In our ever-evolving digital landscape, the integration of AI into workplace processes is becoming increasingly crucial. To gain deeper insights into this transformation, we sat down with our Head of Data, Nick, to explore the implementation of AI in workstreams.

Nick shares his thoughts on the benefits and potential challenges businesses may encounter as they navigate this technological shift.

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Unlocking the Advantages: How AI is Revolutionising the Workplace

Q. What are the key benefits of integrating AI into workplace processes?

A. The two key benefits which come to mind are Insights& Automation.

Insights, particularly dataset or cross-dataset correlational relationship finding is invaluable.

Automation, AI increases efficiency by handling repetitive tasks, allowing employees to focus on more strategic work. Automated processes also reduce errors and maintain consistency, which enhances overall accuracy.

 

Q. How does AI help in automating repetitive tasks, and what impact does this have on employee productivity and morale?

A. Focusing specifically on anomaly detection, we can take advantage of this to trigger model retraining should anomalous outliers be beyond a threshold. Prior to retraining, we can log details of the outliers for further investigation. After the retraining, a comparison between model 1a& model 1b again can investigate changes between the predictions.

For the employee, the productivity is enhanced by Ai's assistance in helping the model, and the moral hopefully be seeing more forward momentum, and less “Oh something has gone wrong; is it the dataset that has changed!?” moments when the prediction is less-than-ideal.

 

Q. How has AI helped organisations save time and reduce operational costs?

A. Speaking from a decade’s experience working within public health sector, there are near-countless daily, bi-daily, weekly, bi-weekly, monthly, quarterly, and annually reports either going internal and external; NHS-E, NHS-I, NHS-D, NHS-X, PHE, Local Councils, for performance &operational requirements.

In the past, I used embedded SQL, dynamic variables, VBA& stored procedures, triggering these manually. However, now not only can the triggers be cloud-based & automatic, but with the advent ofcode-completion technology such as Copilot, the process of developing such processes will be streamlined.

Directly on time & materials saving, I turned a colleague’s workload from being 2 – 6 hours per day (Tuesdays the heaviest) to10 – 20 minutes alone.

 

The challenges of AI

Q. What are some of the challenges and risks associated with implementing AI to improve efficiency?

A. The key challenge is always subject matter expertise. Creating a model for a use case is one thing but retraining the model; knowing when to retrain a model, while continuing to balance bias vs. variance; acceptable inaccuracies in exchange flexibility in predictions, is critically important.

 

Q. Can AI sometimes lead to over-reliance, and how do we strike a balance between automation and human input?

A. Most definitely, but with continued upskilling of the self, over-reliance should be placated. Over-reliance in a Data or Analytical field will likely come in the form of lack of subject matter experience i.e. “The person who made the model left, but it works so I don’t want to touch it”.

Additionally, good communication & documentation is critical to combating wider over-reliance on bad or poor practices.

 

The future

Q. What are some best practices for integrating AI into existing workflows without causing disruption?

A. I would recommend A-B Testing, with A being the current way, and B being the new / AI way. Compare & contrast; run side-by-side, monitor results & expectations. If these align, or are better with B (new /AI way) then migrate to B on a key date with all stakeholders aware of the migration.

Optics is critical to awareness and understanding of AI.

 

Q. What excites you the most about the future of AI int he workplace?

A. Enhanced correlational insights, with neural networks comes greater revelations in insights, and with enhanced compute technology comes greater neural networks.

 

In conclusion, the integration of AI into workplace processes presents a transformative opportunity for businesses. As Nick highlighted, AI's potential to enhance efficiency through automation and provide valuable insights can significantly reshape how organisations operate. However, the journey is not without challenges, particularly around subject matter expertise and avoiding over-reliance on automation. By embracing best practices such as A-B testing and fostering continuous learning, businesses can strike a balance between AI-driven innovation and human input. As AI continues to evolve, its role in the workplace will only become more pivotal, opening new avenues for productivity and growth.

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