Our research shows businesses are excited about AI’s potential, but they’re also hitting some serious roadblocks. On one side of the conversation, there’s hype about how AI (artificial intelligence) will revolutionize everything. On the other, skeptics warn AI is a disaster waiting to happen. As usual, reality lies somewhere in between.
Finding that reality was the motivation behind our State of AI in operations report. In early 2025, we surveyed over 1,000 managers and business leaders to learn how AI is actually impacting their operations and what their real life wins and obstacles look like. The good news? 73% of businesses are already using AI or actively planning to implement it, and the businesses using it are reporting it is having even better results than they expected.
However, many businesses also face significant challenges in implementing AI technologies. Key obstacles include:
Data privacy and security concerns: Protecting sensitive data and complying with regulations.
Lack of knowledge and skills: Uncertainty about AI development and a shortage of talent.
Accuracy, bias, and ethical issues: AI bias, ethical implications, and opaque algorithms undermining trust.
Integration hurdles: Difficulty integrating AI tools into legacy systems and workflows.
Internal resistance: Distrust of AI and fear of job losses among staff.
In this article, we’ll examine how these AI adoption challenges are showing up in real life and learn from successful companies what strategies might help overcome them.
Learn about the state of AI in 2025
Read the full report1. Data security and privacy
The problem: How can we use AI without risking sensitive data?
Over half of survey respondents cited data privacy and security concerns as their top barrier to AI adoption. Qualitative data backs this up. Many leaders aren’t sure that AI really will keep their sensitive data safe. While businesses want to keep up with innovation, they need to balance it with strict data privacy safeguards.
Highly regulated industries like healthcare and finance are especially cautious, wanting to ensure AI can work around their data regulations and compliance requirements. Data protection laws like GDPR in Europe and CCPA in California make data privacy and security a nonnegotiable.
As one manager put it, “All we have is just some privacy concerns. We're afraid that the more information we put into it the more information that the AI will have about our customers.” The problem is AI systems need quality data and large datasets to be effective for businesses.
Leaders report internal IT teams often put AI projects on hold until vendors pass stringent security checks. Companies worry about confidential data leaking or being misused by AI algorithms. “I think they need assurances that it is safe and will protect our data,” reported one respondent.
High-profile incidents haven’t helped: for example, Samsung banned employees from using ChatGPT after engineers accidentally uploaded sensitive source code to it. Clearly, if an AI system can’t ensure privacy and security, many businesses won’t even get past the pilot stage.
“The tools currently available don't meet our security standards sufficiently.”
The solution: Use business-grade AI tools and build in privacy from day one
Fortunately, the right strategies can overcome data privacy concerns. The first step is to choose AI technologies that are enterprise-ready and have strong data management practices. Many enterprise AI tools promise no training on customer data and assurances like encryption to keep information safe.
For example, some platforms let companies deploy machine learning models on their own cloud or on-premises, ensuring data never leaves their control, though the cost of these services can make them inaccessible to non-enterprise businesses. Even popular generative AI services now offer privacy options – OpenAI’s ChatGPT, for instance, allows business users to opt out of contributing data to the training set on paid plans. That way, if your team uses an AI chatbot, your inputs won’t suddenly resurface in someone else’s output.
If you need something more adaptable than a ChatGPT interface and more cost-effective than Enterprise-priced tools like Writer, you can use a platform like Glide that provides managed AI. With managed AI, your specific AI models are selected by the company for security and effectiveness, and you can benefit from their high-security Enterprise plans without paying for every single subscription yourself. This gives you the benefits of advanced AI without the headache of building all the privacy infrastructure yourself.
In the end, though, security concerns are a hurdle to clear but not a major ongoing concern for businesses that want to use AI. Satisfying security concerns is a deal-breaker, but once businesses are satisfied that standards are being met, our survey showed that attention shifts to other factors.
Once companies see that privacy safeguards are in place and standards are met, they often gain the confidence to move forward. This way, you can take advantage of AI’s benefits while mitigating risks – keeping customer data safe, staying within regulations, and maintaining the trust you’ve built with users.
2. Lack of knowledge and skills
The problem: We want to adopt AI, but where do we even begin?
AI may be a hot topic of conversation, but it’s still new territory for many businesses. A significant challenge is simply a lack of AI knowledge or expertise – not knowing how to start, which tools to use, or how to integrate AI into existing decision-making processes.
19% of all survey respondents said they wanted to implement AI but didn’t know how, and it was the second-highest reported barrier to adoption, cited by 44% of respondents. One leader surveyed admitted, “I don't know where to begin.”
As businesses move further into the AI adoption process, this knowledge gap often becomes even more pronounced. For example, when rolling out AI agents, 54% of businesses cited lack of knowledge as the top barrier. “It’s challenging knowing which tools are best for our specific needs,” one respondent wrote. “The scope is so wide,” another explained, “It’s overwhelming to businesses.”
For instance, when rolling out more complex AI applications like AI agents, over half (54%) of businesses said lack of knowledge was their #1 obstacle. It’s challenging knowing which tools are best for our specific needs,” one respondent wrote. “The scope is so wide,” another explained, “It’s overwhelming to businesses.”
These results looked very different when analyzed by industry, business size, and team. Smaller companies feel knowledge barriers are especially hard: 30% of small businesses want to use AI but haven’t figured out which tools or strategies to use, while just 12% of large enterprises say the same.
Unsurprisingly, the tech industry is way ahead of the AI adoption curve—especially highly customizable tools like AI agents. Less tech-forward industries like transportation & logistics and manufacturing report struggling to find the right tech—19% and 17%, respectively, report that they have plans to implement AI but haven’t yet been able to identify which tools they will use.
There’s also an internal skills gap–IT teams are using AI at a significantly higher rate, likely due to better AI literacy and curiosity about tech like machine learning.
The solution: Close the AI literacy gap – learn by doing, and bring in the experts
The companies that succeed with AI tend to be the ones that are willing to learn in a hands-on way. Businesses behind the curve were mostly reading the news and didn’t have much actual experience using AI applications. However, leaders who were successfully rolling AI out to their teams were more likely to report their teams were trying out AI tools in real life, using hands-on experimentation to build understanding and comfort.
They were also much more likely to report using tutorials, videos, and online courses to learn about AI and better understand the basics of model development, data preparation, and AI ethics. Taking an AI Certification Course is a great way to overcome discomfort with new tech and gain a deeper understanding of how it can help your business.
Additionally, don’t be afraid to bring in outside help. If you don’t have an in-house AI expert, consider partnering with a consultant or vendor specialized in AI projects. The number one way businesses reported deploying AI agents was by using a third-party consultant or vendor for AI implementation.
These agencies and specialists have technical knowledge they can bring to the table. They can help with things like machine learning, data management, and AI development frameworks while also connecting your AI to existing software with API integrations and webhooks. Combine their expertise with your deep understanding of your business, and you can more easily deploy tailor-fit solutions that will benefit your company. You can hire an AI expert to get AI deployed faster and more effectively than you could on your own.
3. Accuracy, bias, and ethical concerns
The problem: What if AI is ineffective or biased?
We’ve all seen AI go hilariously (or harmfully) wrong. Early generative AI often produced unreliable outputs, from nonsensical chatbot replies to confidently incorrect answers – not exactly inspiring trust. In our survey, 46% of businesses said AI accuracy and reliability is a significant concern holding back adoption. “We don't know enough about AI yet,” said one respondent. “It hasn't been fully tested.”
Beyond just making mistakes, there’s the issue of AI bias. 32% explicitly cited concerns about AI bias and ethical implications as a reason they hesitated on AI. Algorithmic bias can cause real damage to businesses, especially in sensitive tasks like hiring. One infamous example was Amazon’s experimental AI recruiting tool, which had to be scrapped in 2018 because it discriminated against women.
The AI had been trained on ten years of resumes, most from men—reflecting the tech industry’s gender imbalance. The result? The algorithm taught itself that male candidates were preferable, and it started automatically rejecting women’s resumes. Amazon’s hiring team discovered all female candidates were being down-ranked – a clear case of algorithmic bias that opened them up to legal and ethical trouble.
Many AI systems are black boxes – their internal logic isn’t transparent, so when they do make an odd decision, it’s hard to explain why. This lack of algorithmic transparency and explainability (often called the XAI problem) makes it difficult for humans to fully trust AI outcomes.
The solution: Keep humans in the loop and demand transparency.
The best antidote to AI accuracy and bias concerns is human oversight, transparency, and accountability. Instead of handing complete autonomy to AI, many companies are choosing to use AI as a supportive tool – with a human final check on important decisions. This “human-in-the-loop” approach means AI can do the heavy lifting on data processing or preliminary analysis, but people still review the AI’s work and can override it if something looks off.
For instance, an AI might scan resumes and rank candidates, but a human recruiter double-checks those rankings. Better accountability for AI tools can help mitigate bias and combat ethical concerns. While many businesses want to work towards fully automated agentic AI, only 26% of businesses report using fully autonomous AI agents. The rest report varying levels of human supervision and using agents that are easily audited. This approach can be called
You can also combat AI error and bias by strategically choosing how and where you have AI performing tasks. There’s lots of rote, repetitive work that can be automated with AI without really introducing much risk via intelligent automation. For example, if AI is used to input messy data into a spreadsheet, it’s unlikely to introduce AI bias into the mix, and it’s likely to have a lot less human error than manual data entry.
One great example of this approach is this AI resume screener. Rather than making the selection for the hiring manager, the resume screening agent just speeds up the process of cleaning and organizing applicant data. It provides scores based on how closely the candidate’s resume matches the job description, but the results are fully auditable, and the ultimate decision still lies in the hands of the hiring team.
4. Integration into existing systems
The problem: How do we fit AI into our current tools and processes?
Every business has its own mix of software, workflows, and data pipelines – and integrating AI into that mix can be a massive headache. In fact, “integration with our systems” was ranked the #1 factor businesses value in an AI vendor (even above output quality, security, and cost).
In our survey, businesses had a lot to say about integration challenges. Many companies have legacy systems (older software or databases) that weren’t designed with AI in mind. You can’t just plug a new AI tool into a decades-old ERP system, and integrating data from multiple sources (often in inconsistent formats) can be a challenge.
“Our information and assets are strewn across so many databases. We never know where to find the resources we have and need -- we just don't know where to look. It seems like AI could help us find it, but I don't know how.”
If AI can’t connect to your data sources and tech stack, it’s probably not going to provide much value. When asked about what they wanted to gain from the use of AI, Enterprises ranked better insights and decision-making as a priority, while small businesses sought efficiency gains. Neither of these is going to be accomplished with a siloed AI interface like ChatGPT. Businesses need their AI tech to use their data so they can gain insight from it, and they need to be able to insert AI into the processes that need to become more efficient.
The solution: Give your team the ability to create more customized AI tools
Customization is key to effectively integrating AI with business systems. However, not all businesses can afford the engineering time required for AI development. That might be why the AI tech businesses in our survey reported to have the highest transformational impact were the custom solutions built using no or low code platforms.
Integration challenges might be complex, but the right technology and strategy can help. One valuable insight from successful AI adopters in our survey is the importance of customization. The businesses in our survey reported to have the highest transformational impact were the custom solutions built customized AI tech using no or low code platforms.
Many companies are using no code platforms to build bespoke AI solutions without starting from zero. These platforms allow you to create AI applications with minimal coding, and they often come with APIs and integrations for popular databases, CRMs, etc. By using a no-code approach, even a small team can create an AI workflow that pulls data from your internal systems, processes it, and outputs results back into your dashboards – all without overhauling the legacy systems themselves.
Our research found that teams using no-code tools were able to deploy more AI agents faster, and they trusted these agents more because they were closely aligned with their needs.
Businesses can hire an agency or expert to help them build and customize their AI tech, and teams with some technical expertise can even create their own tools. This might be why our survey found that businesses using no code to build AI agents saw other advantages as well. They were significantly more likely to be using more agents when they gained the ability to create their own, and they were also more likely to trust their agents, with more respondents reporting their AI agents were fully or mostly autonomous.
5. Internal resistance and generational divides
The problem: Our people aren’t on board with AI.
Many businesses report internal resistance to AI adoption – from employees worried about job security to older executives unsure about using a new and unfamiliar technology.
“We need to get our upper management convinced about AI.”
Some respondents reported that their upper management, especially older leaders, were more resistant to AI. Younger employees and those in tech roles might use AI tools daily and be comfortable with them, while others are more hesitant. One manager explained, “I know how to use AI, I'm a millennial. I use ChatGPT almost daily and Copilot. It's our older employees who have no clue how to use it for efficiency.”
In the other direction, less senior employees also resisted AI because they feared for their jobs—13% of survey respondents mention job loss as a concern.
Managers express the most concern about being replaced by AI and are the most distrusting of AI as a result. “I'm trying to be a team player but I can easily see how this will replace a lot of people's jobs, a lot of friends that I've made at work,” explained one team manager. Managers have a much stronger preference for human-initiated or supervised AI, while Directors and above have a stronger preference for autonomous or semi-autonomous agents. Lack of clarity creates an atmosphere of distrust: instead of seeing AI as an innovation, employees see a threat.
The solution: Build a culture of openness, reassurance, and collaboration around AI
Overcoming internal resistance requires empathetic leadership and clear communication. Be transparent: explain why the organization is adopting AI – whether it’s to improve customer service, streamline operations, or help make better decisions. For example, the World Economic Forum recommends emphasizing that AI will not replace humans but enrich their work, and adopting a human-centric approach to AI use.
While roughly 7% of the businesses surveyed reported they did want to reduce their workforce using AI, the vast majority didn’t see that as a goal. AI does automate work but it doesn’t have to make employees obsolete. Most businesses emphasize goals like productivity and growth over cost-cutting and want to use AI to increase the productivity of their existing workforce rather than replacing them.
“Improve revenue. Higher profit margin. Proficiency without laying off staff.”
It also helps to share positive examples and success stories of AI creating opportunities. AI adoption can lead to business growth that actually creates more jobs or new roles. One survey respondent noted that after implementing AI, they realized “we need to hire a dedicated team of individuals who solely work on AI.”
Working with Glide customers, we’ve frequently seen the point-person building AI solutions for their company make full career pivots based on their work creating AI applications. Once they become the internal expert in AI tech they become much more valuable to the company.
Another key is education and involvement. Offer training sessions or workshops to demystify AI for employees at all levels. Encourage teams to experiment with AI in a low-stakes environment so they gain familiarity. Involve employees in the AI implementation process. If you’re rolling out an AI system that will affect a certain department, get representatives from that department to provide input in the design, to beta test the system, and to champion it among their peers. When teams feel a sense of ownership and are more likely to give the AI a fair chance.
Move to the front of the AI adoption curve
It’s clear that while AI is on the rise, challenges continue for many businesses. Data privacy, talent gaps, ethical risks, integration difficulties, and cultural resistance can all slow down an AI initiative. However most businesses are still incredibly optimistic about AI’s potential.
AI can drive innovation, uncover insights from data, enhance decision-making processes, and free humans from mundane tasks to focus on creative, strategic work. It can do that best when it’s genuinely integrated and fits your systems like a glove. The businesses really succeeding are the ones that are creating highly customized, deeply integrated AI solutions for their teams. AI agents, no code platforms, and intelligent automation tools that make teams more efficient are all showing huge impacts on businesses.
Using a tool like Glide gives you much more control and flexibility over your AI development. You can create AI agents to relieve pain points in your processes and deeply integrate them with your business data.
AI is moving fast. Glide can give your business the tools it needs to compete.
