The Latest

23 February 2021: The appetite for crowdfunding of tech startups looks to remain strong, with the fledgling accounting software vendor Thrive securing AU$3 million through the Birchal service.  

Why it’s Important

There are two lessons to take from this announcement. 

First, commercial crowdfunding is a growth area that will favour niche tech start-ups. As more success stories emerge, this has the potential to re-invigorate the Australian startup community, which has been lagging. 

Second, it highlights the likely capabilities to be introduced in SaaS-based financial solutions: namely AI-powered automation and machine-learning decision support.

Who’s impacted

  • CIO
  • CFOs
  • Individual investors

What’s Next?

There is the potential for larger organisations to set aside funds to invest in startups. CIOs and CFOs may wish to watch the crowdfunding space that may provide relevant solutions to their needs, or secure services that may complement or even compete with their organisation. While IBRS acknowledges this strategy will not be suitable for the majority of organisations it works with, there is the possibility this will become more common over the next decade, especially for startups in security, Cloud management and cost control, AI-powered automation and machine learning-based decision support systems.

While Thrive is unlikely to be of interest to CIOs, being targeting squarely at SMEs and sole traders, the vendor’s goals leverage AI to automate much of the account process and provide recommendations, highlighting where development dollars will be going for many SaaS-based accounting solutions.

Related IBRS Advisory

  1. CIOs seek ready-made over DIY AI solutions
  2. How can AI reimagine your business processes?
  3. Salesforce Einstein automate
  4. The evolution of SaaS offerings for legacy systems


Traditional Enterprise Architectures (EAs) were introduced to tighten IT control over the type of technology to be used and ensure IT developers comply with IT standards. While this control driver was essential to ensure cost-effective solutions, it was introduced at the expense of efficiency. Without reducing the essential controls, modern EAs should shift the current focus to continuous service improvement. This will permit a flexible mode of work (e.g. anywhere, anytime, any device) and enable businesses to transform, grow and survive in the digital world.

There is more innovation going on behind the scenes in Australian organisations than they are being given credit for. IBRS advisor, Dr Joseph Sweeney, who specialises in the areas of workforce transformation and the future of work stated, Australian organisations have led the world in the uptake of virtualisation which now has Australia leading in terms of Cloud adoption. 'World-leading Australian innovation was emerging in how Cloud-based services could be used to make internal operations more efficient, which was less glamorous than some of the consumer-facing apps being developed by emerging fintech companies, but equally worthwhile." said Dr Sweeney. 

“One area of innovation IBRS has identified over the last year is a rapid update of low-code platforms to allow less-technical staff to be involved in digitising business processes,” he said. Citizen developers aren't just limiting themselves to e-forms but are using a full range of low code tools and vendors are reporting sales growth of over 30%.

Full story.

The Latest

2 February 2021: Google has announced general availability of Dialogflow CX, it’s virtual agent (chatbot) technology for call centres.  The service is a platform to create and deploy virtual agents for public-facing customer services. Google has embraced low-code concepts to allow for rapid development of such virtual agents with a visual builder. The platform also allows for switching between conversational ‘contexts’, which allows for greater flexibility in how the agents can converse with people that have multiple, simultaneous customer service issues.

Why it’s Important

While virtual agents are relatively easy to develop over time, two key challenges have remained: 

  1. the ability to allow non-technical, customer service specialists to be directly involved in the creation and continual evolution of the virtual agents
  2. the capability of virtual agents to correctly react to humans’ non-linier conversational patterns.

Google’s Dialogflow CX has adopted aspects of low-code development to address the first challenge. The platform offers a visual builder and the way conversations are developed (contexts) can be described as ‘program by example’. While there are third-party virtual agent platforms that further simplify the development of agent workflows (many of which build on top of Dialogflow), the Google approach is proving sufficient for non-technical specialists to get heavily involved in the development and fine-tuning of virtual agents

Who’s impacted

  • CIO
  • Development team leads
  • Business analysts

What’s Next?

If not already in place, organisations should establish a group of technical and non-technical staff to explore where and how virtual agents can be used. Do not attempt a big bang approach: keep expectations small, be experimental and iterative. Leverage low-code ‘chatbot builder’ tools to simplify the creation of virtual agent workflows, while leveraging available hyperscale cloud platforms for the back end of the agents. 

Related IBRS Advisory

  1. Chatbots Part 1: Start creating capabilities with a super-low-cost experiment
  2. Virtual Service Desk Agent Critical Success Factors
  3. SNAPSHOT: The Chatbot Mantra: Experimental, experiential and iterative
  4. New generation IT service management tools Part 1
  5. Artificial intelligence Part 3: Preparing IT organisations for artificial intelligence deployment
  6. VENDORiQ: Tribal Sage chatbot

Conclusion: Machine learning operations (MLOps) adapts principles, practices and measures from developer operations (DevOps), but significantly transforms some aspects to address the different skill sets and quality control challenges and deployment nuances of machine learning (ML) and data engineering.

Implementing MLOps has several benefits, from easing collaboration among project team members to reducing bias in the resulting artificial intelligence (AI) models.

Conclusion: Despite decades of investment in new technologies and the promise of 'digital transformation', workforce productivity has languished. The problem is that technological change does not equate to process nor practice change. Put simply, doing the same things with new tools will not deliver new outcomes.

The mass move to working from home has forced a wave of change to practices: people are finally shifting from a sequential approach to work to a genuinely collaborative approach. And this work approach will remain even as staff return to the office.

The emerging wave through 2020 and beyond is process change: continual and iterative digitisation of process. Practice and process changes will be two positive legacies of the pandemic.