Hello, I'm Tak@, and as a system integrator, I explore the possibilities of AI every day.

Is your company investing heavily, only to find that your AI solutions are no different than just using Excel? The truth is, introducing technology alone isn't enough to maximize AI's potential.

To truly unleash AI's transformative power, a thorough, organization-wide preparation that goes beyond mere tool adoption is essential. This is the core concept of being "AI-ready."

What is AI-Ready?

"AI-ready" refers to a state where a company is fully prepared to effectively utilize AI and maximize its benefits. This isn't just about implementing AI tools. It means preparing to extract the full capabilities of AI technology, strategically integrating it into the organizational culture, and embedding it into daily operations.

SoftBank's AI Promotion Office defines it as a state where all internal data is properly organized, corporate know-how is verbalized and shared, and the company is prepared to maximize the technical capabilities of future AGI (Artificial General Intelligence) and ASI (Artificial Superintelligence) when they emerge.

When I first encountered this concept, I felt it was more than just a tech trend; it was a significant shift that would shake the very foundation of business operations.

For example, whether we can truly unlock AI's potential depends on how well our data is organized and how much of our daily operational knowledge is formally shared.

Why is AI-Ready Important Now?

Today, the advent of AGI (Artificial General Intelligence) is becoming a reality, and the realization of ASI (Artificial Superintelligence) is not far off. In particular, 2025 is predicted to be the "Year One for AI Agents," with a steady increase in AI agent adoption by businesses.

However, the major obstacles to this adoption are data and know-how. If data is insufficient, fragmented across departments, or un-structured, the accuracy of AI agents will suffer.

Furthermore, effectively verbalizing on-the-ground knowledge and incorporating it into AI requires collaboration with those who deeply understand the operations.

The current evolution of AI is like a train that has started moving at tremendous speed. To avoid being left behind, I believe it's not enough to just buy a ticket; we need to be prepared to ride it.

Especially for large enterprises with vast amounts of data and know-how, the importance of being AI-ready is growing daily.

5 Hurdles to AI Adoption and How to Overcome Them

Integrating AI into an organization is fraught with challenges. However, these are not insurmountable barriers. By understanding each challenge and taking appropriate measures, you can successfully implement AI.

The Data Access and Quality Hurdle

AI algorithms learn by training on large datasets to recognize patterns and make predictions. If AI systems lack access to sufficient or diverse data, the learning process can be severely limited, leading to inaccurate predictions or biased outcomes.

For an organization to be truly AI-ready, it must ensure access to large, diverse, and high-quality datasets and be able to handle that data securely and in a way that respects privacy regulations.

Here are some measures to overcome this challenge:

• Establish a data governance program: Set rules for data collection, storage, processing, and protection within the organization, creating a framework to ensure data quality, accessibility, and security. While large enterprises may need a dedicated team, SMEs can start with a cross-departmental working group.
• Implement data cleansing and preprocessing techniques: Clean and transform data by handling missing values, removing duplicates, and correcting inconsistencies before using it in AI systems.
• Collaborate with data providers: Partnering with external specialist providers to access datasets that cannot be generated internally can also be effective.

From my experience, data quality issues are often the biggest stumbling block in the initial stages of AI adoption. Even the most advanced AI model won't yield desired results with poor-quality data.

The Infrastructure and Computing Resources Hurdle

Training complex AI models, especially in machine learning, demands powerful computational capabilities and the ability to process vast amounts of data efficiently.

AI infrastructure is specifically designed to handle the intensive computational requirements and large datasets of AI workloads, including specialized processors like GPUs and TPUs, high-speed networks, and scalable storage systems for massive data.

Here's how to approach this challenge:

• Utilize cloud computing services: These offer flexible and scalable solutions, providing high levels of computing resources on-demand without significant upfront investment.
• Invest in High-Performance Computing (HPC): Designed to process large amounts of data quickly and efficiently, HPC can significantly reduce AI model training times.
• Conduct a technology audit: Evaluate current technical capabilities and identify gaps with the requirements necessary for AI initiatives.

As a system integrator, I've seen many companies hit a wall where their existing IT infrastructure can't meet AI's demands. Without the physical "container," even the most brilliant AI cannot demonstrate its power.

The Data Literacy Hurdle

For AI to be effective, employees across the organization need to understand how to interpret and utilize data. This includes the ability to read, handle, analyze, and discuss data.

A lack of data literacy makes it difficult to extract meaningful insights from AI systems, limiting their value. According to Accenture, only 21% of employees report confidence in their data literacy skills.

Here are effective strategies to overcome this challenge:

• Invest in data literacy training programs: Provide training programs that offer specific, real-world scenarios and examples to help employees effectively use available tools.
• Offer comprehensive and personalized training: Provide detailed training that caters to all levels of data literacy within the organization.

AI is merely a tool. If there are no "people" to wield that tool, it's a wasted asset. I believe that fostering an environment where every employee understands the potential of data and AI and can confidently use them is paramount.

The Ethical and Legal Aspects Hurdle

AI challenges existing assumptions about fairness, accountability, and control, from how data is used to how decisions are made. Therefore, ethical governance is an indispensable element of AI readiness. Without clear policies, procedures, and monitoring mechanisms, organizations risk deploying opaque, biased, or harmful systems.

Here are concrete measures to address this challenge:

• Establish an ethical framework: Before implementing AI tools, companies need to define clear ethical principles that guide the development and use of AI systems, being mindful of their own ethical framework.
• Ensure transparency of AI algorithms: To build trust with customers and other stakeholders, companies must demonstrate the ethical principles of their developed AI systems and be able to explain how customer data is used.
• Thoroughly implement legal compliance measures: Continuously ensure compliance with federal and local requirements, such as data privacy, anti-discrimination laws, and consumer protection laws.
• Utilize model cards and system cards: Increase transparency by creating brief documents that disclose information about models, including their intended use, performance metrics, and evaluations under various conditions.
• Consider open-source AI: Improve algorithmic transparency by publicly releasing source code, which enhances the potential for community scrutiny and improvement.

AI is not just technology; it is something that profoundly impacts society. I believe that the ethical and compliance aspects are as important as, if not more important than, technological advancement. Without trust, no matter how excellent the AI, it will not be accepted.

The Resistance to Change and Cultural Hurdle

A common challenge organizations face when implementing AI technology is resistance to change and cultural barriers. Some employees may fear that AI will eliminate jobs or reduce career advancement opportunities.

There may also be a lack of understanding about how AI works or what benefits it brings to the organization. In fact, a NewVantage survey reported that only 24% of companies have successfully built data-driven organizations.

Here are effective strategies to overcome this challenge:

• Build a culture of innovation and AI literacy: Companies must openly communicate the "why" behind AI adoption, what AI can and cannot do, and how it will impact roles and responsibilities.
• Educate stakeholders on the benefits of AI adoption: Employees who understand how AI will affect their roles and are given the tools to adapt are more likely to become AI advocates.
• Encourage collaboration and cross-functional teams: Build psychological safety so employees feel they are partners in the process and have a forum to voice concerns.

Change always creates resistance. But that's natural. AI adoption is as much an organizational culture transformation project as it is a technological one. I believe careful communication and support are essential to help employees see AI as an "ally" rather than an "enemy."

The Power of "Data" Supporting AI-Readiness

AI systems run on data, but not just any data will do. Effective, scalable, and ethical AI requires high-quality, properly managed, and easily accessible datasets.

Without these, AI initiatives can falter or risk amplifying biases, violating compliance, or producing unreliable results.

The Relationship Between AI and Data

The success of AI is directly linked to data quality. AI algorithms learn, recognize patterns, make predictions, and perform tasks by training on vast datasets. The larger the dataset, the better the AI model learns and generalizes its knowledge to new data.

There's a saying: "Your AI is only as good as your data." This clearly illustrates that AI's capabilities are heavily dependent on the quality of the underlying data.

In my experience, projects that invested more time in data preprocessing and cleaning tended to achieve higher AI prediction accuracy.

Practicing Data Governance

Strong governance transforms raw data into trustworthy, auditable, and compliant AI inputs. This includes policies for access control, metadata standards, data retention, anonymization, and fairness audits.

In the age of AI, proper data governance becomes a competitive advantage.

• Develop a data management plan: Creating a comprehensive plan for how data will be managed across the organization is fundamental to governing all AI systems.
• Manage the data lifecycle: Consider the entire data lifecycle, from collection to retention and disposal, to ensure that the data required by AI systems is appropriately utilized.
• The importance of metadata: Comprehensive metadata provides the right context for users to obtain high-quality information at the right time. Lineage information, showing the origin of data, is also crucial for ensuring data trustworthiness and quality.

Data is truly the "blood" of AI. If that blood is stagnant, the AI "body" cannot be healthy. Establishing transparent data flows and rules for maintaining data quality, though seemingly mundane, is essential for building the foundation of AI-readiness.

Data Quality and Security

Data quality and accessibility play a crucial role in AI readiness and operations. AI algorithms learn through machine learning, and this directly impacts their accuracy, reliability, and effectiveness. Furthermore, organizations have a responsibility to protect data to prevent misuse.

• "Fit for purpose": One of the most critical challenges in designing and developing AI systems is ensuring that the data used is appropriate for its intended purpose.
• Incorporate privacy and security principles: Building and maintaining data trust can be achieved by incorporating principles such as limiting personal data collection, specifying purposes, restricting use, and implementing security safeguards into data management.
• Utilize data inventories: Personal data inventories play a vital role when AI systems use personal data, helping organizations understand and demonstrate how data is collected and used.

In projects I've been involved with, I've always struggled with balancing privacy protection and maintaining AI model performance when designing data anonymization or pseudonymization processes.

I believe that finding the right balance here is a crucial challenge for building trustworthy AI.

Cultivating "AI-Ready" Talent

No matter how advanced AI tools become, their effectiveness depends on the capabilities of the people who build, manage, and use them. AI readiness is not just limited to the IT department or data science team; it spans the entire workforce, from management to frontline employees.

Skill Gaps and AI Literacy

An AI-ready workforce is needed right here, right now. According to a LinkedIn study, AI literacy is listed as the most important employability skill this year.

While educational institutions offer AI literacy opportunities to students and educators feel obligated to foster students' AI skills, there is a current reality where educators themselves lack confidence in their AI skills.

• AI enhances human capabilities: AI models are more likely to augment human capabilities than replace humans.
• Essential foundational skills: Effectively using AI requires foundational skills that were important long before AI emerged. This includes critical thinking, the ability to apply knowledge in real-world contexts, and the ability to continue learning with curiosity and independence.

I believe that AI will transform the nature of our work, making it more creative and human-centric, rather than taking our jobs. To achieve this, the ability to "master" AI is, of course, essential, but so is the critical thinking skill to "question and ponder" the results AI produces.

Continuous Learning and Reskilling

AI is evolving at an incredibly rapid pace, with 47% of trained AI users still feeling insufficiently trained. This suggests that training struggles to keep up with the rapid advancements in AI technology, and people want to know more about AI's potential.

• Managerial AI adoption: Managers have a higher adoption rate of AI (28% compared to 9% for non-managers) and utilize AI's cognitive assistance features more broadly. They use AI for cognitively intensive tasks like summarizing meetings, information retrieval, communication drafting, and idea generation.
• Youth leadership: Young people aged 18 to 24 have the highest rate of daily AI use (39%) and are expected to play a leading role in AI adoption.
• Establish internal learning environments: Companies need to set up internal schools or dedicated AI assistants to enable employees to continuously acquire new skills to keep up with the constant progress of AI.

Even as I struggle to study for my AWS Certified Solutions Architect certification, I deeply feel the importance of validating with minimum functionality using general-purpose models first.

Every time new technology emerges, there's more for us to learn. But isn't this learning process precisely what will be our weapon to survive the age of AI?

Impact on Work and New Roles

AI is fundamentally changing the way we work, going far beyond the traditional automation scope of AI. A survey by Artefact reported that 59% of AI users say AI has created jobs within their companies, with a particular increase in technical roles and business roles that integrate AI into daily operations.

• Increased productivity and time savings: AI users report saving an average of 57 minutes per day, with the top 4% saving over three hours. With this saved time, 56% of users are completing more tasks than before.
• Improved knowledge access and generalization: 69% of AI users feel their access to knowledge has improved, allowing employees to become more versatile "generalists" and bring high flexibility to the organization.
• Automation of repetitive tasks: With the advent of AI agents, repetitive administrative tasks like data entry, recording customer information in CRM, and processing invoices are at risk of automation.
• Democratization of creative work: AI becomes a new "pencil and brush" for content creation (text, images, video), helping designers generate and test more ideas, transforming the entire creative process.
• Evolving role of experts: Experts will shift to roles that manage and supervise AI assistants. They will be responsible for ensuring the accuracy and up-to-dateness of the foundational documents and data that knowledge assistants rely on.

Our work will shift from "quantity" to "quality" with AI. Simple, repetitive tasks will be handled by AI, allowing humans to focus on more creative and emotionally connected areas. I believe this is also an opportunity for us to rediscover our "humanity."

Fostering an AI-Ready "Organizational Culture"

Implementing AI technology is not just about changing the tech stack; it's about changing the organizational culture itself. AI introduces new workflows, shifts decision-making authority, raises questions about job stability, and forces teams to adopt different ways of thinking about trust and transparency.

Leadership and Strategic Alignment

AI is not a plug-and-play technology; it's a transformative capability that affects people, processes, and power structures across the organization. Therefore, AI readiness must begin with strategic alignment across leadership.

Executive teams need to agree on what AI means for the business, which areas to prioritize for AI implementation, and how those priorities contribute to broader business goals.

• Clearly define the "why" of AI adoption: Clarify why you're investing in AI, what you aim to achieve, and how you will define success.
• Align with corporate values: Pursue AI not just for efficiency but for long-term value that respects employees, partners, and customers.
• Collaborate from early on: Leaders need to work together to define use case criteria, ethical guardrails, cross-functional roles, and investment thresholds early in the process.

For leaders to position AI not just as a means to increase efficiency, but as a strategic pillar for corporate growth and social contribution, is the first step toward organizational AI-readiness. From my involvement with many companies, I feel that if this "why" remains ambiguous, even with good technology, the company will go astray.

Change Management and Employee Engagement

AI readiness requires more than just training sessions; it demands intentional cultural transformation. Leaders need to openly communicate the "why" behind AI implementation, what it does, what it cannot do, and how it will impact roles and responsibilities.

• Employee partnership: Employees should feel they are partners in the transformation journey, not just unilateral recipients of automation.
• Build feedback mechanisms: Listen-based change management is crucial, including employee feedback, forums for discussion, and strategies that address both logical and emotional concerns.
• Address opposition: Employees may resist in ways that impede progress if they fear job loss or feel left out of the process.

Changing organizational culture doesn't happen overnight. It's an area that requires persistent effort: empathizing with each employee's feelings and concerns, engaging in careful dialogue, and building the future together.

I believe that true transformation is only achieved when top-down decision-making merges with bottom-up initiatives from the ground.

Concrete Steps to AI-Readiness and Keys to Success

The first step towards becoming AI-ready is to gather information to gain management's understanding and decision-making. After that, a phased approach is effective.

Phased Approach

For companies to truly leverage AI is a critical challenge that redefines the future of business operations. Therefore, management's understanding and decision-making are essential, and gathering the necessary information is the first step.

1. Develop operational rules and establish a governance system: AI technology evolves rapidly, and tool lifecycles are short, increasing risks if implemented without clear rules. For example, setting operational rules like "precautions for using AI tools" or "scope of data allowed for upload" can provide clear company policy to frontline users, creating an environment where they can confidently engage in AI utilization.
2. Foster organizational culture and develop human resources: Cultivate a culture that rapidly adopts new technologies like AI throughout the organization and enhance employees' AI literacy.
3. Improve operations with AI and develop use cases: Commit to transforming operations using AI and create specific use cases.
4. Integrate with data infrastructure and automate/autonomate: Ideally, the final step is to link with data infrastructure and aim for automation and autonomation of operations by AI agents.

By following these steps, I am confident that the effectiveness of AI adoption can be maximized.

Top-Down and Bottom-Up Approaches

Successful AI implementation requires both top-down and bottom-up approaches.

• Top-down: Company-wide issues such as data governance, organization-wide structural reforms, data infrastructure development, motivating AI utilization, and organizational reform related to HR evaluations should be driven top-down to ensure rapid progress.
• Bottom-up: It is crucial for frontline teams to identify and propose AI applications that solve their own operational challenges. Increasing successful use cases leads to gradual expansion across departments, energizing AI adoption throughout the company.

From my experience, companies where these two approaches are well-aligned tend to have higher AI adoption speed and retention rates. A virtuous cycle emerges where small successes at the frontline are recognized by management, leading to company-wide momentum.

Success Stories and Lessons Learned

A successful example within SoftBank is the generative AI tools created by frontline personnel deeply involved in operations. Tools like a summary tool for financial statements in the sales department or a tool for analyzing government bid specifications in the public works department have received extremely high praise because they are infused with on-the-ground knowledge.

• Keys to success: It's crucial not to decide on a tool beforehand but to create an environment where various tools can be tried to find the optimal one for the task and user. I recommend starting with general-purpose models like ChatGPT, Gemini, or Copilot. Also, a common factor in success is starting with an MVP (Minimum Viable Product) to validate value with minimal functionality, rather than launching a large-scale project immediately.
• Lessons from failure: Tools developed by support departments, assuming what frontline personnel "must be struggling with," often miss the mark on practical needs and quickly fall out of use. Additionally, organizations that attempt to create an "all-purpose agent" from the outset face a high risk of failure.

I believe that AI is born from repeated "trial and error." Grasping frontline needs, starting small, and continuously improving. This agile approach is the secret to successful AI adoption.

The Future AI Brings and Our Preparation

AI is already profoundly changing how we work. Operations will become increasingly autonomous, allowing humans to focus on more creative activities. Management decisions will also be made rapidly based on appropriate data, significantly enhancing the speed and flexibility of overall business operations.

AI Evolution and Business Transformation

While traditional AI optimizes and extends processes with a granularity and accuracy that humans cannot match, stimulating business growth, generative AI further dramatically increases individual productivity and enables the reorganization of work.

The World Economic Forum predicts a significant increase in AI-related jobs over the next six years, with, for example, a +110% growth in big data-related jobs and an +80% growth in AI/machine learning-related jobs.

• Automated tasks and new roles: Repetitive, routine administrative tasks are likely to be automated by AI agents, but this will create more interesting and specialized jobs, such as monitoring AI agents and workflows.
• Democratization of creativity: AI will become a powerful tool for content creation, making it easier for everyone to participate in creative activities.
• Importance of experts: Even as AI evolves, the role of human experts remains crucial. Areas requiring specialized knowledge, such as supervising AI systems, refining outputs, and intervening when problems arise, will persist.

I see AI as having the potential to augment our abilities and free us for more "human" work. AI is not perfect. However, when combined with human judgment, it has the power to create immeasurable value.

Empathetic AI Readiness

True AI readiness focuses not just on speed, scale, or competitive advantage, but on "impact." As AI shapes decisions in employment, healthcare, finance, education, and public life, companies must consider their moral responsibilities beyond just technical capabilities.

• Human-centered approach: It's important for AI systems to be explainable to users, for people affected by AI decisions to have clear ways to appeal, and for the deployment process to include human oversight, cultural sensitivity, fairness testing, and transparency by design.
• Employee consideration: Provide retraining, psychological support, and clear communication for employees whose jobs are changing, building AI as something that enhances human capabilities rather than replaces them.
• Ethical vendor selection: Evaluate whether the vendors and tools adopted promote transparency, user control, and long-term sustainability, and if they align with the company's values.

AI is not only asking us about efficiency but also about a deeper value: "empathy." In the AI era, empathetic leadership is an indispensable strength for building customer trust, employee loyalty, and resilience against regulatory and reputational risks.

Conclusion

AI is more than just a buzzword; it's a powerful force shaping the "present" and "future" of business. When implementing AI, many companies tend to focus solely on the technical aspects, but I believe what's truly important is the organization's overall preparation to maximize that technology – in other words, being "AI-ready."

Whether we can enjoy the infinite possibilities that AI brings depends precisely on our own preparation. A wide range of initiatives are necessary, from data organization to talent development and organizational culture transformation, but these are not always complicated.

Even a small step is fine.

Is your company ready to confidently navigate the seas of the future with the powerful "compass" of AI in hand?

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