Organizations worldwide are racing to implement artificial intelligence. According to industry reports, 42% of organizations plan to invest in AI-related IT tools within six months, and 77% plan to implement AI initiatives within the next year. But there’s a critical problem most organizations haven’t considered: you can’t effectively deploy AI if you don’t know what IT assets you have.

As CEO of Teqtivity, an IT Asset Management (ITAM) solutions provider, I’ve seen firsthand how organizations struggle with this fundamental challenge. The rush to adopt AI technologies is creating a dangerous blind spot in enterprise technology management.

The Hidden Crisis in IT Asset Management

The current state of IT asset tracking across global organizations is alarming. Nearly 90% of IT administrators are concerned about devices or applications managed outside of IT, with 38% reporting they don’t have the ability or visibility to discover all applications in use. This lack of visibility becomes exponentially more dangerous when you add AI tools to the mix.

For international organizations, the complexity multiplies. Global IT teams must manage technology assets across different time zones, regulatory environments, and operational standards. When departments work in silos (for example: IT focusing on infrastructure, Finance tracking costs, HR managing employee equipment, and Security monitoring compliance), critical gaps emerge.

The financial impact is staggering. According to IBM’s Cost of a Data Breach Report, the average cost of a data breach has surged to $4.88 million, with 40% of these breaches involving data distributed across multiple environments. Meanwhile, 39% of organizations now spend 26-50% of their IT budget on licensing fees, up from 28% in 2024.

Why AI Makes Asset Management More Critical

Artificial intelligence implementations require unprecedented visibility into your technology ecosystem. AI tools need to integrate with existing systems, access data across platforms, and operate within security frameworks. Without proper IT Asset Management, organizations face several risks:

• Shadow AI Proliferation: Just as 88% of IT admins report concerns about shadow IT, unauthorized AI tool adoption is becoming a significant governance challenge. Employees are implementing AI solutions independently, creating security vulnerabilities and compliance gaps.
• Resource Misallocation: 85% of IT administrators want a single tool for IT management, yet 26% still use 11 or more tools. Adding AI solutions to an already fragmented technology stack without proper asset management creates operational chaos.
• Security Vulnerabilities: With 46% of organizations having experienced a cyberattack, 33% of those being AI-generated attacks, tracking every technology asset becomes critical for security teams.

International IT Asset Management Best Practices

Global organizations require sophisticated ITAM strategies that address cross-border complexities. Effective international IT Asset Management involves several key components:

• Unified Asset Visibility: Organizations need real-time tracking of hardware, software, and cloud resources across all locations. We recently worked with a client who improved their inventory accuracy from 13% to 95% within one month by implementing proper ITAM processes.
• Cross-Departmental Integration: Modern ITAM solutions must bridge departmental silos. When IT Asset Management systems integrate with HR onboarding workflows, Finance budgeting systems, and Security monitoring tools, organizations achieve better outcomes across all functions.
• Automated Compliance Tracking: For international operations, automated compliance monitoring ensures adherence to different regional regulations while maintaining operational efficiency.
• Lifecycle Management: Proper asset lifecycle management helps organizations optimize their technology investments, reduce waste, and maintain security standards across global operations.

Building the Foundation for AI Success

Organizations that establish robust IT Asset Management practices before implementing AI initiatives gain significant competitive advantages. While many leaders believe AI is outpacing their organization’s ability to protect against threats, companies with mature ITAM practices are turning this challenge into an opportunity.

Proper asset management enables:

• Strategic AI Deployment: Understanding your current technology landscape helps identify the best integration points for AI tools. Rather than adding AI solutions randomly across departments, organizations with complete asset visibility can map AI capabilities to existing workflows and ensure new AI tools complement rather than conflict with current systems. This prevents the IT sprawl that already affects teams.
• Risk Mitigation: Complete asset visibility supports security teams in protecting against AI-driven threats. With AI-generated attacks becoming more common, security teams need real-time visibility into every endpoint, application, and data flow. ITAM provides the foundation for security models by ensuring every asset is accounted for, monitored, and properly configured. When security teams know exactly what technology they have and where it’s deployed, they can respond to threats quickly and effectively.
• Cost Optimization: Clear visibility into technology spending helps organizations make informed decisions about AI investments. With licensing fees consuming an increasing portion of IT budgets, organizations cannot afford to add AI tools without understanding their current technology spend. ITAM enables teams to identify underutilized resources, consolidate redundant tools, and allocate AI budgets strategically.
• Compliance Assurance: Proper asset management supports regulatory requirements across different markets. As AI regulations evolve globally, organizations need comprehensive documentation of how AI tools access, process, and store data. ITAM provides the framework that compliance teams require to demonstrate responsible AI deployment across international operations.
• Operational Excellence: Mature ITAM practices enable the cross-departmental collaboration required for AI success. When IT, Finance, HR, and Security teams share real-time asset data, they can coordinate AI deployments that serve business objectives rather than departmental silos. This collaborative approach transforms AI from a technology experiment into a strategic business enabler.

Organizations that invest in foundational ITAM practices before pursuing AI initiatives are positioning themselves for sustainable competitive advantage.

The Path Forward

As AI reshapes every industry, organizations with complete asset visibility, cross-departmental alignment, and proactive technology management will set the pace while others struggle to catch up.

Before your organization makes its next AI investment, ask yourself: Do you know exactly what technology assets you have, where they are, and how they’re being used? If the answer is no, you’re not ready for AI. But with the right IT Asset Management foundation, you can position your organization to succeed in the AI gold rush while maintaining security, compliance, and operational excellence.

For more information about IT Asset Management solutions for global organizations, visit www.teqtivity.com.

Chinese Big Tech player Baidu (NASDAQ: BIDU) has announced upgrades to its search engine, adding new artificial intelligence (AI) functionalities into the service, its biggest improvement in over a decade.

According to a CNBC report, Baidu is opting to innovate its search engine to remain viable, as studies highlight a trend of users turning to AI-powered chatbots for answers.

Baidu’s latest changes to its core search product will allow users to enter over a thousand characters in the search box. Previously, users were limited to only 28 characters, reducing search precision and requiring keyword prioritization.

Going forward, users can conversationally ask questions on the search engine, akin to how they interact with chatbots. Furthermore, Baidu is improving its voice search and image prompts.

Lastly, Baidu’s biggest upgrade is the integration of its AI chatbot into the search product. The integration will allow users to use AI to generate text, images, and video on Baidu Search.

Morning Star strategist Kai Wang disclosed that the changes to the Search product are designed to mirror how consumers interact with mainstream AI products. Baidu’s search users have fallen, with several users opting for AI chatbots for their search requirements.

Baidu Search faces stiff competition from China-based AI heavyweights like DeepSeek and Tencent (NASDAQ: TCTZF). Furthermore, short video platforms are turning their gaze to AI Search, slashing off a significant chunk of Baidu’s market share.

Despite the new pressure on Baidu from its rivals, the company took the lead with AI back in 2023 with the release of its Ernie Bot chatbot. In less than six months, Baidu racked up 100 million Chinese users to lead its peers, announcing several AI products to maintain its headstart in the local scene.

However, new entrants are catching up with Baidu with their range of AI products. The stiff competition has sent Baidu stock inching up by only 2.5% since the start of the year, while AI heavyweights Alibaba (NASDAQ: BABA) and Tencent have gained 30.5% and 20% respectively in the same window.

Google racing to innovate Search

Outside of China, Google Search (NASDAQ: GOOGL) is also facing challenges driven by the rapid adoption of AI chatbots. To stay ahead of the curve with emerging technologies, Google has rolled out new AI policies for its Search product, presenting AI summaries for queries ahead of website links.

Furthermore, Google says it integrates its AI mode directly into Search, allowing users to improve their queries and get conversational responses. The U.S.-based search giant has unfurled its independent AI chatbots, providing stiff competition to traditional AI companies like OpenAI and Anthropic.

Beijing schools to integrate AI into learning curriculum

For each academic year, pupils across primary and middle school will have at least eight class hours on AI. Upon full rollout, there are suggestions that the number of hours may increase, matching the hours in secondary schools.

The curriculum will attempt to achieve three key objectives. The Beijing Education Commission will focus on AI awareness and cognitive abilities, AI applications and innovation capabilities, ethics, and social responsibility.

The new curriculum attempts to step up from basic IT knowledge to promote critical thinking skills in pupils. Furthermore, the report notes that AI skills will form part of the comprehensive assessments of Beijing students.

Schools in the capital city of China will be free to teach AI courses independently or merge them with other subjects. The report name-checks information technology, science, and emerging technologies as potential courses for schools to integrate with AI.

“We expect that under the new guidance, an integrated AI educational innovation scenario from primary schools to middle schools could be built, which will better help the education sector seize the opportunities brought by the AI technological reforms,” Li Yuxin, principal of Beijing Bright Horizon Foreign Language Primary School, said.

Pundits have hailed the curriculum for aligning with the goals of general AI education, given its tailor-made design for elementary students. In May, the Chinese Ministry of Education launched new guidelines for AI use in classrooms, prohibiting students from submitting AI-generated text as their original work.

AI to become mainstay in global classrooms

Across several jurisdictions, regulators are bracing for the adoption of AI tools in classrooms. Technology firms are leading the charge via launching innovative products for students and teachers, with Khanmigo and Speechify emerging as frontrunners.

However, Japan’s regulators are limiting the use via key guardrails, including age restrictions and a blanket ban against their use in examinations. The United Nations also urges tighter AI restrictions in schools, citing a raft of ethical considerations, including age restrictions and the emotional well-being of younger students.

In order for artificial intelligence (AI) to work right within the law and thrive in the face of growing challenges, it needs to integrate an enterprise blockchain system that ensures data input quality and ownership—allowing it to keep data safe while also guaranteeing the immutability of data. Check out CoinGeek’s coverage on this emerging tech to learn more why Enterprise blockchain will be the backbone of AI.

Watch: AI is for ‘augmenting’ not replacing the workforce

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Chinese companies are doubling down on open-source artificial intelligence-powered models as part of a broader push to bring fast-evolving AI technology to more businesses and developers worldwide, and bolster its application in a diverse range of fields.

Experts said the open-source approach will lower the threshold for the development and application of AI, greatly reduce computing power costs, and foster the sharing of AI tech around the world, as well as boost collaboration and innovation.

The recent progress in open-source large language models has showcased China”s growing technological prowess and open attitude in the AI domain, given that Chinese AI startup DeepSeek’s open-source models have taken the world by surprise, they added.

Tech heavyweight Alibaba Group has stepped up efforts to enable broad access to its AI technology and innovations by releasing large language models from its Qwen family as open-source, and boasting China’s largest AI open-source community platform, ModelScope.

The company has made more than 200 generative AI models open-source in recent years. The models have multimodal capacities and can process and generate various types of content, covering text, images, audio and video.

ModelScope, which was launched in November 2022, hosts over 70,000 open-source models, and the user base has expanded from 1 million in April 2023 to 16 million as of June 30, serving 16 million developers from 36 countries around the world.

It supports developers in experiencing, downloading, fine-tuning, training and deploying models. Various types of open-source AI models have been included in the community.

“We aim to simplify and reduce the cost of developing, customizing and deploying AI models for developers and corporations, thereby enabling the creation of revolutionary AI applications that have a positive impact on society,” said Zhou Jingren, chief technology officer at Alibaba Cloud Intelligence, emphasizing they are committed to making AI models more accessible and easier to use.

Baidu Inc has recently open-sourced its multimodal LLM Ernie 4.5 series, consisting of 10 distinct variants. The model family includes mixture-of-experts (MoE) models with 47 billion and 3 billion parameters, the largest model having 424 billion parameters, alongside a 0.3 billion dense model. The Ernie 4.5, launched in March, is Baidu’s multimodal foundational model.

The company said the MoE architecture has the advantages of enhanced multimodal understanding and improved performance on text-related tasks. All models are trained with optimal efficiency using the PaddlePaddle deep learning framework, which enables highper-formance inference and streamlined deployment.

Experimental results show that the models achieve state-of-the-art performance across multiple text and multimodal benchmarks, particularly in instruction following, knowledge memorization, visual understanding and multimodal reasoning.

Zhu Keli, founding director of the China Institute of New Economy, said the open-source approach adopted by a string of Chinese AI companies will lower the technical threshold, speed up the popularization of AI tech across various sectors including automobiles, manufacturing, finance and education, and allow more enterprises and developers to participate in AI research and development.

Zhu believes technological innovation is unstoppable, and international cooperation serves as an important way to promote the development of AI tech, adding that China’s open and inclusive attitude helps promote the advancement of the global AI industry.

“Open source will allow resource-constrained startups, small businesses and entrepreneurial developers to access cutting-edge AI tech and build their own models more cost-effectively,” said Pan Helin, a member of the Expert Committee for Information and Communication Economy, which is part of the Ministry of Industry and Information Technology.

Pan said it will accelerate AI technological advancements and breakthroughs by enabling global developers to create customized industry-specific models, and foster a more competitive and diverse AI ecosystem.

Chinese AI companies have the ability to take the lead in global AI innovation, as they have sought an alternative AI development approach that emphasizes efficiency and open-source collaboration — which is different from their US counterparts — while reshaping the global AI landscape, Pan added.

The market size of the nation’s AI sector will reach 1.73 trillion yuan ($241.2 billion) by 2035, accounting for 30.6 percent of the global total, said market research firm CCID Consulting.