AI has moved beyond experimentation to become a core part of business operations, but deployment challenges persist.
Research from Zogby Analytics, on behalf of Prove AI, shows that most organisations have graduated from testing the AI waters to diving in headfirst with production-ready systems. Despite this progress, businesses are still grappling with basic challenges around data quality, security, and effectively training their models.
Looking at the numbers, it’s pretty eye-opening. 68% of organisations now have custom AI solutions up and running in production. Companies are putting their money where their mouth is too, with 81% spending at least a million annually on AI initiatives. Around a quarter are investing over 10 million each year, showing we’ve moved well beyond the “let’s experiment” phase into serious, long-term AI commitment.
This shift is reshaping leadership structures as well. 86% of organisations have appointed someone to lead their AI efforts, typically with a ‘Chief AI Officer’ title or similar. These AI leaders are now almost as influential as CEOs when it comes to setting strategy with 43.3% of companies saying the CEO calls the AI shots, while 42% give that responsibility to their AI chief.
But the AI deployment journey isn’t all smooth sailing. More than half of business leaders admit that training and fine-tuning AI models has been tougher than they expected. Data issues keep popping up, causing headaches with quality, availability, copyright, and model validation—undermining how effective these AI systems can be. Nearly 70% of organisations report having at least one AI project behind schedule, with data problems being the main culprit.
As businesses get more comfortable with AI, they’re finding new ways to use it. While chatbots and virtual assistants remain popular (55% adoption), more technical applications are gaining ground.
Software development now tops the list at 54%, alongside predictive analytics for forecasting and fraud detection at 52%. This suggests companies are moving beyond flashy customer-facing applications toward using AI to improve core operations. Marketing applications, once the gateway for many AI deployment initiatives, are getting less attention these days.
When it comes to the AI models themselves, there’s a strong focus on generative AI, with 57% of organisations making it a priority. However, many are taking a balanced approach, combining these newer models with traditional machine learning techniques.
Google’s Gemini and OpenAI’s GPT-4 are the most widely-used large language models, though DeepSeek, Claude, and Llama are also making strong showings. Most companies use two or three different LLMs, suggesting that a multi-model approach is becoming standard practice.
Perhaps most interesting is the shift in where companies are running their AI deployment. While almost nine in ten organisations use cloud services for at least some of their AI infrastructure, there’s a growing trend toward bringing things back in-house.
Two-thirds of business leaders now believe non-cloud deployments offer better security and efficiency. As a result, 67% plan to move their AI training data to on-premises or hybrid environments, seeking greater control over their digital assets. Data sovereignty is the top priority for 83% of respondents when deploying AI systems.
Business leaders seem confident about their AI governance capabilities with around 90% claiming they’re effectively managing AI policy, can set up necessary guardrails, and can track their data lineage. However, this confidence stands in contrast to the practical challenges causing project delays.
Issues with data labeling, model training, and validation continue to be stumbling blocks. This suggests a potential gap between executives’ confidence in their governance frameworks and the day-to-day reality of managing data. Talent shortages and integration difficulties with existing systems are also frequently cited reasons for delays.
The days of AI experimentation are behind us and it’s now a fundamental part of how businesses operate. Organisations are investing heavily, reshaping their leadership structures, and finding new ways for AI deployment across their operations.
Yet as ambitions grow, so do the challenges of putting these plans into action. The journey from pilot to production has exposed fundamental issues in data readiness and infrastructure. The resulting shift toward on-premises and hybrid solutions shows a new level of maturity, with organisations prioritising control, security, and governance.
As AI deployment accelerates, ensuring transparency, traceability, and trust isn’t just a goal but a necessity for success. The confidence is real, but so is the caution.
