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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.
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
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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.
While virtual agents are relatively easy to develop over time, two key challenges have remained:
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
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.
For enterprises and small to medium businesses (SMBs), Artificial Intelligence (AI) opportunities are widespread and industry-specific. Each industry will grapple with conversations to understand how AI can:
What businesses need to plan for is that AI engineering and AI ops are destined to be the essential umbrella to govern AI in the coming decade. Hyper-automation (HA) of business processes will see some business models fail whilst others thrive into the 2030s.
5 December 2020: Australian education solution vendor Tribal, has upgraded its digital learning design chatbot. The move is illustrative of how chatbots can be leveraged to aid complex tasks - in this case, learning content, delivery, and leaner coaching.
Chatbots are not unique to Tribal. However, Tribal is demonstrating how such agents can deliver new capabilities into the LMS market, which can be glacial in the adoption of innovation. The Tribal chatbot is aimed at improving knowledge transfer inside an organisation. It assists domain experts to build learning content and share knowledge by recommending approaches to online training.
Like most forms of AI, chatbots will make their way into organisations through their addition to existing software solutions, either via paid upgrades or as part of the ongoing improvements of SaaS solutions. Chatbots will increasingly act in an advisory manner or as a guide for complex processes inherent in the vendors’ solutions.
As a result of this trend, staff will be presented with a growing number of chatbots embedded in different vendor’s solutions, each serving a specific purpose. This itself will present a new challenge for digital maturity and staff satisfaction.
2 December 2020: Salesforce Einstein is being extended into the Mulesoft automation and data integration platform. The newly announced Flow Orchestrator enabled non-technical staff to transform complex processes into industry-relevant events. The new AI-assisted MuleSoft Composer for Salesforce will allow an organisation to integrate data from multiple systems, including third-party solutions.
AI enables business process automation as a key technology enabler that favours organisations with a Cloud-first architecture. Salesforce will leverage its experience and connections with selling to organisation’s non-IT executives to secure a strong ‘brand leadership’ position in this space.
In mid-2019, IBRS noted a significant upswing in interest in Mulesoft and integration technologies more broadly from the non-ICT board-level executives. In particular, COOs and CFOs expressed strong interest in, and awareness of, process automation through APIs.
Digging deeper, IBRS finds that Salesforce account teams, who are well-known for bypassing the CIO and targeting senior executive stakeholders, are also bringing Mulesoft into the business conversation. Also, Microsoft is expected to double-down on AI-enabled business process automation with the PowerPlatform.
As a result, the addition of Salesforce Einstein AI into the discussion of automation and integration is expected to land very well with COOs and CFOs.
CIOs need to be ready to have sophisticated discussions with these two roles regarding the potential for AI in process automation. Expectations will be high. Understanding the possible challenges of implementing such a system takes careful consideration. CIOs should be ready to build a business case for AI-enabled business process automation.
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.
IBRS was asked to present on the AI market for 2018 - 2019. This advisory presents an AI market overview for this time with an outlook towards 2025. How has your organisation's AI journey progressed?
Conclusion: The recent use of artificial intelligence (AI) solutions has demonstrated the value of this type of technology to consumers and organisations. It resulted in the recent discovery of new antibiotics, the emergence of self-services (e. g. virtual agents) and the ability to analyse unstructured data to create business value. However, releasing AI solutions without integrating them into the current IT production environment, the corporate network and Cloud will limit the value realisation of artificial intelligence deployments.
NewsIBRS advisor Dr Joseph Sweeney has been tracking the three major Cloud vendors capabilities in AI and said Google is right to believe it has an edge over AWS and Microsoft when it comes to corpus (the data that 'feeds' certain AI applications) and also in AI application infrastructure cost and performance. However, he said this advantage was not materialising into significant gains in the Australian market.
Conclusion: As Australia’s use of consultancy services continues to grow, so too does the need for businesses to obtain value from these engagements quickly and effectively. Key to obtaining this value is the organisation’s ability to easily and rapidly provide consultants and contractors with the specific context of your business, your customers and your unique challenges.
By providing the organisational context quickly, you can mitigate time, scope and budget creep, improve the quality of outputs developed by consultants and ensure that consequent plans are actionable and genuinely valuable for your business.
However, the ability to provide the needed organisational context quickly and effectively to consultants remains a common organisational challenge, and therefore a pitfall for successful vendor engagement. This paper covers how you can overcome this pitfall.
Conclusion: Despite market hype around the role of data scientists and in-house developers for the successful exploitation of artificial intelligence (AI), organisations are increasingly looking to their vendor partners to provide ready-made solutions. Both business and technology leaders are expecting solutions to be based on the vendor’s ability to leverage their customer base across various industries to create AI features such as machine learning models.
Vendors are responding by increasingly incorporating these features into their offerings, along with a new breed of vendors that are producing pre-trained or baseline machine learning models for common use cases for specific industries.
However, organisations must be prepared to contribute to this AI product development or continuous improvement process which in practical terms means giving major vendors access to data. Without access to good data the result will be sub-optimal for both parties.
Conclusion: While the current artificial intelligence (AI) initiatives are data-driven, there are instances whereby the current data is insufficient to predict the future. For example, answering the following questions might be challenging if the available data is only of a historical nature irrelevant for forecasting purposes:
The purpose of this note is to provide a framework that can be used to derive sales principles to answer the above questions. The same approach can be used to derive other business processes principles such as procurement, customer service and client complaints tracking.
"Acknowledging the limits of machine learning during AI-enabled transformation" IBRS, 2019-01-06 22:29:52
"Analytics artificial intelligence maturity model" IBRS, 2018-12-03 09:44:43
"Machine learning will displace “extract, transform and load” in business intelligence and data integration" IBRS, 2018-02-01 10:03:37
Conclusion: Increasingly, leaders in the field of AI adoption are calling out the limitations of the current machine learning techniques as they relate to knowledge representation and predictive analysis.
Organisations seeking to adopt machine learning as part of their AI-enabled transformation programs should ensure they fully understand these limitations to avoid unproductive investments driven by hype rather than reality by expanding their definitions of machine learning to include the use of graph networks and social physics solutions.
"Prepare to manage the “evolution” of AI-based solutions with “DataOps”" IBRS, 2018-03-31 06:43:42
"Preparing for the shift from digital to AI-enabled transformation" IBRS, 2018-06-01 04:10:21
Conclusion: Artificial intelligence technologies are available in various places such as robotic process automation (RPA), virtual agents and analytics. The purpose of this paper is to provide an AI maturity model in the analytics space. The proposed maturity model can be applied to any type of industry. It provides a roadmap to help improve business performance in the following areas:
Conclusion: Organisations seeking to ride the new wave of AI-enabled transformation are facing a clear choice when it comes to the adoption of supporting AI capabilities such as machine learning or speech recognition, either:
Deciding which path to take requires that organisations reflect on their current maturity when it comes to building solutions. Only those organisations that can honestly demonstrate full development lifecycle capabilities and that have contemporary development tools and frameworks should expect anything but proof of concept success with DIY approaches to AI solutions.
"Proactive optical character recognition of incoming content will accelerate AI-enabled automation" IBRS, 2018-03-06 06:54:57
Conclusion: In seeking to achieve their vision, goals and objectives, organisations constantly evaluate internal and external factors in order to take action. Although tuned to the unique needs of each enterprise, there have been identifiable waves of factors and responding actions that have occurred since 2000 in the form of business and digital transformation.
Business transformation addressed the changing nature of markets in a connected and globalised world by focusing on delivering cost savings through new models of operation, while the subsequent wave of digital transformation sought to employ technology and exploit pervasive connectivity to increase the efficiency of internal processes and customer-facing interactions.
IBRS has identified a new wave we call “artificial intelligence-enabled (AI-enabled) transformation”, which is focused on optimising business operations through the use of emerging technologies that leverage “self-learning” algorithms to make predictions, respond to real-world objects and events, and possess user interfaces that mimic how humans communicate.
However, in order to successfully exploit this new wave of transformation, organisations must first understand what exactly AI is and how AI-enabled transformation differs from the waves that have come before it.
Conclusion: Due to years of tactical software deployments in response to urgent digital transformation uplifts, organisations have created a jungle of business intelligence (BI) technologies deployed in the absence of a well described and comprehensive approach to the challenges faced; challenges that will continue to increase with the shift to AI-enabled transformation.
Instead the majority of solution paradigms have centred around the application of emerging technologies with little articulation of a coherent architecture traceable to the underlying functional or non-functional requirements required to support a well governed and long lived data analytics platform. Instead, with each new trend in reporting and analytics, e. g. big data, results in a litany of partial solutions.
Enter Data Vault 2.0 (DV2.0) is the first well described architecture, methodology and modelling approach to emerge from the BI community in the last 5 years. DV2.0 provides a solid basis for organisations wishing to avoid the data sins of the past and adoption should be a top consideration for the inevitable expansion of BI that flows from business application transformation and as part of a clear DataOps strategy.
Conclusion: The development of AI-based solutions is heavily dependent on various types of data input in the form of either:
While at first the data management needs of AI-based solution development might leverage both data scientists and their existing business intelligence platforms to exploit these types of data, the actual lifecycle management needs of AI developers will expand quickly beyond the boundary of the traditional enterprise data warehouse.
Therefore, like the source code and configuration data underpinning transactional business applications, the raw data and algorithms of AI solutions must be managed by evolving DevOps practices towards a comprehensive “DataOps” model.
Conclusion: Although online digital platforms are in ready supply, organisations remain unable to avoid the receipt of critical information in the form of paper documents or scanned images. Whether from government, suppliers or clients, organisations are faced with written correspondence, typed material, completed forms or signed documents that must be consumed. For a variety of reasons, it may be unreasonable or impractical to expect this information to be sent in machine-readable form.
However, machine-readable content from incoming information, both past and future, is emerging as a prerequisite to exploit artificial intelligence and machine learning as part of digital transformation. Therefore, organisations need to re-examine their data ingestion strategies and move proactively to the use of optical character recognition on incoming paper- and scanned image-based information.
Conclusion: Business leaders should convert recent global interest in AI applications, safety and effectiveness into AI governance guidelines in the exercise of their triple bottom line responsibilities (for profit, social responsibility and sustainability) as outlined in IBRS research note, “The emerging need for IT governance in artificial intelligence”1.
AI includes a very broad range of technologies being applied in virtually all industries. This means that the use of AI in both IT and operational technologies2 (OT) requires C-level attention and supervision.
Conclusion: Organisations continue to emphasise their competitive differentiation based on the data they hold, and the insights gained from analysing this valuable resource. The rate at which organisations are shifting from traditional process-based to insight-oriented differentiation is being further accelerated by the adoption of Cloud-based data analytics services.
The combined result is an increasing portion of enterprise project activity that can be classified as extract, transform and load (ETL).
Despite ETL being the mainstay of data integration for decades, the cost of specialised skills and significant manual effort expended on integrating disparate data sources is now coming into sharp focus. In response, organisations are rightly seeking lower-cost solutions for data integration.
Although ETL exists in the form of at least one tool in almost every enterprise, the cost of ETL as a proportion of data analytics projects means organisations must decrease reliance on traditional ETL tools in favour of automated solutions that exploit machine learning techniques to reduce the need for ETL developers.
Conclusion: The release of Amazon’s Echo in 2014 heralded the first of a series of “ambient” technologies1. These new devices are unobtrusive, multiple purpose and capable of responding to conversational input through integration with virtual digital assistants (VDAs) such as Amazon’s Alexa.
A key enabler of these platforms is the ability to implement “skills” or expand the platform’s capability to interpret and respond with appropriate conversational content beyond the basic function of the device itself.
The consistency of information required by organisations under omni-channel delivery models, combined with under-resourced editorial teams, mean organisations must prepare for conversational channels by transforming existing content sooner rather than later.
Failure to do so will see history repeat itself through short-term replication of content to support new channels only to have that content and channel functionality merged back into increasingly sophisticated content management platforms at significant cost.
Conclusion: Abbreviated trialling of RPA platforms is shaping up as a relatively low risk, low cost approach to exploring the use of robotics to aid business process rather than lengthy technical evaluations.
However, business process re-engineering experience shows that just automating existing business processes without addressing inherent inefficiencies and adding a robotic overlay is a total waste of resources.
Basic RPA applications do not need IT coding and can reduce repetitive tasks and improve accuracy.
In more complex situations, use of RPA platforms and tools relies on leveraging IT systems integration in providing robotic aid to assist human intuitive decision-making.
In more complex situations, use of RPA platforms and tools relies on leveraging IT systems integration in providing robotic aid to human intuitive decision-making.
Conclusion: As the nature of work is becoming less routine and linear, the most effective collaboration solutions are supporting the ways that teams and individuals want to work.
At the same time, customer service techniques are changing to appeal to individuals in the ways that they like to be treated.
Developments in business work flow and customer service are emerging in four broad generations of deployment:
Many IT organisations are trying to change their perceived image from high-cost / low quality to value-added service providers. However, many of the adopted approaches revolve around improving just few processes (e.g. problem management). While these processes are important, they are insufficient to produce the desired effect for IT groups to deliver value-added services.
In this IBRS Master Advisory Presentation (MAP), IBRS outlines the high-level issues, surrounding Running IT as a Service from both business and technology viewpoints.This MAP is designed to guide and stimulate discussions between business and technology groups and point the way for more detailed activity. It also provides links to further reading to support these follow-up activities.
The MAP is provided as a set of presentation slides, and as a script and executive briefing document.
Many organisations are seeing growing demandand discussion around mobility and mobile ap-plications, in particular in the Networks Group.In theory, mobility can enable significant businessinnovation and optimisation of business process-es. However, few organisations have been able toclarify the benefits of mobility in terms that arealigned to their organisational goals and visionsstatements. This challenge is exacerbated by therapid innovation and changes underway in themobility market.
What is needed to address these problems is aconsistent, repeatable process that embeds mo-bility into the organisation’s overall IT Strategy.At the same time, mobility needs to be treatedslightly differently to many traditional projectsof work, as most mobility initiatives are smaller,with shorter deliver times, than large system de-ployments, but of often intimately interconnectedwith, and enabled by, the traditional larger backend systems.
To meet this challenge, IBRS developed its Mobil-ity Strategy Methodology, which provides a formalframework and process.
Conclusion: Pattern-based and repeatable processes, such as gathering operational data, validating data, and assessing data quality, offer potential for automation. The Web and software-as-a-service technologies offer powerful tools that facilitate automation beyond the simple mechanical pumping of data from one system to the next. Operational management tasks that focus on administration and control can and should be automated, so that managers have time to think about the organisation as a system, and can focus on continuous improvement.
Conclusion: Manually re-implementing application functionality in a new technology every few years is highly uneconomical. Model driven automation offers the potential to eliminate software design degradation and to minimise the cost of technology churn. Yet the model driven approach only works if conceptual models of questionable quality are discarded, and if deep knowledge about the business is used to develop elegant, compact, and tailored specification languages for domain knowledge.
This article is the final in a series of three on technologies and techniques that are leading to fundamental changes in the architectures used to construct software applications and software intensive devices.
"The Industrialised Web Economy - Part 1: Cloud Computing" IBRS, 2008-03-31 00:00:00
"The Industrialised Web Economy- Part 2: Software Supply Chains" IBRS, 2008-04-28 00:00:00
Conclusion: Collaboration is not something you can buy. It is not a product. It is not even a solution. It is an approach to doing business. As such, collaboration initiatives must be viewed more as a transformative business project with IT support. Large-scale, monolithic collaborative initiatives run exclusively by IT will prove difficult to justify over time and likely turn out to be white-elephants. Instead, collaboration should be driven first and foremost by a change in company culture fully backed by management, with IT supplying a supportive network and software service architecture.
Conclusion: When selecting Software as a Service (SaaS) solutions, IT managers should demand evidenced from SaaS providers as the levels of service that can be expected using a formal framework. Including IBRS’s SaaSability questionnaire in requests for information will help to ensure that all parties understand their roles and responsibilities.
Three months ago two Google researchers unveiled a project which has wide implications but attracted little attention. They proposed using ambient-audio identification technology to capture TV sound with a laptop PC to identify the TV programme that is the source of the sound and to use that information to produce personalised Internet content to the PC. This technological turnkey is called Mass Personalisation by the researchers because it brings TV and the Web together, harnessing large audiences but which are informed over the Web as individuals.
As-a-Service machine learning (ML) is increasingly affordable, easily accessible and with the introduction of self-learning capabilities that automatically build and test multiple models, able to be leveraged by non-specialists.
As more data moves into Cloud-based storage – either as part of migrating core systems to the Cloud or the use of Cloud data lakes/data warehouses – the use of ML as-a-Service (MLaaS) will grow sharply.
This paper summarises options from four leading Cloud MLaaS providers: IBM, Microsoft, Google and Amazon.
The decision to integrate machine learning (ML) into systems and operations is not one that is made lightly. Aside from the costs of acquiring the technology tools, there are added considerations such as staff training and the expertise required to improve ML operations (MLOps) capabilities.
An understanding of the ML cycle before deployment is key. Once requirements and vision are defined, the appropriate tools are acquired. ML specialists will then analyse and perform feature engineering, model design, training, and testing and deployment. This is also known as the dev loop. At the implementation stage, the ML model is deployed and the application is subsequently refined and enhanced. The next stage is the monitoring and improving stage where the organisation refines the model and evaluates the ROI for its data science efforts. This stage triggers the retraining of the model through data drift and monitoring.
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