Key considerations and challenges of Intelligent Automation
Learn how to determine if Artificial Intelligence (AI) and Intelligent Automation are the right fit for your organisation
Intelligent Automation is an extremely powerful way to boost your business’s efficiency, enhance customer experience, and secure your long-term digital transformation goals. However, this transformative power is only realised when these technologies are deployed correctly. As a strategic technology leader, it is vital to have the right strategies in place to ensure a successful implementation from the very beginning.
Implementing AI and Intelligent Automation can be akin to layering advanced cognitive capabilities onto your existing processes and legacy applications. Once these intelligent workflows are active, you must carefully consider change control for both the automated processes and the underlying applications they interact with. When thinking about intelligent automation at scale, you should view it as a major, transformative IT change programme.
Get your digital transformation off on the right foot. We have compiled a list of key considerations and challenges you need to keep in mind when determining if AI and Intelligent Automation are right for your organisation:
Control and governance
A lack of control and governance is particularly prevalent when individual departments adopt third-party AI tools or build their own automated workflows without central IT oversight. Similar to the unmanaged spread of legacy macros, shadow AI can be very powerful in the right hands, but it introduces significant operational and security risks.
Consideration must be given to who will be building and managing your intelligent processes. Strategic leaders need to establish robust governance frameworks that allow for innovation while protecting the enterprise. Partnering with expert consultants can help you implement a secure, scalable infrastructure that gives your business units the agility they need without compromising on compliance or data security.
Don’t just accept your existing processes
Intelligent Automation presents the perfect opportunity to fundamentally enhance and improve your operational workflows. You should consider if simply automating the status quo is genuinely the right solution, or if alternative cognitive improvements would lead to a better overall scenario.
Rather than just speeding up an inefficient task, AI allows you to rethink how value is delivered to the end user. Re-analyse and deeply de-compose your processes so you thoroughly understand how they can be optimised. By applying machine learning and natural language processing, you can create bespoke solutions that not only cut costs but actively improve the customer experience.
Cultural and organisational change
Adopting AI is as much a cultural change as it is a technological one. Business users, especially the end-users who will be working alongside intelligent systems, need to be comfortable and accepting that these tools are here to empower them, not replace their jobs.
At Ten10, we understand that AI is not about replacing people; it’s about complementing them. Speak with your staff regularly, provide the bespoke technical training needed, and reinforce the benefits of automation to the people who will be using it every day. Addressing these cultural shifts head-on is a proven strategy for improving talent retention. When you remove tedious manual tasks from your team’s workload, you allow your diverse technology talent to focus on high-value, strategic problem-solving.
Balance expectation and enthusiasm with realistic ROI
Realistic expectations must be set from day one. Experience tells us that no client requirement is the same, so it’s critical to ask all the right questions and cut through the hype. Like any strategic digital transformation initiative, an investment needs to be made upfront, and implementation may feel deliberate at first.
The first processes you choose to automate should ideally offer high visibility but carry lower operational risk. Jumping straight into your most complex, mission-critical processes out of the gate is a recipe for a stalled project. By adopting a pragmatic approach, you can deliver cost-effective solutions that demonstrate tangible ROI early on. This builds confidence across the executive board and secures the buy-in necessary for long-term scalability.
Creation of an entirely new capability and support function
Intelligent Automation at scale requires the creation of an entirely new capability and support function within your IT department. Unlike rules-based systems, AI models can experience data drift or encounter highly nuanced edge cases. Therefore, robust exception handling needs to be built into all processes.
Exception handling is a critical component that stakeholders often forget when they only consider the “happy path.” You must clearly establish who owns the automated process post-implementation: the business unit or IT. Creating a dedicated centre of excellence ensures that your scalable infrastructure remains healthy, updated, and aligned with your broader strategic objectives.
Develop a data strategy
Intelligent Automation constantly requires AI models to process and handle vast amounts of data, which often includes highly confidential information. This must be handled with the highest level of sensitivity and security.
You cannot rely on the easiest routes, such as unstructured Excel files and standard emails, when training AI or executing automated tasks. Governance, strict data privacy protocols, and the ability to thoroughly audit intelligent processes are essential. Ensuring your data sets are clean, diverse, and well-managed is the foundation of any successful AI initiative. When building your platform, you must prioritise a data strategy that actively prevents bias and guarantees regulatory compliance.
It’s not just about the tools
The lack of suitable documentation for existing manual processes can present a massive challenge for any automation initiative. Does everyone in your department perform a specific workflow in the exact same way? Are there underlying flaws that need to be resolved prior to introducing machine learning?
Technology is developing so quickly that it’s hard to ensure your teams evolve and adopt the skills they need to manage these new systems. Quality AI engineers and automation architects can be expensive and difficult to retain. Our Tech Academy provides high-calibre, diverse technology talent, selected and developed to provide the best combination of aptitude, relevance, and cultural fit. By utilising our Recruit-Train-Deploy model, we enable our clients to address their immediate and future tech talent needs seamlessly.
Intelligent Automation evaluation
To fully release the strategic value of an effective AI implementation, it is vital to have a robust evaluation framework in place. The careful analysis of a potential automated workflow is just as important as the algorithmic code itself. You can read our recommended process for evaluating automation opportunities below:
Our recommended IA evaluation process
Identify an initial list of business challenges and candidate processes that your stakeholders believe have high automation potential. Look for areas where cognitive data extraction or predictive analytics could dramatically reduce operational bottlenecks.
Create current state process flows and gather relevant operational data. Assess these candidates based on technical feasibility, the quality of available data, and the potential impact on customer experience and talent retention.
Review the scoring results and establish a clear process selection order. This ensures that your investments are directed toward the most cost-effective and transformative solutions first.
Bring your technical and business participants together to discuss and provide input on the initial prioritisation list. This collaborative approach ensures that the chosen direction aligns perfectly with your overarching digital transformation goals.
Evaluate which AI frameworks, machine learning models, and automation platforms are most suitable. Base this decision on the specific processes identified, the required scalable infrastructure, and your allocated budget.
Update the prioritisation list and begin development work on a carefully selected process as part of a targeted Proof of Concept. This allows you to test the waters safely before committing to a wider enterprise rollout.
A simplified evaluation process ultimately involves identifying an initial list of automation candidates that possess high business value. By documenting the exact steps and challenge areas of each process, you can establish the complexity and potential business benefit. Once this priority is known, you can select the AI toolset most suited to your bespoke needs and confidently start your Proof of Concept.