There’s a big difference between organisations having AI awareness and having the ability to orchestrate successful AI adoption.
Awareness has never been higher. Recent research shows that a staggering 96% of professionals have a basic awareness of AI. Look closer, however, and the cracks start to show. The same report found that almost three quarters of professionals lack a strong understanding of AI’s practical applications.*
Another study, meanwhile, found 93% of an organisation’s AI investment budget goes on technology – leaving only 7% to fund initiatives around change management, stakeholder engagement and human behaviour.**
As we’ll explore further in today’s post, it’s investing in those human elements that will pave the way for successful adoption and long-term value.
What are the common barriers to AI adoption?
In our work both with clients and internally here at AC (primarily enabling agentic AI using Atlassian Rovo and Forge, and AWS Strands), we see the following objections and concerns crop up fairly regularly.
- Pushback from teams (often driven by fear)
- Lack of understanding (or belief) in the value of AI
- Data security and compliance concerns
- Resistance to change
Our role is to guide both leaders and teams on the ground through these blockers – often through human engagement and education work. Let’s have a quick run through each of them.
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Pushback from teams
This is common a common AI adoption blocker – and entirely understandable.
A lot of it is driven by fear. ‘AI will take my job‘… ‘The robots will replace me’… These are familiar refrains to many of us.
It is inarguable that advances in technology have led to reduced headcount or changes in the scope of a role for some organisations.
But that’s only the part of the story. For Service Management teams, for example, enabling AI Agents and automated workflows can enhance role fulfilment, as it enables human agents to move away from repetitive, lower value tasks, and focus instead on more strategic, priority work.
The solution to this blocker lies in how you frame it.
Acknowledge the hesitancy that some of your team members may have, but gently educate on the opportunities that AI can bring – and the greater job satisfaction that they may derive from higher value work as a result.
Don’t put AI on a pedestal…
Don’t put AI on a pedestal, beyond the team’s reach. Teams that see long-term success with AI will be the ones where Agents and automations are embedded deeply within working patterns and processes – not simply added on for the sake of ‘using AI’.
We’d always recommend that teams get hands on with your AI tooling. Show them your AI agents or workflows in action. Ask them for ideas around custom Agents that could support and improve their day-to-day work.
You should also avoid AI initiatives falling into the trap of a ‘set and forget’ mentality. Instead, you’ll want to regularly review the impact of the tooling:
- Is it delivering value in the context of your use case?
- Are your parameters still the same?
- Do you need to scale any elements up or down?
- Could you replicate it elsewhere in your organisation, or integrate with other tools or systems for even greater impact?
‘I don’t really understand how it will benefit us… where’s the value?’
This is blocker is similar to the one above, but has its roots in scepticism as opposed to fear.
Change-resistant stakeholders may not believe in the tangible benefits that AI experiences can bring to delivery. Busy teams, meanwhile, may not have the time to experiment and see the results for themselves.
This blocker is compounded by stats like this from Deloitte: Despite 85% organisations increasing their AI budgets, only 6% saw a return within a year.
No wonder there is a lack of enthusiasm. Can using AI really deliver the benefits it promises?
Education and awareness
The solution lies in carving out dedicated time for education and training. When we’re supporting clients with these kind of internal blockers, we recommend a programme including:
- Discovery workshops to run through real-world benefits that other clients have experienced
- Hands-on demos (of a specific AI Agent, for example)
- Sharing case studies for team members to take away and digest
- Appointing an internal AI champion, who can be a positive spokesperson for the technology in your organisation.
We also work with customers to identify examples of highly manual or time consuming work, map out the processes, and then we advise where AI or automation would be able to cut this down. This is an impactful way of demonstrating tangible benefits.
Finally, manage expectations around AI. It is not necessarily a silver bullet. Long-term, measurable success needs to be supported by stronger underlying processes and culture.
ℹ️ The leadership impact
A recent Atlassian initiative found that a leader’s attitude towards experimentation actively influences their team’s approach.
People who watched their manager or leader live demo AI use cases were:
- 4x more likely to work with AI throughout the day
- 3x more likely to be “Strategic AI Collaborators” (using AI thoughtfully to improve work, not just dabbling).
Your leaders set the tone; successful AI adoption must begin at the top down.
Read the full story on Atlassian’s blog.
Data protection and compliance requirements
This is a tough one, as some organisations/teams simply cannot adopt AI tools if their company policies don’t permit it.
There are secure ways around this, such as creating a self-hosted AI LLM, using a service like Databricks (another of our partners here at AC). This would ensure that your organisation’s data is not being fed back to train the data models.
If your organisation does permit AI, meanwhile, but the key blocker is a lack of trust, then there are steps you can take to support clients through the change, and provide peace of mind.
The power of an open conversation
In our experience, the starting point to address this challenge is to listen to stakeholder concerns.
We take the time to fully explain what happens with their data when it comes to an AI tool, like Rovo, and how we would embed it safely and responsibly. A simple discussion like this can really ease hesitancy.
All AI innovations should follow users’ existing permissions. To further strengthen trust and peace of mind, we’d conduct a health check of a client’s tooling permissions. This can flag any areas that require attention, and give the client confidence that people aren’t able to use AI to see or do anything they wouldn’t be able to manually.
In our line of work, we also help create governance frameworks, and can embed certain guardrails within a client’s ways of working.
Unsure how to balance innovation and compliance? Our Service Management + AI in Regulated Industries gets right to the heart of these challenges.
‘We just don’t like change…'
How can you overcome a general culture of change resistance?
One issue is a lack of training. We see companies rolling out AI, and expecting users to find the value themselves. Where there is no user training or ownership of the rollout, it’s bound to fail.
Garbage in, garbage out…
Often AI doesn’t work very well in the first instance, because organisations don’t have their house in order, or their data is a mess.
Again, we’d approach this by conducting health checks to audit existing data and configuration.
We always work with teams to improve their knowledge management and data structures so that the AI tools they use can work effectively.
AI adoption blockers: What’s next?
AI Agents and tools deliver tangible, long-term benefits when they are deeply embedded in your processes, confidently adopted by your teams on the ground, and are regularly reviewed and improved.
There are significant barriers to achieving this – often requiring a cultural shift and investment in training and internal education – but they are not uncommon.
These AI blockers can also be easier to address if you have the support of external eyes, such as a consultancy like us!
Speak to our team to book your free 30 minute consultation
Written by Hannah Vincent
