The Future of Work: Integrating AI into Your Workforce

There are as many reasons to be excited about artificial intelligence’s (AI) growing role in shaping the digital workplace as reasons to be fearful. On one hand, the technology is capable of fulfilling repetitive but crucial tasks for long hours without suffering fatigue. On the other hand, it’s replacing countless jobs, some of which require human intelligence.

Regardless of your stand on the matter (or on any AI-related issue), AI in the workplace is inevitable. McKinsey Global Institute’s latest survey on AI-driven automation reported that 92% of companies plan to increase their investment in generative AI within the next three years. Among those who are already doing so, only 1% believe their investments in workplace AI have reached maturity.

Is AI really something to worry about, however? If you’re like IKEA, which rolled out a chatbot in 2021 to handle simple questions but kept some of its human workers to deal with more complex tasks or job roles, we argue that it’s a great use of the technology. Here are a few more examples of how AI can enhance operational efficiency.

Cloud migration is notorious for its high failure rate. The numbers are all over the place, but they typically range from 50% to 90%. So many cloud migration projects are falling short of expectations that industry experts such as David Linthicum are wondering if we’re actually getting better or worse at it.

Linthicum, a best-selling author on cloud computing and AI, states that one reason for the high failure rate is cloud complexity. He goes so far as to describe it as the “silent killer” of migration efforts as businesses struggle with technologies becoming more complicated to use. Not that it doesn’t have a solution, but it involves much planning.

Assuming you get through all that, operational costs become another immense hurdle. One study revealed that factors like inflation and haste in adopting AI-driven solutions have business leaders paying 25% more on average for cloud than they initially allocated.

With cloud technology becoming increasingly necessary, overcoming these obstacles becomes more imperative. One promising approach to cloud migration for Melbourne and Australian businesses harnesses predictive analytics.

Although no system can predict the future with total certainty, predictive analytics through AI produces accurate models of operational outcomes. By using machine learning (ML), it learns patterns in current data and generates possible solutions. Common techniques for predictive analytics include:

• Regression analysis – identifies patterns and connections between variables
• Decision tree – understands the decisions and the myriad factors behind them
• Neural network – confirms the accuracy of regression and decision tree models

One paper published in the International Journal of Computer Engineering and Technology stated that companies adopting predictive analytics in cloud migration resulted in cost savings of up to 30%. Fortune 500 companies managed to reduce their monthly cloud costs by as much as USD$2.5 million thanks to accurate cloud resource forecasting.

To say that automation is a bad thing isn’t necessarily accurate. The technology has been around for far longer than most people think, as machines invented in ancient times are a form of automation. It’s born out of necessity: unlike humans, machines don’t get tired.

The IKEA example at the beginning is an example of robotic process automation (RPA), a principle that—for lack of a better definition—automates monotonous or repetitive tasks. RPA tends to be associated with AI and concerns about job displacement, but they’re two distinct technologies as shown below.

That’s not to say that they’re incapable of working together. Combining RPA and AI results in a more capable technology called intelligent process automation (IPA). This is basically RPA but with the added capacity for continuous learning to produce better results and achieve short and long-term business goals.

That said, RPA plus AI is an oversimplification of how IPA operates. Other than these two, IPA also integrates the following critical components:

• Integration platform as a service
• Natural language processing
• Machine learning
• Cognitive automation
• Computer vision
• Intelligent character recognition
• Process mining

The most telling benefit of IPA is quality output for reduced time. A video comparison of a human and a robot worker performing the same routine task showed that the latter had done the process hundreds of times over while the former was only half done. Also, considering its immunity from fatigue, IPA is less prone to costly errors.

We briefly explained agentic AI in our discussion on how to leverage AI to make more effective business decisions. It’s a step up generative AI that’s capable of generating actionable insights without frequent manual data input and modelling.

Agentic AI works using a four-step cycle called a feedback loop or data flywheel model.

• Perception: Agentic AI requires larger amounts of data, more than its traditional and generative counterparts. This is because it takes a more thorough approach to recognising relevant entities and features.
• Reasoning: Using retrieval-augmented generation (RAG), the AI generates a model for a specific routine task, such as content creation or recommendation engine. To do this, RAG entails retrieving data from various sources.
• Execution: The AI implements the models from the previous step using application programming interfaces (APIs). Limitations can be imposed to ensure the AI works as intended and avoid poor results.
• Learning: The data flywheel feeds the AI data from its interactions with the data it analysed, leading to an endless self-reinforcement process. Every iteration of the learning data fed improves the AI’s output over time.

Agentic AI is poised to supplant the Software-as-a-Service business model, which lacks the full autonomy that the former enjoys. By delegating decision-making and repetitive tasks to agentic AI, companies are free to allocate resources to more pressing matters.

Not only is the workplace of the future near, but it’s already here. No matter where you stand on the AI question, there’s no doubt that AI integration can only grow from here. Falling behind as a business owner is the last thing you want to happen.