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What would an AI transformation mean for your business?

  • Written by Robert D'Astolto, Head of Data Analytics at Data Agility

How quickly is Artificial Intelligence being embraced around Australia? We recently found that 69% of Australian Leaders Have Incorporated AI into Their Business Strategy for 2022.

When so many organisations in Australia are planning to undertake an AI transformation it’s understandable that you may feel pressure to follow suit.

However, before embarking on such a major change, it’s critical to get the foundations of AI right to avoid a myriad of issues down the track. That said, when conducted properly the payoffs of transforming to AI can be exceptional.

In this article we look at some of the considerations Australian businesses need to make prior to launching an AI transformation.

The current appetite for AI transformation

In Data Agility’s recent survey, we found a growing number of business leaders have begun an AI transformation or have factored one in to remain current and competitive.

What are the triggers of an AI transformation?

Our survey also showed that currently, across all business sectors, three common objectives are top of mind for implementing AI in 2022. These are:

  1. work efficiency

  2. better service delivery

  3. competitive advantage.

Interestingly, organisational growth is currently one of the least desirable objectives across all sectors.

What’s more, all the Australian leaders surveyed believe AI will increase innovation - with 86% believing AI will increase innovation by 15% or more in 2022 alone.

This is a cross-sector agreement – which shows just how many organisations could be turning to AI for planning purposes in the near future.

Bringing it home: Identifying triggers in your organisation

Having a deep understanding of “why” and “to what purpose” should be your first consideration when assessing the automation of any business processes.

There are a multitude of benefits of switching to AI – including being able to predict outcomes, accurately forecast and drive planning (as we are witnessing with the stats above). However, like all business cases, identifying your objectives will help you weigh up the requirements, disruption, costs and implementation.

Some triggers that could influence your transition include:

Changes in service planning and policy settings

Structural change within departments

Keeping pace with technological advancements

Service improvements, speed of delivery and higher volumes of


Outdated legacy systems and processes

Internal growth pressures

Departmental alignment, etc

And if you choose not to adopt an AI transformation? That’s another important consideration in which the pros and cons should be taken into account.

Key considerations when implementing an AI transformation

When planning an AI transformation, there needs to be agreement on four key areas across your business:

  1. State of your data

Data is at the foundation of every automated system, so it makes sense to ensure the management and application of it is first and foremost in your approach to an AI transformation.

Without reliable data, functionality is lost, reporting is unreliable, forecasting is incorrect and AI processing can be inaccurate.

To make sure your data meets your current and future business objectives we recommend you thoroughly audit your data to find out:

  1. Where your data is currently kept? What form is it in? Who is it accessible to and who governs it?

  2. Is your data at a fit-for-purpose level of quality? Does your current data need to be cleansed?

  3. Is there data you require that you currently don’t capture or collect? If so, how will this gap be solved?

  4. What security and privacy implications are in place around your data?

  5. What are the Data Governance procedures your team follows? Do they meet guidelines, regulations and departmental policies? Do they meet new business objectives?

The considerations above are just the tip of the iceberg and we discuss them at greater length below. If you need guidance for your data governance, feel free to contact our team or download our Data Governance ebook.

2. Your culture

You’ll need to look at a Change Management program to ensure culture, morale, and productivity don’t lag. The internal conversations with your teams and messaging will need to address these concerns. Additionally, you will need to consider training for the new AI systems and your working procedures throughout the transformation to AI.

Change Management factors should include:

How to affect this big change in your team?

How quickly can team members be brought up to speed?

Are specialised skills required, and what are the financial ramifications?

How can the change be conducted to avoid loss of productivity during implementation?

What cultural, communication and supportive elements would be needed for teams?

3. Technical choices

If you have examined the AI market, you would already know that there’s a plethora of Artificial Intelligence platforms and tools available which can be configured to your current and future business requirements.

Making the most appropriate selection will hinge on a wide range of considerations – starting with desired functionality. Other factors may include incorporating any legacy technology you already have; your architecture, what you want to achieve – both short and long term; how scalable the tech is; and if there’s a possibility of redundancy any time soon.

You will also need to ascertain whether your business requirements call for a Deep Learning (or Machine Learning) framework or Narrow AI that requires human oversight. Ideally, your business objectives, legal obligations and planning process will dictate the most suitable approach to adopt.

The list of considerations above is certainly not exhaustive, however it will provide you with a good start.

4. Implementation

Detailed communication and good change management across the business will be imperative. Also, don’t be overly ambitious with timelines. In fact, we recommend you build in extra time and financial padding to accommodate holdups, ironing out bugs in testing and bringing your teams up to speed.

The rollout process should look something like this:

1. Identify needs, objectives and any restructuring required

2. Audit your data and develop a roadmap:

2.1 Assess where it currently sits, who it’s accessible to and who governs it, how it’s updated, how many versions exist, how a true source can be developed etc

2.2 Is your data fit for purpose within the new AI transformation? Does it need to be cleansed prior to migration?

2.3 What are the gaps in your data? How should missing data and new data be sourced?

2.4 Do you need to develop Data Management processes to ensure existing and new data are automated, secure, private and well governed?

2.5 Ongoing data governance procedures may also need to be established. This should include the training of business users for collection and maintenance protocols – ensuring universal standards, procedures and definitions are adhered to.

2.6 Consider a dedicated Data Management Department or specialised Data Manager. Doing so can assist the setup process and ensure the collection, sharing and governance of data doesn’t go astray over time.

3. Design and build of the AI platform

4. Data migration to the new platform

5. Testing and fine tuning

6. Training

7. Considerations for ongoing maintenance, development and data governance

Next steps

To cover all bases above, bringing in an external team that specialises in Data Management and AI transformation could prove your least costly and stressful approach - either for collaboration on specific phases of the transition, or for complete oversight.

If you want to discuss your data requirements and technical processes – or find out more about transitioning to Artificial Intelligence – Data Agility can advise you about end-to-end data analytics solutions; from strategy development to oversight and implementation of the system transformation. For consultations, contact us today.


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