2025年の現実がこれを証明しています:AIの導入だけでは十分ではありません。実行によって成功または失敗が決まります。アーリーアダプターは、戦略なしにAIの統合を急ぎました。その結果は?システムの断片化、非効率性、予測不可能なパフォーマンス。今、CTO は余波を解決しなければ、遅れをとらなければなりません。

概要: このレポートでは、2025 Reveal Surveyから得られた主要なインサイトを示し、2025年にAIの導入が拡大する中でビジネスリーダーが理解しなければならない重要な変化について概説しています。今年の調査結果を2024年と比較することで、AIの優先事項がどのように進化したか、そして企業が競争力を維持するために次に何をすべきかを検証します。

AIの進化:導入から実行の課題まで

2024年はAIが加速した年でした。企業は、ジェネレーティブAIを開発プロセスに統合することに熱心に取り組み、ワークフローの自動化、効率の向上、エラーの削減を望んでいました。

しかし、アーリーアダプターはすぐに、AIが生成するコードに一貫性がなく、人間の監視が必要であり、新たなセキュリティリスクをもたらすことに気づきました。

報告すべき主な調査結果

成長機会:AI主導の成功

2024年と2025年:優先順位の変化

企業のリーダーは、もはやAIを採用するかどうかではなく、AIを大規模に機能させる方法を求めています。

Reveal調査データ:SaaS開発とビジネストレンド(2024年対2025年)

2025年の変化を完全に理解するには、2024年の優先事項と比較する必要があります。この分析を並べて示すと、AI の導入、セキュリティ上の懸念、労働力の傾向、ビジネス上の課題における最も重要な変化が浮き彫りになります。

The transition from 2024 to 2025 marks a pivotal moment. AI adoption is no longer the focus—execution, security, and talent shortages now define industry challenges.

The table below outlines the key shifts:

Business Challenges

Company Growth & Expansion

Software Development Trends

Embedded Analytics & BI Growth

Challenges in Embedded Analytics Adoption

Key Takeaway:

After the AI gold rush, CTOs and corporate leaders are facing unexpected complexities—security risks, inefficiencies, and a deepening talent crunch. In 2024, developers struggled with limited resources (41%), but as resources expand, the focus has shifted to AI reliability (45%), security threats (51%), and workforce gaps (48%).

AI-driven companies saw 80% revenue growth and 82% took on new projects, proving that AI delivers massive ROI—but only for those who execute effectively. 73% are expanding AI use, yet 42% struggle with tech resources, and 35% face shifting analytics needs, exposing cracks in execution.

The Clock is Ticking: Resources are no longer the bottleneck—AI is delivering massive ROI, and the race is now about who can overcome these challenges the fastest. Those still stuck in adoption mode are running out of time, and in this accelerating AI climate, execution is the difference between life and death.

Key Findings & Analysis

1. AI Deployment: From Adoption to Execution Struggles

Overview

The biggest shift in 2025 is the move from AI adoption to execution challenges. In 2024, businesses focused on integrating AI, but by 2025, execution failures, security risks, and workforce shortages have overtaken adoption as the primary concerns.

Key Statistics

• 55% of tech leaders say AI deployment is their biggest challenge.
• 45% report AI code reliability issues.
• 44% still struggle with AI rollout complexity.

Reason

Many companies adopted AI expecting automation and efficiency gains, but without a clear data strategy, governance framework, and scalable infrastructure, execution failures emerged. AI models require constant monitoring, refinement, and security protocols, leading to deployment bottlenecks.

Analysis

Having AI is no longer a competitive edge—making it work effectively and securely is now the challenge. Companies that failed to plan for maintenance, security, and AI reliability are now dealing with high costs, performance issues, and operational setbacks.

 

“AI deployment at scale isn’t just a technical hurdle—it’s an enterprise-wide challenge. Too many organizations implement AI in isolated pockets rather than integrating it across their business. Without a clear strategy for governance, execution, and sustainability, they risk inefficiency, compliance issues, and missed ROI.”

Kurt Petersen, SVP of Customer Success at Camunda, “AI Deployment Challenges,” MSN, 2025

 

Summary

AI has shifted from an innovation driver to an operational challenge. Companies must now focus on stabilizing AI workflows, improving reliability, and securing AI applications. Those that fail to refine implementation will fall behind competitors who prioritize optimization.

2. Security Takes Center Stage

Overview

Security is now the top software concern in 2025. As AI becomes embedded in business processes, automated cyber threats, AI-generated vulnerabilities, and regulatory risks have escalated. Attackers are evolving faster than security teams can adapt, making proactive AI security strategies a necessity.

Key Statistics

• 51% of organizations rank security as their top concern.
• 41% cite data privacy as a growing challenge.

Reason

AI-generated code often lacks built-in security validation, exposing companies to data breaches, unauthorized access, and automated cyberattacks. Additionally, AI-driven cyber threats—deepfake scams, phishing automation, and synthetic fraud—are increasing in sophistication, making traditional security defenses ineffective.

Analysis

Security can no longer be an afterthought—it must be integrated into AI development from the start. Companies need real-time threat detection, AI auditing, and compliance-driven security measures to stay ahead of evolving threats.

Summary

AI-driven security risks have transformed cybersecurity from an IT concern to a business imperative. Organizations that fail to adopt security-first AI practices risk major breaches, regulatory fines, and operational failures.

3. The Tech Talent Crisis Deepens

Overview

The AI talent shortage, which saw some improvement in 2024, has worsened in 2025—especially in AI and cybersecurity roles. Companies that rapidly adopted AI now lack the specialized workforce needed to scale, refine, and secure their AI-driven infrastructure.

Key Statistics

• 48% of tech leaders say hiring AI and cybersecurity talent is a major challenge.
• 63% say AI expertise is their most critical hiring need.
• 55% of companies created new AI-related roles.

Reason

AI’s rapid expansion has outpaced workforce readiness. Many companies assumed generalist developers could handle AI, but now realize that specialized expertise—AI engineers, data scientists, and cybersecurity specialists—is essential. The demand for AI and security professionals far exceeds supply.

Analysis

The talent shortage is now a direct roadblock to AI execution. Without skilled professionals to manage AI governance, security, and optimization, businesses are experiencing stalled innovation, higher risks, and operational inefficiencies.

Summary

This isn’t just a hiring problem—it’s an industry-wide crisis. Companies must invest in AI training, upskill existing employees, and prioritize specialized hiring to sustain long-term AI success.

4. Business Growth Exceeds Expectations

Overview

Despite AI deployment struggles, companies that strategically implemented AI have seen significant revenue growth and increased project opportunities. Businesses that took a structured, security-first approach experienced the highest efficiency and profitability gains.

Key Statistics

• 80% of companies saw revenue growth.
• 82% took on new projects in 2024.

Reason

Organizations that avoided rushed AI rollouts and focused on scalable AI integration, automation, and analytics-driven decision-making saw the strongest financial benefits.

Analysis

AI adoption alone isn’t enough—businesses that optimize execution, security, and workforce training are outperforming expectations. Companies that failed to address these areas are seeing inefficiencies and missed opportunities.

Summary

AI-driven growth is possible, but only for companies that focus on execution, security, and workforce readiness. Businesses that refine AI strategies and invest in scalable, secure AI applications will continue to see financial gains.

5. Embedded Analytics Becomes a Must-Have

Overview

Data-driven decision-making is no longer optional—it’s a competitive necessity. Companies that fail to leverage AI-powered insights, predictive analytics, and real-time monitoring risk falling behind.

Key Statistics

• 81% of companies now use embedded analytics.
• 42% struggle with implementation challenges.

Reason

While embedded analytics adoption is growing, businesses lack the expertise and infrastructure to integrate, scale, and automate AI-driven insights. Data silos, poor analytics workflows, and limited technical resources are preventing companies from fully capitalizing on analytics.

Analysis

Organizations that invest in seamless analytics integration, AI-powered decision-making, and real-time data accessibility will have a competitive edge. Companies that fail to overcome analytics adoption barriers will struggle to remain competitive.

Summary

Embedded analytics is no longer a “nice-to-have” feature—it’s now a fundamental business requirement. Organizations that fail to optimize analytics risk losing out on AI’s full potential.

How Reveal AI is Bridging the Industry Gap with Conversational Analytics

The Accessibility Challenge in Traditional Analytics

One of the biggest gaps in traditional analytics is accessibility—most BI tools require specialized knowledge, SQL queries, or manual report-building, making it difficult for non-technical users to extract meaningful insights.

Even with embedded analytics, users still need to navigate dashboards, apply filters, and analyze data before drawing conclusions. Historically, this process can be slow, technical, and prone to human error.

Conversational Analytics: A New Approach

Conversational analytics changes this paradigm. Instead of manually sorting through data, applying filters, and generating reports, users can simply ask direct questions in natural language and receive AI-powered insights instantly.

Reveal AI is at the forefront of this transformation, enabling businesses to:

• Access insights instantly: Conversational queries replace static dashboards, allowing users to get answers without navigating complex interfaces.
• Eliminate manual analysis: AI automatically uncovers trends, anomalies, and key insights, removing the need for users to manually analyze data.
• Real-Time AI-Driven Decision Support: Automated analytics proactively identifies patterns, trends, and opportunities, giving businesses a competitive edge.

How it Works

Conversational analytics functions like an AI-powered assistant—think of it as a ChatGPT-style prompt menu, but designed to generate precise, real-time insights directly from your data based on your specific queries. Instead of navigating complex dashboards, users can type a specific question into a prompt and receive immediate, precise answers without needing to manually filter reports or analyze raw data.

For example:

• Instead of searching through multiple reports to find monthly revenue trends, a user can simply ask: “What were our top-performing products last quarter?”
• Rather than applying filters manually, a user can ask: “Show me sales growth by region over the last six months.”

With Reveal AI, users no longer need technical expertise to find critical insights—they ask, and the system provides answers. This removes the complexity of traditional analytics and ensures that every decision-maker has access to real-time, AI-driven insights without relying on data teams or BI specialists.

The New Standard for Business Intelligence

Reveal AI eliminates the complexity of traditional BI, making data accessible, actionable, and immediate. By removing the need for specialized training, it opens the door for anyone—not just data analysts—to engage with analytics and make informed decisions.

This shift represents a fundamental change in how businesses operate. With AI-powered analytics embedded directly into workflows, organizations can adapt faster, scale more efficiently, and drive continuous innovation.

Stay ahead of the trend visit our site for early access to Reveal AI.

Final Thoughts: What This Means for Businesses and Corporate Leaders

The 2025 Reveal Developer Survey confirms a stark reality: AI adoption alone no longer guarantees success. Companies that fail to scale, secure, and execute AI effectively will fall behind—fast.

The race isn’t about who has AI—it’s about who can control it before inefficiencies, security risks, and talent shortages take their toll.

CTOs must act now. Hesitation means market irrelevance.

Key Takeaways for Tech Leaders:

• AI Execution Crisis: 55% struggle with AI deployment. Scale fast or fall behind.
• Security Risks Are Surging: 51% rank AI security as the top concern. Weak defenses = breaches, fines, and losses.
• AI Talent Shortage Is a Crisis: 48% can’t find AI/security experts. Without them, failures and risks explode.
• AI-Driven Companies Dominate: 80% saw revenue growth, 82% expanded. Leaders execute—laggards fade.
• Embedded Analytics Defines Winners: 81% use it, 42% struggle. Real-time insights are the new competitive edge.

The Year Ahead

2025 is the year of execution. Companies that scale AI, secure it, and hire the right talent will dominate. Those that don’t won’t survive. The clock is ticking. Who will execute, and who will be left behind?