Artificial intelligence and automation are becoming increasingly common in UK businesses, having the potential to make processes more efficient and provide useful insights. However, many organisations still find it difficult to implement and on-board these technologies successfully. Often the challenge isn’t the actual technology itself or internal budgets, but internal barriers within the organisation.

In this blogpost, we’ll look at five of the most common internal blockers to AI and automation adoption and offer practical ways to overcome them.

What are internal blockers?

Internal blockers are obstacles within an organisation that make it harder to adopt new technologies, such as AI automation. Unlike external challenges such as regulations or technology limitations, these blockers are about people, culture and processes. Addressing and overcoming these barriers is important to a businesses success, as the tools implemented will not deliver results if they are not used properly.

The top internal blockers to AI & Automation

There can be many different blockers, barriers and challenges when implementing AI, automation or a combination of both into a business. Here are 5 of the most common blockers and how to overcome them effectively.

Resistance to Change

What it looks like:

Resistance to change can show within a business in different ways. Teams may be slow to adopt AI tools, delay projects or openly question the value of automation. Some employees may avoid using new systems, stick with familiar, manual, sometimes error-prone processes even when automation or AI tools could make their work easier. Resistance may not always be vocal, sometimes it appears as disengagement or lock of participation in new technology initiatives.

Why it happens:

People naturally resist change, particularly if it is something that disrupts routines, introduces uncertainty or is perceived to be a large learning curve. There may be some fear of the unknown or potential security risks, as well as a lack of understanding of what AI and automation can contribute.

How to overcome it:
  • Communicate clearly and consistently: Explain not only what AI and automation tools do, but why it is being introduced and how it will benefit both the organisation and the employees. Focus on how it complements human work rather than replaces it.
  • Start with small, manageable projects: Pilot initiatives that demonstrate tangible results quickly. Seeing real improvements like reduced workload, fewer errors or faster reporting can help shift perceptions and build confidence.
  • Provide support and training: Offer workshops, guides, and one-on-one support to help employees understand and use new tools. Reducing the learning curve can lower anxiety and increase engagement.
  • Celebrate early successes: Recognise and share positive outcomes from AI and automation initiatives, whether it is time saved, improved accuracy or new opportunities for team members. Celebrating wins reinforces the value of the change and motivates wider adoption.

Skills gaps and talent shortages

What it looks like:

Skills gaps become apparent when AI or automation projects struggle to progress, deadlines slip or there is heavy reliance on external parties. Teams may lack confidence in handling tools, or initiatives may fail to scale because internal staff don’t have the required expertise. Even when projects are technically implemented, teams may struggle to extract meaningful insights or maintain systems effectively.

Why it happens:

AI and automation require specialised skills in areas such as machine learning, data analytics and process automation, which many organisations may not have sufficient internal expertise in. Without the right skills in place, businesses risk slow adoption, underutilised tools or dependency on external experts.

How to overcome it:
  • Upskill existing staff: Offer targeted training courses, workshops, or online learning to help employees develop relevant skills. Focus on practical, hands-on learning that can be applied immediately.
  • Leverage external partnerships: Collaborate with specialist consultants, training providers or industry experts to provide guidance and support while building internal capability.
  • Create knowledge-sharing structures: Encourage teams to document processes, share best practices and mentor colleagues. Over time, this reduces dependency on external expertise and spreads skills across the organisation.

Data challenges

What it looks like:

Data challenges can appear within businesses as poor-quality, incomplete or inconsistent data. Teams may struggle to access the data they need, or important information may be stored in separate, incompatible systems. Reports and analytics may vary depending on who generates them and what tools are used.

Why it happens:

AI relies on good quality, structured, and accessible data. Many organisations have legacy systems, siloed departments or inconsistent data management practices that make it difficult to gather and integrate data. Without proper governance and infrastructure, AI tools can produce unreliable results, which lowers confidence in technology and discourages further adoption.

How to overcome it:
  •  Improve data governance: Establish clear rules for how data is collected stored and maintained. Standardisation ensures consistency and reliability.
  • Break down silos: Create processes that allow departments to share data safely and efficiently. This ensures AI tools can access the comprehensive datasets they need.
  • Invest in infrastructure: Find experts that might be able to help merge and combine legacy systems or migrate them to modern solutions, which make it easier to manage, integrate and analyse data across the organisation.

Progress fragmentation

What it looks like:

Process fragmentation occurs when workflows are inconsistent, manual or disconnected across teams. AI tools may not fit smoothly into these workflows, leading to duplicated work and inefficiencies. Projects can stall because processes aren’t standardised enough to integrate automation effectively.

Why it happens:

Many organisations have evolved over time, with different teams developing their own ways of working. Manual processes, lack of standardisation and unclear responsibilities make it difficult to introduce AI and automation in a coordinated way. Fragmented processes also make it harder to measure outcomes or identify areas where automation could add real value.

How to overcome it:
  • Map existing workflows: Understand how work is currently done and identify areas where AI could add the most value.
  • Standardise processes: Reduce unnecessary variation and clarify responsibilities to make workflows more predictable.
  • Start with pilot projects: Implement AI in small, controlled areas where processes are already relatively consistent. Success here can provide lessons and momentum for wider adoption.
  • Encourage cross-team collaboration: Engage multiple teams early to ensure AI tools integrate well and meet the needs of different stakeholders.

Lack of understanding/ stakeholder buy in

What it looks like:

Without leadership support, projects may lack direction, funding or visibility. Teams may be unsure why AI and Automation are being implemented or how it fits with business priorities. Initiatives can stall or fail entirely if stakeholders are not engaged or don’t understand the value of new technologies.

Why it happens:

Leaders may not fully grasp the benefits or limitations of AI, or they may be concerned about the risks involved. If AI projects are seen as experimental or non-essential, they are less likely to receive the resources and attention needed to succeed. Without a clear strategy and executive sponsorship, teams may feel unsupported, and adoption may be inconsistent.

How to overcome it:
  • Educate leadership: Present AI initiatives in terms of business outcomes, cost savings, efficiency gains, and competitive advantage. Make the case in plain language rather than technical jargon.
  • Align with business goals: Ensure projects address key organisational priorities and solve real business problems.
  • Secure executive champions: Identify leaders who are enthusiastic about AI and can advocate for adoption across the organisation. Their support helps secure resources, influence culture, and overcome resistance.
  • Communicate success stories: Share early wins with leadership to demonstrate tangible benefits, reinforcing their confidence in continued investment.

Final Thoughts

AI and automation, both singularly and combined, can provide real benefits to businesses but often internal blockers can get in the way. Resistance to change, skill gaps, data challenges, progress fragmentation and lack of executive buy-in are all common barriers that can slow down or completely halt progress.

These challenges can be easily addressed. Clear communication, focused training, stronger data management and collaboration with external partners all help to overcome the challenges and successfully adopt AI and automation initiatives. By tackling these areas, organisations can create the right conditions for AI and automation to deliver real value, improve efficiency and support growth.

Not Sure Where to Start with AI?

If you’re looking to explore how AI and automation could benefit your business but aren’t sure where to begin, our free 90-minute workshop is a great place to start. We’ll walk you through practical opportunities for your organisation and show you how these tools can deliver real value. Learn more and sign up here.