Process Automation, Data Analytics and Artificial Intelligence (AI) – Why Now?
To keep up with the competition – let alone outperform it – the digital imperative today demands that a business consider how automation, data analytics and AI augments or substitutes work performed by people.
Operational excellence requires a foundation of efficient & standardised business processes enabled by modern IT systems, to support key business functions including sales & service, finance & accounting, human resources & payroll, assets, inventory, manufacturing and distribution.
However, no business will have a single IT system to cover everything, so there is both an opportunity and need to consider how automation, data analytics and AI can work across all systems and business processes to further increase business efficiency.
Not only can a business leverage non-integrated internal data to make better decisions and actions, external data can also be utilised.
Whilst benefits can be gained by considering automation, data analytics and AI individually, the largest benefits are obtained when combining these capabilities to enable “intelligent automation”.
The most exciting aspect is that these technological capabilities are available now and at an attractive price point to the SME market.
Even better, significant “quick win” opportunities exist that a business can act on immediately, that can also “scale up” with business growth.
Process automation software can free the human worker from repetitive tasks and ensure processes are executed consistently and more efficiently, enabling people to focus on higher value tasks, and improving the experience for employees, suppliers and customers.
Data analytics can turn data into useful information to make better decisions and actions. Data analytics looks backwards and forwards, providing insights into what happened, why it happened, what is likely to happen next, and what the optimal path forward should be.
AI refers to a software capability that is trained or learned, as opposed to programmed instructions. AI learns from vast amounts of both structured data (e.g. data in rows and columns) and unstructured data (e.g. words, images, sounds, sensors).
If you are looking for answers to any of the following, we can help:
- We don’t have practical experience with this, we don’t want to learn on the job and repeat the mistakes of others, we don’t know who can we trust to give us independent expert advice?
- How do we identify high value improvement opportunities, especially the quick wins? Show us some relevant examples and educate us to find opportunities in our business.
- What technologies should we be considering and why? How do we pick the right ones?
- What is our approach to developing the new skills for our business? Retrain existing people or recruit new roles / suppliers for data engineers, visualisation designers, data scientists, automation developers, for example?
- How do we overcome the perception of “poor data quality” (in reality no one has “perfect” data)?
- What is the investment required and ROI? Can we “seed fund” a proof-of-concept to gain support for greater investment?
- What does the implementation plan look like? How do we “think big, start small, scale fast”?
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