Overcoming the Obstacles to AI Adoption
Artificial Intelligence is the word of everyone’s lips right now, but what’s next for AI innovation? How will it impact our day-to-day lives? There are many unknowns when it comes to what AI can do, which is unsurprising given it represents the swiftest technological evolution most of us have ever seen, with the capabilities of AI evolving almost weekly.
The power of AI combined with suitable use cases and a robust implementation plan can help businesses to radically reduce the time spent on manual, repetitive tasks, and allow teams to prioritise value-added work.
But in all the excitement, it’s evident that many businesses are held back by inertia, and a lack of understanding about how to actually go ahead and implement AI into their business.
The road to AI
The first thing to know is that the road to implementing AI probably isn’t going to be smooth. There are many hurdles to overcome, not least organisational resistance. Employees may be feeling wary of AI, intimidated, resistant to change or just plain ambivalent about the whole thing. Then there’s the spider’s web of security and compliance to battle with, and the question of implementation costs and training.
When it comes to technology, IT systems and data must be primed for AI, which poses even further issues if they are built on old legacy systems that don’t support innovations.
This is why getting your operational ducks in a row and understanding the purpose of AI in your business is crucial to overcoming these barriers. Making use of groundbreaking technology is one thing, but without a clear application in mind, progress will be stunted. Having a business orchestration tool in place will give you a clear view of your entire operation and set the foundations for you to take a good look at your business and figure out the areas where AI could make processes more efficient and take away manual effort. Knowing exactly what you aim to achieve with AI enables you to build a compelling case for its use and address any emerging issues.
Build a roadmap
Identifying the problems you wish to solve with AI is a critical step before investing in shiny tools. I believe the best way to discover which tools are right for you is to conduct an end-to-end examination of your business processes, with a special focus on manual and repetitive tasks. From there, you can consider how an AI co-pilot could make a difference. For example, if managing numerous forms is part of your workflow, adopting Intelligent Document Processing to automate data entry could be a game-changer. Similarly, for handling large volumes of service emails, email triage and sentiment analysis can significantly enhance efficiency.
AI doesn’t represent all types of automation
AI has become a broadly used term, but it’s important to recognise the difference between artificial intelligence and other automation technologies. The way I see it, AI can be categorised into three types: AI models developed by data scientists for predicting outcomes, narrow-field AI found in products like invoice data extractors, and generic generative models like ChatGPT that are multifunctional. Implementing automation technology such as RPA, Rule Engines, iPaaS, or Low Code solutions warrants a different strategy.
Understanding these distinctions clarifies the unique applications of AI, and therefore makes it easier to understand where they can fit within your processes.
Safely embracing GenAI
We’ve all heard of the dark sides of AI and privacy, but the truth is there are scenarios which the technology can be used with minimal risk. For creative professions such as graphic design, coding, or copywriting, embracing GenAI is a low-risk endeavour. For instance, in our own organisation our Copywriters rely on AI for proofreading, while our Coders use it to write their first draft of code. These teams, which have established procedures for testing, quality control, and validation, find AI invaluable for accelerating routine tasks.
I recommend approaching GenAI in three steps.
First, identify all employees within the organisation whose roles involve creation, be it writing, designing, or coding.
Next, form task forces for each skill set and empower the employees to find the best AI co-pilot for their specific tasks. For example, Graphic Designers might find tools like Midjourney useful, whereas Copywriters could save time with ChatGPT.
Finally, procure low-risk AI tools to assist individuals across the organisation in their creative projects. This method facilitates the precise integration of AI co-pilots, boosting productivity and creativity while mitigating risks.
For roles associated with ‘delivery,’ ‘process,’ or ‘execution,’ it’s crucial to establish safeguards around GenAI and manage risks, which is where orchestration comes into play. It’s necessary to have a method for measuring expected outcomes and a clear policy regarding data management and the use of organisational data in training other models.
To make AI implementations a priority across the organisation, businesses should empower their employees to take the lead on these projects. The key is to move beyond seeing AI as simply a task for the IT department. When off-the-shelf AI models are made accessible to business users, they can directly apply these tools to their work areas, enabling a deeper understanding and ownership of the technology.
Unlocking AI potential
When effectively utilised, AI can streamline manual tasks, save time, and empower service teams to focus on more valuable work that is more closely aligned with their interests. By focusing on processes that are inefficient, businesses can utilise AI and automation to make a significant difference, rather than taking a ‘stab in the dark’ approach.
