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Q&A with Jeremiah Mannings

Exclusive Trusted Magazine Q&A with Jeremiah Mannings, Chief Data Officer.

How could you describe your career path in a few words?

I began my career in AI as a Cognitive Engineer at IBM Watson in a small team implementing (what was then) cutting-edge tech for clients at the forefront of what technology could achieve. I then progressed through roles that deepened my expertise in AI and Data Science platforms and use cases, undertaking various projects building systems such as a Machine learning as a Service platform for a Big4 bank and an Automated document lodgement and extraction system for a large Australian government body. These projects have enabled me to develop technical, architecture, communications, and consulting skills, leading to my current position as Chief Data Officer at Uniting and to the start of my consulting firm, Futuregrip Data & AI Consulting.

I've been committed to driving innovation in AI and Data strategy throughout my career, demonstrating success in leading complex transformations and implementing AI platforms. My path is characterised by a blend of technical expertise, strategic leadership, and a focus on leveraging Data & AI to achieve tangible business results. Data Science has undergone a remarkable evolution in recent years, and my success has come from having a deep technical understanding and being able to communicate that to the broader organisation succinctly and clearly, a skill worth developing in any career path. I have also been blessed by having key mentors that I have reached out to when I've been in challenging circumstances, and the value of mentorship can never be overstated.

What are the highlights of the key digital innovation trends for 2022? Can you give us some major examples?

In 2022, key digital innovation trends were predominantly shaped by advancements in AI and the shift away from bespoke platform development. AI's evolution is particularly noteworthy, with LLM's building on top of the firm foundation of transformer models, GANs, etc. and the training becoming more sophisticated, enabling everyone to use these systems. With GenAI reaching a far greater audience than any single AI approach beforehand, it had a transformative effect on understanding these systems' power. It gave people - technical and non-technical alike - a tangible understanding of what they could achieve. GenAI is not new as a technology, but its current implementations are novel and are starting to provide great value in B2C.

The other major trend I have seen is the shift away from bespoke platform development - vendors for AI & Data Science platforms have come such a long way over the last 2-3 years that bespoke development is almost entirely off the table for everything, but the most niche areas. Businesses that have put off adopting advanced AI are now in a great position to choose from a plethora of powerful platforms and don't have to forge the path on their own. This market expansion further increases the adoption of these technologies to drive business value.

Based on your experiences, what are the impactful trends in digital innovation that are becoming more important in the context of 2023?

I would be remiss not to mention GenAI for the reasons above, mainly the ease of adoption and the quick ability for people to understand its value and impact. GenAI will continue to have transformative impacts as the ecosystem develops with better and more competitive products coming to market. These approaches are being augmented on existing platforms, providing greater value out of more traditional systems. As the use of these technologies progresses, businesses will need to consider how it will change the roles of their workforce and what augmented workers can achieve - noting the significant data security concerns with this tech. It also exposes the consumer to the breadth of AI tech that can change and influence processes and drive significant value if implemented in the right places.

By the end of 2024, if your organisation is not using augmented processes, capture and prediction as a part of your decision-making process for senior leadership, you will start to fall behind your peers. Boards are progressively realising this and incorporating these technologies into their decision-making processes.

Another critical technology developing is the rise of edge computing. By processing data closer to where it's generated, edge computing enhances real-time data analysis. It is crucial for industries like autonomous vehicles and smart cities where powerful algorithms are less practical than small, low power and tactical ones to drive outcomes. With the amount of power used to generate outcomes for GenAI, edge computing will grow in popularity as you don't risk the downtime of relying on a single platform.

Lastly, the development of quantum computing, although still in its nascent stages, is beginning to influence sectors like cybersecurity, pharmaceuticals, and materials science, promising to solve complex problems much faster than traditional computers. These trends underscore a shift towards more efficient, secure, and sustainable digital solutions, where, similar to edge computing, outright power is not the objective - it's efficiency.

In your opinion, how can they create high value for organisations?

Like any new technology, the first question organisations need to ask is around the use case: What is the use case for this technology? Implementing GenAI, Edge computing, or quantum sounds tremendous and innovative. Still, without a strong use case, it will fail like the blockchain projects in several organisations over the last few years where the focus was technology, not business value. It is worth spending time on strategy and understanding the high-value pressing use cases in your organisation where these technologies can assist. Technology is how the problem is solved; you need to understand the problem. Working with Senior Leadership and Boards to understand the capability of these technologies and clearly articulating the value in relation to problems your business needs to solve is the first step in making sure that you are actually creating value.

The other significant improvement is the time to market; in traditional Data science approaches - the old world - projects took 3/6/9 months to get to production (if they made it all). Now, in the world of rapid development, high-quality off-the-shelf SaaS solutions and well built technology, you can expect the time to production for most solutions to be far smaller. This reduction in delivery time enables your organisation to assess, try, and compare several solutions to find the right fit. It reduces the reliance on trying to hire the best AI developer to make it all work!

Overall, it's an exciting time to be in this space, and it continues to grow in leaps and bounds on rapid time scales. It's crucial to seek outside perspective to fully leverage AI's potential and ensure you're extracting maximum value from these groundbreaking technologies.

Here's the link to my firms website :

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