Biggest AI Adoption Challenges and How To Overcome Them

"Change is the law of life. And those who look only to the past or present are certain to miss the future." – John F. Kennedy  

As companies race to operationalise artificial intelligence (AI), Kennedy’s words have never rung more true. Change is now non-negotiable for businesses across all industries. But how quickly you adapt to and manage it in your organisation will be the difference between success and failure.  

The pace of AI adoption is skyrocketing, with 78% of companies now leveraging AI in at least one business function. Generative AI, in particular, has emerged as a mainstream tool, with 71% of organisations integrating it into their operations. These numbers aren’t just impressive; they’re a wake-up call. AI is not a passing trend. It boosts efficiency, personalises customer experiences and turns data into actions.  

But in practice, many companies are struggling to get started. From tricky integrations to sceptical teams, adopting AI comes with its fair share of hurdles that not only make implementations harder, but make teams delay even looking into them. 

In this blog, we’ll explore the top six barriers to AI adoption and, even better, how to overcome them. Whether you're battling legacy systems or staff skills gaps, we’ve got the insights and strategies you need to get AI working for your business. Ready to take the plunge?

1. Waiting for the Perfect Plan 

AI is evolving fast—and if you wait for the perfect strategy, you might never get started. Many businesses are holding back because they think they need a flawlessly detailed roadmap before taking action. But in reality, AI adoption favours momentum over perfection.  

This quest for perfection becomes a barrier in itself—slowing progress, stalling innovation and creating a culture of hesitation. In fast-moving spaces like AI, over-planning can be just as risky as under-planning.  

That doesn’t mean diving in blindly. A clear vision still matters—people need to understand how AI connects to their daily work and what success looks like. Without that, teams can feel lost or disconnected from the effort. But that vision doesn’t have to be perfect. What matters more is building a direction that leaves room to experiment, evolve and learn along the way. 

It’s not about having every detail figured out—it’s about having the confidence to move forward, even when the path isn’t fully paved. 

How To Overcome It: Make It an Executive Priority—and Then Empower Action 

AI success starts at the top. Your executive team needs to treat AI not as a side initiative, but as a strategic priority—with the leadership, urgency and visibility to match. That means creating space for experimentation, encouraging teams to explore practical use cases and giving them a mandate to take action—even when the plan isn’t perfect. 

Start by identifying high-impact opportunities where AI can deliver early wins. These don’t need to be sweeping changes—just meaningful improvements to existing processes, decisions or workflows. Then empower cross-functional teams to test, learn and iterate. 

At the same time, make responsibilities clear. Who’s accountable for moving projects forward? Who’s tracking outcomes? Consider forming an AI task force to coordinate efforts, ensure alignment with business goals and keep momentum strong across departments.

2. Poor Data Quality and Silos 

Implementing AI with poor data quality is a recipe for unreliable outcomes. Data that's incomplete, outdated or inconsistent can skew results, leading to insights that are more misleading than enlightening. This wastes resources and, worse yet, when teams get burned by early inaccuracies, they’re less likely to trust the technology again. Strong data quality is the foundation for adoption, accuracy and long-term success. 

Sometimes, it’s not just about bad data, it’s not having enough of the right data. If key workflows haven’t been digitised, or critical steps aren’t being captured, your AI has nothing to learn from. This creates blind spots in analysis and prediction. Plus, manual processes increase inaccuracies, further contributing to the “garbage in, garbage out” problem.  

Data silos present another significant hurdle. When departments hoard information, it blocks the broader view AI needs to be most effective. For instance, if your sales and customer service teams don't share data, you miss out on valuable insights into customer behaviour and preferences, resulting in redundant efforts, duplicated costs and fragmented processes.

How To Overcome It: Prioritise Data Readiness 

Start by cleaning and consolidating your data. This means eliminating duplicates, correcting errors and ensuring consistency across different sources—including standardisation in field names and labels where possible. AI depends on structured, high-quality data, so taking the time to refine your datasets will pay off in better decision-making and more accurate predictions. 

Additionally, implement strong data governance policies. Define who owns the data, who can access it and how it should be maintained. Without these guardrails, data inconsistencies and security risks can undermine your AI initiatives and open the door to compliance issues. 

Another key action is to look at the systems that will surround your AI tools. If your data still relies on manual entry or disconnected workflows, you’ll always be playing catch-up. AI works best when paired with an integrated, modern tech stack—one that reduces human error, breaks down silos and ensures your data flows freely across departments.

3. Skills Gaps and Missing Expertise 

The rapid evolution of AI makes skill gaps inevitable—particularly in industries that haven’t traditionally relied on advanced digital tools, or where teams are still learning the differences between AI, machine learning and other emerging technologies. Even if your team has some foundational knowledge, keeping up with the latest advancements demands continuous learning and development.  

To make matters worse, AI talent is in high demand and short supply. Hiring experts can be both challenging and expensive with long recruitment processes, rising salary expectations and increased competition for top candidates.  

That said, not every business needs a team of data scientists to succeed with AI. Today’s most effective AI tools are designed for real users—not just technical experts. You’ll need to commit to ongoing education allowing your existing staff to use AI confidently with minimal upskilling. Let’s take a look at that in more detail. 

How To Overcome It: Bridge Skills Gaps With Training and Intuitive Tools 

While the skills gap can be an AI adoption challenge, it’s far from insurmountable. Offering training programmes can help your employees gain foundational AI skills—think literacy, automation basics and responsible AI use. Encourage a culture of continuous learning, where employees feel empowered to explore new technologies and apply them in their day-to-day work. Internal workshops, online courses and certifications can all be valuable tools for upskilling your team. 

For more advanced AI needs, hiring AI specialists might still be necessary. Data scientists, machine learning engineers and AI strategists can provide the technical expertise needed to drive AI projects forward. Partnering with universities, AI consultancies, or hiring AI-as-a-service providers can also fill critical gaps without long-term overhead. 

Most importantly though, choose AI tools that are built for real users and reduce the need for deep technical expertise. Look for intuitive interfaces, natural language query capabilities and out-of-the-box functionality that reduces the need for custom development. And don’t overlook vendor support: the right partner will provide onboarding, training materials and best-practice guidance to help your team build confidence alongside capability.

4. Legacy Systems and Outdated Infrastructure 

Many businesses still rely on legacy systems and aging on-premise infrastructure that creates considerable AI implementation challenges. These systems have no native AI capabilities and lack the speed, scalability and flexibility required to support AI applications effectively. As a result, slow performance, limited data accessibility and frequent downtime become common roadblocks to progress. 

Integrating AI solutions with legacy systems can also be a technical nightmare. Compatibility issues may arise, requiring extensive customisation or even complete overhauls of existing infrastructure. These integration challenges lead to increased costs and stretched project timelines, causing frustration among stakeholders and delaying ROI. 

What's more, maintaining legacy systems alongside new AI technologies can strain your IT resources. Your team may find themselves juggling multiple platforms, each with its own set of requirements and maintenance needs. This complexity can divert attention from strategic initiatives, as your IT staff become bogged down with keeping the lights on rather than driving innovation. 

How To Overcome It: Invest in Modern, Cloud-Based Technology Infrastructure 

As you can see, not having the right tech foundation in place is one of the biggest challenges of AI adoption. The first step to overcoming it is investing in AI-ready technology to ensure a smoother transition and better long-term results. 

Cloud-based systems are the smartest move, providing the flexibility and scalability needed for AI applications. With cloud infrastructure, you can more easily deploy, integrate and scale AI tools while keeping data accessible across your organisation, and get the latest updates and innovations easily. Evaluate your technology stack and ensure it supports AI adoption without requiring constant workarounds. 

Interoperability—the ability for systems to exchange and use information—is another key factor. Your AI tools should integrate well with your existing software, databases and workflows. Look for tools that integrate natively with your core systems—enterprise resource planning (ERP),  customer relationship management (CRM), supply chain management (SCM) solutions and beyond—so AI becomes a connected part of your workflow, not a bolt-on burden. The right vendors will offer native capabilities, pre-connected applications and robust support to help minimise disruption.

5. Hesitation and Resistance To Change

AI projects often stall not because of the technology itself, but because of human hesitation.  Resistance to change is a natural reaction, especially when it involves unfamiliar technologies like AI. Many of your employees may fear that AI will replace their jobs, or at least feel unequipped to work with new systems, leading to anxiety and pushback against adoption efforts. 

This hesitation isn't limited to frontline staff; management may also exhibit resistance due to unclear ROI, fear of disruption or a general discomfort with emerging tech. Without strong support from the top, AI initiatives can struggle to gain the necessary momentum, resulting in half-hearted implementations that fail to deliver promised benefits. 

Furthermore, resistance to AI adoption can manifest in subtle ways, such as passive non-compliance, low usage rates, skipped training sessions or lack of engagement with new systems. These behaviours can undermine the effectiveness of your AI initiatives, as their success often relies on active participation and feedback from users. Overcoming this resistance requires not only technical solutions but also cultural change management strategies to foster an environment open to innovation.  

How To Overcome It: Demonstrate Value To Create Excitement

Your AI implementation strategy requires the support of your entire organisation, from executives to frontline employees. So you need more than a deployment plan; you need a change management programme that builds trust, kindles excitement, reduces fear and creates champions across your organisation. The key is to demonstrate how AI benefits everyone, not just the company as a whole. 

Start by addressing concerns. Employees may fear job loss or increased surveillance, so transparency is crucial. Explain how AI will remove tedious tasks, enhance decision-making and enable them to focus on more strategic projects that develop their skills. Engage everyone in discussions and create a clear feedback process—because framing AI as a tool that empowers people builds confidence, not resistance. 

Successful AI adoption isn’t just about clear communication—it’s about sparking enthusiasm and making everyone feel part of the journey. That means going beyond announcements to offer hands-on training to increase familiarity and reduce uncertainty. In addition, appoint an executive sponsor to keep staff engaged through regular updates, and share real success stories—whether from pilot projects in your own company or proven examples from your industry.

6. Ethical and Regulatory Concerns

Ethical and regulatory challenges are among the most complex AI adoption challenges. AI systems can inadvertently amplify biases, leading to unintended consequences such as discriminatory decision-making. These aren’t just technical glitches—they’re reputational and legal risks that can undermine trust and damage brands. 

Regulatory compliance is another challenge to be aware of. AI adoption often involves handling sensitive data, which means your organisation must adhere to strict privacy laws and industry regulations, or face hefty fines and legal complications. On top of that, environmental impact is an emerging issue—AI models can be energy-intensive, raising questions about sustainability as usage scales. 

Perhaps most critically, AI adoption depends on trust. If your employees, customers or partners feel like AI is a black box with no accountability, they’ll hesitate to get on board. Transparency, fairness and responsible usage matter and ignoring them can further slow adoption. The solution? A clear ethical framework and solid governance policies to keep AI both effective and trustworthy. 

How To Overcome It: Building Trust Through Governance and Responsible AI 

When you introduce AI into your organisation, it’s natural to be excited about the benefits. But trust and ethics can’t be an afterthought. Ethical and regulatory concerns aren’t just technical issues—they’re business risks. To stay compliant and build trust, you need to embed responsibility into every stage of your AI journey. 

Start by selecting vendors who adhere to recognised standards like SOC 2 Type II, ISO 27001, NIST 800-53 and GDPR. These frameworks signal a commitment to data privacy, security and ethical handling of sensitive information. Make sure your AI tools offer explainable outputs and include audit trails so you can track how decisions are made. 

Develop internal governance policies that address bias monitoring, model performance and data usage. Appoint cross-functional stakeholders—legal, compliance, IT and business leads—to review AI initiatives regularly and consider implementing new sustainability initiatives to offset concerns about environmental impact. This way, your employees, customers and partners will feel confident adopting AI—and your organisation can maximise the rewards it brings. 

Getting Started: What Are Your Next Steps? 

AI adoption is often portrayed as a monumental challenge, thanks in part to headlines that focus more on hype than reality. But in truth, most of us are already using AI tools in everyday work, often without a second thought—whether it’s automated meeting notes, intelligent email sorting or tools like ChatGPT.  

That’s not to downplay the real AI adoption challenges businesses face—poor data quality, skills gaps, outdated infrastructure and resistance to change can all slow progress. But these hurdles are not dealbreakers. With the right strategy, tools and mindset, they’re absolutely manageable.  

So how do you get started? Here’s how to take the first steps with confidence: 

• Make AI a clear leadership priority. Ensure your executive team is championing AI adoption in all areas of your business and creating a buzz with employees.  

• Partner with an AI-focused vendor. Having a trusted partner by your side that is focused on innovation helps you navigate challenges and accelerate adoption. 

• Choose AI-powered, industry-specific software. These tools are designed to fit your unique business needs—not generic solutions that may not work well for your workflows. 

• Empower your teams with training and support. Make sure everyone has access to resources that build AI confidence and capability.

Aptean Helps You Embrace AI—Without the Barriers 

While all the tips above are important, the one that’ll set you most quickly on the right path is partnering with a supportive vendor who doesn’t just offer AI—but makes it work for your business from day one. 

That’s where Aptean comes in. With Aptean AppCentral, AI isn’t a bolt-on. It’s built-into our industry-specific applications. Imagine if you could:

• Ask anything and get instant answers – with an intelligent, on-demand assistant

• Move from what happened to what’s coming – with predictive intelligence that thinks, learns and gets smarter every day

• Work faster and act faster – by outsourcing the manual and mundane to AI agents built for you or by you

• Elevate your personal game – with a smart, adaptive assistant built to support your specific responsibilities, goals and needs.

Transformational technologies are available now, just waiting for you to take advantage of them. And wherever you are in your AI journey, from learning and experimenting to ready to go big, Aptean will be there to support you every step of the way.

As the saying goes, the best time to start was yesterday. The next best time is today. Let’s seize this moment and shape the future—together.

Ready to turn challenges into opportunities? Contact us now to explore how AppCentral can make implementing AI in your operations easier.