AI is all the rage in today’s tech-centric business landscape. As a business leader, you may, likewise, be also looking to adopt AI-powered capabilities in your daily operations. While common use cases are available based on factors such as business size, industry/sector and department, understanding what exactly to implement, how and why is still a puzzle that even the most veteran organisations need some help solving.
In this article, we break down the following items about AI and its role in improving outcomes for operational tasks:
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Tracking, monitoring and reporting on key numbers are tasks that AI can conduct smoothly, subject to clean codes and extensive testing done prior. Whether it is logging readings from equipment or conducting calculations from user inputs, AI can be used to not just report on key findings, but process those key findings in ways that are clarified and easily digestible.
This is where smart suggestions come in; based on the numbers, AI can provide recommendations on what the next steps can be, for improved outcomes. Of course, this is more an aid than a hard-and-fast rule, so teams need to still exercise discretion before making any decisions.
Extending further, such suggestions can be configured to execute automatically. But once again, making sure these are reliable is imperative, to avoid any mishaps.
Predictive analytics has been around well before the mainstream use of AI. But now, AI has amplified the leverage that predictive analytics can offer to organisations. In addition to numbers and other data of a quantitative nature, AI can process other forms of data such as anecdotes to help derive more meaning out of data.
This means that teams can expect projections from predictive analytics that aren’t only more accurate, but also more insightful and contextually relevant. As always though, manual discretion still remains essential, in order to determine whether predictions are realistic.
The ability to be available at all times has been one of AI’s greatest pull factors, for many businesses. This rings especially true for customer service, where AI-powered chatbots are now replete, and ready to serve customers any time of the day.
However, adopting AI chatbots to serve customers can be a hit or a miss. Poorly configured conversation flows that loop customers around can cause frustration, while excessive steps can also lead to frustration and yes, subsequent abandonment.
As a result, conversation flows are an important component of AI chatbots, and something that is taken very seriously by companies that run successful AI chatbots. Configuring your conversation flow can literally make or break your chatbot, so ensure this is mindfully configured and extensively tested, before ever releasing for customer use.
Of course, conversation flows are just one part of your round-the-clock chatbot – and even if you refine this, it doesn’t mean AI alone can be relied upon for taking care of your customers. Manual intervention needs to be on standby (especially for resolving quandaries of a more complex nature), so your customers aren’t completely at the mercy of a tool that cannot help them during times of dire need.
It’s easy to get sucked into the rabbit hole of automating a certain process or business unit. But this can lead to you and your teams being unable to see how other operational elements are affected, as a result.
For example, bottlenecks in Materials Requirement Planning (MRP) at a manufacturing facility could give birth to the idea of fully automating this entire process. But what happens during circumstances that are completely unforeseen, such as ingredient/raw material shortages, economic fluctuations or even political unrest?
Which is why, for AI-driven goals, it is useful to focus first on challenges that are continuously being faced, and how they need to be reduced or completely alleviated to enable smooth operations.
For this, ask questions such as:
While the goal is to automate for the purpose of efficiency, manual supervision is still required for AI-oriented projects – especially in the presence of challenges that have never been encountered before.
Starting small is a great way to test your AI-powered idea, before it can be given the approval to fully take flight. A beta version or MVP can also be a safe way to try something that carries a higher risk, as smaller rollouts can get teams acclimatised to what they can expect – while centralising all efforts around customer preferences, making for a truly customer-centric product.
Following the rollout of a beta or MVP, and upon the establishment of a devops lifecycle, your AI-powered application needs regular monitoring to make sure things are functioning as expected, and savings in time, effort and money are being observed along the way.
With many AI models dependent on a constant stream of data for improvement, your AI engineering team needs to be working closely with your business teams to make fixes and adjust for improvements, so your AI project delivers steady ROI and is a general success.
An AI project brings its fair share of complexities, but once established correctly, can deliver operational efficiencies both for the short and long term. Whether it’s automating a single process or delivering predictive insights, your AI endeavour needs to undergo a thorough assessment before any development can take place. This way, objectives, budgets and timelines all stay aligned, as well as the expectations of everyone across your teams – both engineering and organisational.
In a nutshell, AI can streamline operational outcomes by:
Contrary to popular belief, AI cannot fully supplant human efforts; while many processes can be fully automated with AI, manual supervision is essential to make sure things function as expected. This also extends over to tasks of a more strategic nature, where the expertise of a dedicated human resource can steer components both AI and non-AI, in the right direction.
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