Building AI-Ready Enterprises: Methods and Strategies

Author: Ting-Yuan Wang, Solution Architect at Vizuro

Realities of AI Implementation

Over the past eight years, Vizuro has witnessed the rapid development and widespread application of AI across various industries. According to our client statistics, AI applications are most prevalent in the healthcare sector, accounting for 34%, followed by 25% in the pharmaceutical and life sciences industries, and 23% in manufacturing. Despite the enthusiasm and expectations surrounding AI technology, its practical implementation faces significant contradictions and challenges.

Over the past eight years, Vizuro has launched lots of AI projects to serve a wide range of industries.

Although as many as 92% of enterprises plan to increase their investment in generative AI within the next three years, only 1% of senior executives believe their organizations have achieved the ideal state of AI maturity. More concerning is that, according to Gartner's research report, about 30% of generative AI projects are forced to halt during the proof-of-concept (POC) stage due to excessive implementation costs. These figures highlight not only the complexity of AI technology in practical applications but also the real dilemmas enterprises face when promoting AI transformation.

Key Challenges to Become AI-Ready Enterprises

Building an AI-ready enterprise is a complex and multifaceted task that must overcome the following key challenges:

Business Value: Enterprises need to clearly define the connection between AI initiatives and business objectives, ensuring that the application of AI technology can directly drive core business outcomes and create measurable value.
Talent: In an era of declining birth rates, enterprises face the severe challenge of talent shortages. Attracting, cultivating, and retaining professionals with AI technology and cross-disciplinary capabilities has become crucial for enterprise transformation.
Technology: Seamlessly integrating AI technology into corporate culture and daily operations to enhance overall productivity and efficiency requires not only upgrading technical infrastructure but also driving organizational cultural changes, enabling employees to accept and effectively use AI tools.
Process: Enterprises must achieve transformative changes in processes while ensuring that AI systems' decision-making processes are explainable and transparent, thereby driving effective actions and building trust.
Data: Building customized AI solutions that allow AI agents to provide core value through excellent user experiences requires enterprises to establish high-quality data foundations and ensure data availability, security, and compliance.

As emphasized in McKinsey's January 2025 report, "Superagency in the Workplace: Empowering People to Unlock AI's Full Potential," science itself does not possess inherent authority—the real key lies in how we apply these technologies and transform them into actual business value and social impact. In the process of promoting AI transformation, enterprises must be people-oriented, combining technological innovation with organizational change to fully unleash AI's potential.

Core Business Objectives Driven by AI Applications

Currently, the primary business objectives for enterprises promoting AI technology applications focus on the following key areas:

Cost Optimization (23%): Enhancing operational efficiency through AI technology to reduce labor and resource costs has become the top priority for nearly a quarter of enterprises.
Customer Experience and Loyalty Enhancement (20%): Enterprises aim to provide more personalized and real-time services through AI technology, thereby increasing customer satisfaction and brand loyalty.
Business Growth Opportunities (18%): AI technology is seen as an important tool for exploring new markets and discovering new business opportunities, helping enterprises maintain a leading position in the competition.
New Product or Service Development (16%): The innovative capabilities of AI technology drive enterprises to develop more competitive products and services to meet market demands and create differentiated advantages.
Infrastructure and Operations Improvement (6%): Some enterprises apply AI to optimize internal infrastructure and operational processes to enhance overall efficiency and stability.
Software Development Lifecycle Enhancement (4%): The application of AI technology in software development, such as automated testing and code generation, is gradually changing traditional development models, improving efficiency and quality.
Other Purposes (13%): Including risk management, data analysis optimization, and other diverse application scenarios, demonstrating the broad potential of AI technology.

Source: Gartner, 2024 report, Executive Pulse: AI Funding and Accountability Is Not One Size Fits All. The report reveals the diverse goals of companies in AI investment and application, and emphasizes the key role of AI technology in driving business value and innovation.

Key Considerations for Selecting AI Pilot Projects

When selecting AI pilot projects, enterprises must carefully evaluate the following four key factors:

Outcomes: What specific and achievable outcomes can the project deliver? Are these outcomes aligned with the enterprise's strategic objectives?
Value: What kind of business value can the project bring to the enterprise? Can it significantly improve efficiency, reduce costs, or create new revenue streams?
Technical Feasibility: Does the enterprise have sufficient technical resources and capabilities to implement the project? Can the existing infrastructure and team support the smooth progress of the project?
Risk Tolerance: How much risk can the organization tolerate? Are the potential risks and returns of the project within an acceptable range?

Project selection should be based on the "Risk and Value Matrix" for evaluation. Ideal pilot projects should be located in the "high value, moderate risk" quadrant, as these projects can bring significant business benefits while keeping risks manageable. Projects in the "safe zone" (low risk, low value) are easy to implement but often have limited impact and are unlikely to drive substantial enterprise transformation. On the other hand, enterprises should be cautious about projects with high ambition but low success probability to avoid resource waste or significant losses due to excessive risk-taking.

Through systematic evaluation and selection, enterprises can ensure that AI pilot projects are not only feasible but also lay a solid foundation for future comprehensive AI transformation.

Evolution and Paradigm Shift in AI Approaches

AI has been undergoing various paradigm shifts, each representing different technological focuses and application directions:

First, AI-Centric
This paradigm focuses on developing general AI capabilities, emphasizing text-based analysis and summarization. The goal is to create AI systems that can be widely applied across various scenarios with high versatility and adaptability.
Then, Data-Centric
This paradigm focus on multimodal data recognition and its applications, optimized for specific domains. Through deep learning driven by vast data, AI systems can exhibit outstanding accuracy and efficiency in specific tasks, such as medical imaging or industrial automation.
Now, Agentic
This paradigm represents a further evolution of AI technologies in LLM (Large Language Model), leading to the advent of multi-agents that can work in concert to automate complex tasks. They can understand text, images, sound, and other data forms and collaborate across business functions to achieve highly efficient decision support and process automation.

Each paradigm shift reflects the needs and challenges of AI technology at different stages. From exploring general capabilities to optimizing specific domains and then to cross-functional integration, the continuous evolution of AI methods is driving the digital transformation of enterprises and society, opening up infinite possibilities for future innovative applications.。

Example: Reinventing Old Business with AI

As an example, AI is fundamentally reshaping automobile manufacturing, extending far beyond traditional automation to drive unprecedented efficiency and sustainability. This transformation leverages virtual environments, holistic optimization, and end-of-life value recovery.

Digital Twins: Interfacing Real World with Omniverse

Digital design blueprints can turn into dynamic virtual replicas of physical assets and processes – interface with platforms like Omniverse. Here, AI machine vision models are trained on vast synthetic data to master anomaly detection and quality control with superhuman precision. These virtually-honed AIs are then deployed onto real-world production lines, identifying defects and ensuring components meet exact specifications, drastically improving quality and reducing waste.

Beyond the Shopfloor: Holistic Simulation and Optimization

The scope broadens to holistic enterprise optimization. Recognizing that localized improvements don't guarantee overall efficiency, AI analyzes interconnected Digital Twins representing the entire value chain – factories, supply chains, logistics – within the Omniverse. This allows for system-wide simulations and optimizations, enhancing resilience against disruptions, refining global production schedules, and managing resources more effectively across the entire organization.

Circular Economy: Giving Product A Second Life Through AI

One of the most promising applications of AI in manufacturing lies in enabling circular economy models. By leveraging AI-powered predictive maintenance, quality assessment, and intelligent logistics, companies can identify when products can be reused, refurbished, or recycled — all before reaching end-of-life. This shift not only reduces waste and environmental impact but also creates new revenue streams by extending the lifecycle of assets.

In this context, AI becomes more than a productivity tool; it becomes a driver of sustainable innovation.

AI Agent 101: A Practical Guide to Implementation

On-Prem or Cloud?

For companies with existing NAS (Network Attached Storage) infrastructure, on-premise deployment may be the more suitable choice. This not only ensures data security and privacy but also maximizes existing resources, reducing upfront investment costs. However, for companies seeking greater flexibility and scalability, cloud-based solutions offer more powerful computing capabilities and collaborative potential.

Define Success Metrics

When implementing AI agents, companies should avoid focusing solely on single indicators like speed or adoption rates. Instead, the true key to success lies in achieving 100% process coverage, ensuring that AI technology is seamlessly integrated across all aspects of business operations to maximize value creation.

Customizing Enterprise AI Agents

Leverage the experiences learned from the pandemic to reexamine and optimize the remote workflows. This not only boosts employee productivity but also provides targeted use cases for AI agent design. For example, automating repetitive tasks through AI allows employees to focus on higher-value, strategic work.

Strengthening the Ecosystem

Leading AI companies are solidifying their ecosystems by enhancing the user experience. This includes offering more intuitive interfaces, smoother operational processes, and highly personalized services. When implementing AI agents, enterprises should learn from these successful models—ensuring that AI not only improves internal efficiency but also delivers outstanding customer experiences.

The Path to Becoming an AI-Ready Enterprise

To truly build an AI-ready enterprise, organizations must follow these three essential steps:

1. Insight: Understand the Strategic Value of AI

Companies must deeply explore the strategic role of AI in their business—clarifying how it drives innovation, improves efficiency, and builds competitive advantage. This isn’t just about adopting technology; it’s about redefining the future direction of the organization.

2. Precision Execution: Implement Goal-Oriented AI Solutions

When deploying AI solutions, companies should establish clear goals and measurable outcomes, ensuring that every step moves toward the desired business value. Only through precise execution can AI technology become a true catalyst for business growth.

3. Own the Future: Lead the AI Revolution, Don’t Follow It

Enterprises should proactively embrace AI technology, viewing it as a core engine of transformation and innovation. Through forward-thinking strategies and bold positioning, organizations can claim leadership roles in the AI revolution—instead of passively following market trends.。

To truly become AI-ready, organizations must go beyond tool adoption and invest in building a culture of experimentation, cross-functional alignment, and continuous learning. Agentic AI is not simply a technical upgrade—it represents a shift in how companies approach value creation, decision-making, and agility. Those who prepare their teams and infrastructure today will be the ones defining tomorrow's industry standards,