While AI has become a central part of business operations beyond experimentation, the challenges of deployment continue.
Zogby Analytics’ investigation shows that by proving AI, most organizations have graduated from testing AI Waters to diving the Prime Minister with production-enabled systems. Despite this advancement, companies are still tackling the fundamental challenges of effectively training data quality, security, and models.
Looking at the numbers, it’s pretty impressive. Currently, 68% of organizations run and implement custom AI solutions. Companies put their money in places where they also have mouths, with at least 81% spending on AI initiatives annually. Approximately a quarter invests more than 10 million people each year, indicating that they have moved far beyond the “Let’s experiment” stage to a serious, long-term AI commitment.
This shift also restructures leadership structures. 86% of organizations have usually appointed someone to lead AI efforts, such as with a “Best AI Officer” title. These AI leaders are almost as influential as CEOs when it comes to setting a strategy, with 43.3% of companies saying that CEOs call AI shots and 42% give AI chiefs responsibility.
However, all AI unfolding journeys are not a smooth voyage. More than half of business leaders acknowledge that training and fine-tuning AI models are tougher than expected. Data issues continue to arise, causing headaches with quality, availability, copyright and model validation. We’ll explain how effective these AI systems are. Almost 70% of organizations report that at least one AI project is behind schedule and data issues are the main perpetrators.
As businesses become more comfortable with AI, they are finding new ways to use it. Chatbots and virtual assistants are still popular (55% adoption), but more technical applications are gaining the foundation.
Software development has now reached the top of the 54% list, along with predictive analytics for forecasting and fraud detection and 52% predictive analytics. This suggests that businesses are using AI to improve their core operations beyond flashy customer-oriented applications. Marketing applications have been the gateway for many AI deployment initiatives, but have not received much attention these days.
When it comes to the AI model itself, it focuses on generator AI, with 57% of organizations becoming a priority. However, many people combine these new models with traditional machine learning techniques to adopt a balanced approach.
Google’s Gemini and Openai’s GPT-4 are the most widely used and large-scale language models, but Deepseek, Claude and Llama also have strong shows. Most companies use two or three different LLMs, suggesting that a multi-model approach is becoming a standard practice.
Perhaps most interesting is the change in where businesses are running their AI deployments. Almost 9 in 10 organizations use cloud services, but at least some AI infrastructures are increasingly moving things back into the company.
Two-thirds of business leaders believe that non-cloud deployments provide better security and efficiency. As a result, 67% are moving AI training data to on-premises or hybrid environments, seeking greater control over their digital assets. Data sovereignty is a top priority for 83% of respondents when deploying AI systems.
Business leaders seem confident in their AI governance capabilities, with around 90% claiming that they can effectively manage AI policies, set the guardrails they need, and track data lineage. However, this confidence is in contrast to the practical challenges that cause delays in the project.
Data labeling, model training, and validation issues are still at fault. This suggests a potential gap between executive trust in governance frameworks and the day-to-day reality of data management. The lack of talent and difficulty in integrating with existing systems is also frequently cited for reasons for delays.
The era of AI experiments is behind us, and now it is a fundamental part of how companies operate. Organizations are investing heavily, restructuring their leadership structures, and finding new ways to deploy AI across operations.
But as ambitions grow, so does the challenge of implementing these plans. The pilot-to-production journey reveals the fundamental issues of data preparation and infrastructure. The resulting shift towards on-premises and hybrid solutions shows a new level of maturity by prioritizing control, security and governance.
As AI deployment accelerates, ensuring transparency, traceability, and trust is not just a goal, but a need for success. Confidence is authentic, but so is caution.
(Image by Roy Harryman)
Reference: Leng Zhengfei: The Future of Chinese AI and the Long Game of Huawei
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