Improving the ease and speed of accessing benefits is key to government efforts to extend social and economic safety nets to what the International Social Security Association (ISSA) refers to as the 鈥溾��. Self-employed and gig workers, as well as rural, migrant, and domestic workers are typically time-poor, not already engaged in social protection systems, and are often not included in targeted outreach programs. This makes them vulnerable to economic shocks and cost-of-living increases that can tip them into poverty and homelessness.

As such, many government agencies and not-for-profit organisations are looking at ways to make social services more accessible and responsive by reducing the 鈥渉assle costs鈥� associated with claiming benefits.

How AI can help

Governments around the world have been realising significant efficiency gains through applying artificial intelligence (AI) in the back-office to improve workforce productivity. Encouragingly, there are also recent examples of AI being leveraged in the front-office to improve the efficiency and effectiveness of citizen engagement.

1. Quicker time to payment with AI-supported assessment processes

At , a combination of Machine Learning (ML) and Generative AI (GenAI) support staff to efficiently process applications for more than 鈧�3.5 billion in financial aid.

麻豆原创 Machine Learning is used to link citizen application data to supporting documentary evidence, enabling case workers to expedite processing for the bulk of applications and to focus their attention on those most likely to be non-compliant. Across two programs, Hamburg reports that nearly 180,000 benefit applications have been processed, with more than 10 million pages of supporting documents automatically evaluated and classified by AI.

麻豆原创 Generative AI Hub has also been introduced to summarise inbound applications and to generate draft outbound correspondence, further reducing time to payment for customers while minimising the burden of repetitive manual work for staff.

2. Improved customer service with AI-powered workflow automation

Similarly, uses 麻豆原创 AI to automate workflows and to recommend potential benefits based on customer circumstance data. AI has contributed to a 20% increase in user productivity, which amounts to a substantial efficiency gain when applied to an agency of 9,000 public employees managing 鈧�1.8 billion in monthly payments. These efficiencies flow through to citizens, as described by the Deputy Director General of Benefits and Subsidies, who reports their AI-enabled system 鈥溾�as empowered me to shift focus from administrative tasks to truly enhancing citizen service, allowing for quicker responses and more meaningful interactions.鈥�

3. Faster query resolution with AI-enabled chatbots

At , an 麻豆原创 AI chatbot responds to 50% of citizen inquiries with no human intervention, resulting in 77% being answered and closed within the same day. While social services inquiries would typically be more complex, there鈥檚 certainly potential for AI to categorise and prioritise inbound communications and to route them to the appropriate channel or group. This is the case for more than 83% of the inquiries being received by the Office every day, which embassy staff say 鈥溾�eans efficient communication, satisfied customers and a gain in personnel resources for other tasks.鈥�

Reducing the barriers to adoption with embedded AI

To date, the types of use cases described above have been delivered as custom AI solutions, limiting uptake to agencies that are sufficiently resourced to assemble AI systems from Large Language Models (LLMs) and other necessary components. This is further exacerbated by the additional work needed to protect customer data, prevent bias, and ensure the reliability of AI recommendations.

Thankfully these barriers to adoption are being reduced as AI capabilities become embedded into enterprise software, enabling agencies to adopt out-of-the-box solutions. For example, something as simple as supporting staff to retrieve information using AI-enabled natural language search could reduce the time customers spend waiting on hold while their case worker struggles to locate their file.

Revolutionising social services with agentic AI

The advent of agentic AI could be a tipping point for social services AI use. By virtue of their ability to take multiple paths and iterative steps towards achieving an outcome, AI agents are particularly suited to the type of complex case processing inherent in social services. We can imagine a future where benefit applications are picked up and processed by a team of specialised AI agents that can autonomously validate compliance, determine eligibility and entitlement, identify potentially fraudulent claims, and present reasoned recommendations to human case workers for approval.

Such a future could be just around the corner. predicts that, 鈥渂y 2028, 33% of enterprise software applications will include agentic AI, enabling 15% of day-to-day work decisions to be made autonomously.鈥� Similarly, it notes, 鈥渂y 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention, leading to a 30% reduction in operational costs鈥�.

In summary, AI is already enabling early adopters like Hamburg鈥檚 Ministry of Finance to improve the efficiency of application processing for social benefits. AI adoption in social services is now set to scale as AI capabilities are embedded into enterprise software, and this could lay the groundwork for a big leap forward with agentic AI. AI will be increasingly capable of reducing the 鈥渉assle costs鈥� associated with claiming benefits, helping to ensure that social and economic supports reach the people that need it, when they need it.

The post Leveraging AI to make social services more responsive appeared first on 麻豆原创 Australia & New Zealand News Center.

With so many new generative AI tools hitting the market, all helping to change how we do our daily tasks, let me first explain what generative AI is. Generative AI is a subset that focuses on creating and generating new content. AI-generated content is trained on text, images, audio and other data forms. The data, however, is limited by the quality and quantity of the data used for training, and it is crucial to verify the information the tools provide. There is much hype about generative AI and its possibilities, creating many conversations about AI, ethics concerns and the best way to use the tools. However, other than using the tech to amply and boaster your personal work, AI has many benefits in business.

Business AI is AI applications that exist today, rely on data for learning, are narrow and are measurable. The AI technologies most relevant to business are Machine Learning (ML) (the most applied), Deep learning (CL), computer vision, Edge AI (connected devices) and Natural Language Processing (NLP). ML and DL models ensure the grocery store shelves are fully stocked and help provide insights into sales and customer data. Computer Vision helps detects defects on a production line and helps detect injuries or cancer, aiding in healthcare. Digital assistants (powered by NLP) help us track the status of our orders online and help switch lights in our house. Connected devices help predict maintenance on machines and intelligent checkout in stores and help connect our Garmin or Apple watches to map out our morning run.

Business AI creates the most value when it is scaled across functions. Using business AI can modernise the business environment by streamlining and automating manual tasks, making processes more efficient, predicting future trends by processing and interpreting data across the value chain, providing insights by connecting and making sense of large data volumes and delivering personalised products and services for customers. In a recent report by 28% of respondents said that implemented AI initiatives made an average of AU$ 360k in revenue benefits due to timesaving. However, on of AI projects fail to reach scale.

To reduce the risk of AI project failure, I want to discuss a five-step approach to finding potential AI initiatives aligned with the business. The process can help ensure your organisation stays focused on the right place to apply AI that can benefit your business.

Here are the five steps for undertaking an AI project.

STEP 1: Understanding the business problem or opportunity.

As a starting point, it is essential to have a relevant business problem that can scale across the organisation. I once worked with a business that鈥檚 data team built an AI ML model to predict how many employees would leave. The model accurately predicted 98% of the number of employees leaving. However, HR did not have a process to solve this problem, nor did they use the data from the model. This model then landed up being shelved.

Blindly using AI due to over-eagerness about the technology can make AI projects irrelevant to your business and at risk of failure. When finding AI initiatives, the first step is a clearly defined business problem or opportunity. 聽Articulating the value of solving a business problem could drive investment in technology. For instance, an inefficient process could lead to employee dissatisfaction due to a manual workaround and solving the this can improve satisfaction.

STEP 2: Suitability for AI

Only some initiatives are suitable for AI. Sometimes redesigning a workflow, simple automation, or a rules engine will suffice. 聽Therefore, the second step is determining the initiative鈥檚 suitability for AI. Doing so could help focus internal resources on the relevant use cases. To resolve this, ask the following questions:

Question 1: Is the problem either manual, repetitive, required complex decision making or needs simplifications?

First, you need to understand if the problem defined in step one is either manual, highly repetitive or needs to be simplified. If so, determine if the problems need to do something, such as providing insights, recognising patterns, classifying, automating a task, or predicting future trends. There are inefficiencies everywhere throughout an organisation, and framing the potential AI initiative can help determine suitability. 聽For instance, large data volumes of customers make it difficult to recognise customer segments for targeted marketing campaigns. Finding patterns in the data can help optimise marketing campaigns and personalise messages based on customer data.

Question 2: Do you know what data is needed, available, accessible, and of good quality?

AI is all about data. Data is used to learning about the problem, determine feasibility, develop the models and train the model. Any AI initiative will have a prerequisite for data.

The data required for the AI initiative will be directly related to STEP 1鈥檚 problem. For example, your problem is that the routing service ticket takes too long because it is manually done today. To do this, you must have a historical ticket and routing data available, stored, and accessible. Manual data, such as service ticket data, is excellent for AI. If the data is unavailable and you have a new problem, then the data must be collected before you can continue.

Side note: Companies should have data management practices for running AI, ensuring data control processes, data quality management, privacy management, and governance practices for the entire AI lifecycle.

Question 3: Can the output be measured?

Determining the initiative’s specific outcome, goal, or output and whether it can be measured is crucial. This goes hand in hand with the problem definitions. For instance, if a work process requires a manual workaround, the goal will be to automate the workaround. A manual task that鈥檚 now automated can be measured by determining the time saved.

If all the criteria are met, you have a suitable AI initiative to automate, predict, provide insights, or personalise. Continue to the following step.

STEP 3: Rank the AI initiatives based on business value and complexity.

You want to start by ranking the AI initiatives based on maximising business impact while minimising risks. To do so, it is vital to work with an expert to determine the initiative’s feasibility and complexity of the AI use case. Experts can be data scientists, AI consultants or technology providers that understand your business.

Once the AI feasibility and complexity have been determined, you should see the business value and score accordingly. If the AI initiative business value is high and the complexity is low (HVLC), plan to do these initiatives first. It can help create excitement about the AI possibilities and enable business users to see the value soon rather than waiting years to change their processes. If the AI initiative business value is high and the complexity is high (HVHC), these could be larger projects requiring strategic relevance, business case approval and proper planning. Ideally, you want to mix quick wins (HVLC) and strategic AI initiatives (HVHC). Any initiatives with low complexity and difficulty articulating the benefit should be parked until value increases. Any low-benefit, high-complexity AI initiatives should be removed.

STEP 4: Determine the AI initiatives and technology fit.

To ensure the AI initiative fits with the technology available. Consider the following:

Option 1: Embedded AI

There could be instances where the AI is already embedded into business processes; however, to do so, it could require an update from current to best practice processes, changing the way of working. For instance, the 麻豆原创 S/4HANA cloud has multiple processes that are AI-enabled out of the box.

Option 2: Extend the existing process

To keep the application clean without customising the current system, there could be capability available in software currently licensed. Generally, these are quick-win AI initiatives. For instance, 麻豆原创 BTP offers business-centric AI services to help enable processes such as automatically extracting information from documents and classification, recommendation, and clustering of service tickets.

Option 3: Buy or Build

Leaders must consider whether buying or building the AI initiative is best for strategic AI initiatives. For instance, if the AI initiative is to improve demand planning, purchasing a demand planning solution could be the best approach. Generally, there is not a one size fits all approach, and it will depend heavily on the internal capability of the team and the organisation’s AI maturity. Any decision here would need to go through feasibility reviews, prototyping, finding AI specialists and the complexity of the AI initiative.

STEP 5: AI project iterations to scale

The final step is the start of the AI initiative project and goes into project mode until deployment. During this stage, it is essential to focus on the measure success of the AI initiative. However, AI is not only the focus of IT but should also include the entire organisation. Implementing AI into workflows is a change management activity for businesses and requires focus across the whole organisation (that is a discussion for another time). Implementing AI into workflows is not only about the AI, but there is also a logical path between people, tasks, structure and technology.

Undertaking any AI initiative requires the entire organisation’s support, not just technology. Every organisation should have an AI strategy in place to incorporate AI into the way of working that will change, building internal capabilities, data governance and responsible AI practices.

In conclusion, AI has taken a massive leap in 2023 with the launch of new generative AI, which has created much hype and conversation about AI. However, beyond these creative tools, AI has significant benefits in business. The Global AI market is projected to reach The opportunities for AI technologies to help modernise the business environment are endless. However, to ensure the success of AI initiatives, businesses should undertake a five-step approach to find potential AI initiatives that align with their business problems or opportunities. By doing so, companies can reduce the risk of AI project failure and create significant revenue benefits.

AI@麻豆原创

麻豆原创鈥檚 AI strategy focuses on embedding AI functionality across solutions to enable automated, accelerated, sustainable processes. Extending capabilities by offering business-ready AI services through the 麻豆原创 Business Technology Platform and trusted AI built on ethics and data privacy standards. 聽Visit to learn about our offerings.

Dr. Kim Oosthuizen is Innovation Principal of 麻豆原创 ANZ.

The post The time is now to take advantage of business AI appeared first on 麻豆原创 Australia & New Zealand News Center.

What if this also related to an organisation, a business, service, or product?

There are聽four聽fundamental aspects to EQ (as measured by the聽Emotional聽Competence Inventory, published by The Hay Group): Self-Awareness, Self-Management, Social Awareness, and Relationship Management.

Realising business value from Customer Experience (CX) has always required great intelligence. With聽the latest technology advances and rapidly shifting market trends, a different type of intelligence is required聽to sail smoothly through the highly competitive market.

Many research studies have proven that to make a business successful, you need to retain talent and deliver 5-star customer experiences at every micro-moment. This requires CX professionals to not only have the standard business acumen and intelligence, but emotional intelligence embedded in their technology solutions to win over their online customers.

Expectations of organisations to deliver meaningful, contextual, and positive interactions have never been higher. According to PWC鈥檚 Future of CX report, 1 in 3 customers say they would leave a brand they love due to just one bad experience. Being able to flex your customer journeys with emotional intelligence is an opportunity for organisations to convert more customers, build loyalty and retain their customer base.

What does an Emotionally Intelligent Organisation look like?

Experience management enables you to determine the emotional tone that represents experiences and is used to gain an understanding of the opinions, attitudes, and emotions expressed by a visitor or customer as it relates to the brand, product, user, or service experience.聽This type of data capture and analysis in conjunction with operational data such as; stock availability, orders, cart abandonments, and shipping information can put CX leaders in a powerful position to make informed business decisions that directly address customer sentiment and expectations.

Emotional power

Most people think that they act consciously while they go shopping, but studies have reflected that about 95% of purchasing decisions whether online or offline are based on emotional impulses. They are influenced emotionally and visually and online shoppers are likely to feel three times more excitement while adding various items to their baskets. Scientists believe there are 27 emotions and negative emotions play a huge role in decision-making.聽CX product owners must utilise emotional intelligence and alter their customer journey to offer a better experience. But first, you have to capture this intelligence and derive meaningful insights. Find out more about Intelligent Shopping Solutions at聽

Trusted Preferences

70% of customers will provide personal preference information if they trust the source and receive something of value, for example, personalised products that actually meet their needs or expectations. Being one step ahead of the legislation creates an opportunity to differentiate and empower your customers to be in control of their data. Capturing customer data and potential customer expectations allows you to then analyse actual experience data to identify moments for conversion and experience optimisation. Learn more about聽

Conversion optimisation

Whilst some analysts say that this is an old technique, I am still a fan of A/B testing. This allows the Customer Experience owners to choose the best design or journey versions that yield the best conversion rates. With this technique, users are shown varied experiences, and their reactions toward different models are measured accordingly. Making sure you can capture not just the interaction data, but the experience data is crucial to making informed decisions on the best variants or how to optimise further.

When it comes to eCommerce organisations need to be agile to constantly test, track, adapt, and review different experiences to optimise conversions. A key enabler to this is a headless experience layer.聽聽on the benefits of headless commerce.

Identifying future customer needs

Retaining customer details, auto-fill options for addresses, and offering incentives to people who are likely to return are great options to make things easier for customers. Being able to predict what the person wants in the future also helps a lot. Some tools that work are:

1.聽Reviews, likes, and suggestions from other shoppers often add a social component.

2.聽Maintaining transparency in purchases often creates a feeling of trust and security.

3. User-friendly experience makes the user feel that they are purchasing tailor-made products that match their style and taste.

Using a Customer Data Platform allows organisations to achieve a true single view of your customer by combining data from across the enterprise.聽Responsibly manage profiles with trusted governance and privacy controls for known and anonymous identifiers. Generate actionable insights and scale audiences with AI and machine learning and then deliver personalized experiences in real-time across all channels and destinations.聽Find out how聽聽could be beneficial to your organisation.

To conclude, we can say that developing a continuous program to understand not just action, but tone and sentiment enables Customer Experience programs to be more successful and helps to optimise the conversion rates, creates a better experience for customers, and taps into emotional intelligence.

The post Emotionally Intelligent Customer Experience appeared first on 麻豆原创 Australia & New Zealand News Center.

A cohort of both education and technology partners have come together with the aim of launching NSW as the nation鈥檚 leading provider of digital skills, combining world-class facilities with an industry-driven education model.

As an industry partner, 麻豆原创 is supporting the institute in co-designing and delivering specific courses in key disciplines such as data management, software development, and cloud computing.

Students of TAFE NSW and the IAT will have access to a number of free learning modules via , and , and from mid-year, the first two 麻豆原创 courses will be available at the IAT:

• S/4 HANA Financial Accounting for 麻豆原创 Consultants
• S/4 HANA Management Accounting for 麻豆原创 Consultants

Additional courses will be added as the IAT matures, and are specifically being designed to focus on areas of skills shortages.

Damien Bueno, President and Managing Director, 麻豆原创 Australia and New Zealand said on the opening:

鈥淲e need more, and better, digital skills in Australia, but reaching the numbers and level of skill needed requires us to invest in new and different ways of finding and fostering talent.

鈥淲hich is why we鈥檙e really excited to partner with the Institute of Applied Technology (IAT) 鈥� Digital, at TAFE NSW Meadowbank. More than 800 students are due to be trained in 麻豆原创 skills through the Institute over the next five years, which will bring new capabilities, value and experience to our customers and the broader 麻豆原创 ecosystem.鈥�

You can read more in the media release .

The post 麻豆原创 supports the future of digital skills training appeared first on 麻豆原创 Australia & New Zealand News Center.

The innovation specialist: Dr Kim Oosthuizen, 麻豆原创 on where ChatGPT fits into the AI mix

ChatGPT is just the latest and most notable of a set of generative IT tools and its fit in the spectrum on the market that can be applied to multiple media types. Dr Kim Oosthuizen, Innovation Principal, Customer Transformation Advisor at 麻豆原创 unpacks where it sits in the spectrum.

Read more on Mi3

The post Buckle up! The ChatGPT disruption is just the beginning appeared first on 麻豆原创 Australia & New Zealand News Center.

There鈥檚 still much confusion surrounding the fundamental questions of , what is AI, what is capable of and what isn鈥檛 it capable of. Many of us were introduced to AI through pop culture depictions such as the friendly robot Rosie from The Jetsons or the rogue servant droid Sonny from I, Robot, which has set unrealistic expectations for their use.

Kim summarises AI in the simplest terms as 鈥渃omputational agents that act intelligently.鈥� Performing tasks by using data, algorithms, and programs and processing a specific output or goal. Classified as 鈥榥arrow鈥�, the AI that exists today can only perform the task in the simple or specific domain its programmed to do so in, it can’t do anything outside of that.

As AI is merely an umbrella term for the vast array of intelligent technologies that exist, we can classify three categories of machine learning AI:
1. Analytical AI: Intelligence that we see 90% of businesses use today, AI that predict, recommend and mine data and learns from past experiences
2. Human Inspired AI: Intelligence with some extent of sentiment and reaction that must be programmed, e.g. chat bots
3. Humanised AI: Intelligence that understands human emotions and has considerable empathy to respond to the end user in a human-like or natural manner, potentially a technology that will be available very far into the future.

鈥淲e have a bit of a tendency at the moment to call everything intelligent just because it’s software.鈥�

Passing the Turing Test
Speaking to Pete about the real world applications of AI, he made one thing clear, 鈥測ou can’t call it AI unless it’s passing the Turing test in some way鈥�. Referring to the test created by Alan Turing to determine whether or not a computer is capable of thinking like a human being. Using this logic as the foundation of our understanding of AI, we can apply this narrow technology to the business world of today.

鈥淭hrough a machine learning angle, AI can detect patterns that you wouldn’t normally decipher through traditional statistical algorithms. An example would be, we鈥檝e got a client that was using this technology to process applications for grants. They had a massive backlog that came up unexpectedly, and they used this machine learning to recognise what was tending to get approved and could identify and accelerate those application.

In order to keep things safe and apply their principles, they use the rule that the machine can say yes to something that is beneficial to the customer, but if the outcome is no, then it goes to a human to get double checked and processed. That’s the kind of thing that companies are doing to protect us from the AI getting it wrong and people being disadvantaged by this kind of technology.鈥�

https://www.youtube.com/watch?v=SVMG6tOkbF4

A benefit to businesses
Kim was able to concisely offer three of the major benefits that implementing can offer business and dramatically change their day to day processes.

鈥� With multiple data touch points across a businesses value chain, there is a risk of creating data complexity. AI acts to process that information and provide analytics and insights into what the data is saying and alternatively give insights into customers and sales alike.
鈥� Customers expect that businesses are online 24/7 to answer queries which can be combated by the use of conversational AI and remove the necessity for a human to answer every query.
鈥� With recent supply chain disruptions, this situation has created quite a lot of inefficiencies in operations and AI can assist by streamlining some of these processes by taking over the manual tasks and automating them to enable decision making.

Pete couldn鈥檛 agree more following on with the sentiment that 鈥渁 lot of benefit can be achieved just by using logic and automating simple processes with the application of basic logic. I think people get confused with the notion that automating a process means you are using AI which isn鈥檛 the case. AI comes into play for very specific areas where you need something computed that something beyond simple logic to solve or decipher.鈥�

鈥淚f a human can’t do it, it’s gonna be very hard to get an AI to do it.鈥�

Unlike what the movies have made us believe, the true benefit of AI for businesses is the opportunity to allow the human worker to contribute to the process with the most value and leaving the relatively repetitive and tedious work to the intelligence to automate.

The binary function of humans and technology doesn鈥檛 exist in silos, one cannot exist without the other. With a balanced approach of human led and technology led workflows, incorporating artificial intelligence into your business is far from out of reach.

To hear more from our discussion about Artificial Intelligence, its challenges, responsible use and the future use of AI in business, listen to the

The post AI in Business: What You Need To Know appeared first on 麻豆原创 Australia & New Zealand News Center.

The diverse group of independents, Greens and progressive candidates elected to the new parliament bodes well for Australia鈥檚 tech industry and for its burgeoning sustainability efforts, according to one of the nation鈥檚 most senior technology executives.

Local president and managing director of German multinational enterprise giant 麻豆原创, Damien Bueno, said the new climate-focused parliament would mesh well with his company鈥檚 sustainability agenda, which included software to help businesses achieve net-zero emissions. The change also would help Australia鈥檚 technology sector more broadly.

鈥淎s far as the number of independents being elected and the Greens as well there鈥檚 clearly a mood for change,鈥� Mr Bueno said. 鈥淎nd climate is very much at the heart of that. So for us that鈥檚 something we鈥檙e excited about, because both in the community we operate in Australia and as a global citizen, we鈥檙e very engaged with that topic and with creating solutions.

Read more of this article from The Australian

The post [Moved to media coverage]Progressive parliament good for technology industry, says 麻豆原创鈥檚 Damien Bueno appeared first on 麻豆原创 Australia & New Zealand News Center.

On the one hand, new technologies and possibilities generate excitement and a sense of wonder. On the other hand, we feel anxiety when faced with multiple emerging trends, uncertainty, and change.

How can organisations guide employees to navigate a jungle still growing wild? Above all, how can we achieve a sense of inner calm and perform purpose-led, deep-work?

Predict vs Prepare
There is no dearth of predictions about the Future of Work, especially how AI and automation would transform man’s relationship with work and life as such. Even a century ago in 1932, renowned philosopher Bertrand Russel said, “in a soon-to-be automated world, ordinary men and women, having the opportunity of a happy life, will become more kindly and less persecuting鈥� and even lose their 鈥渢aste for war鈥�.

Around the same time, John Maynard Keynes predicted that by the early 21st century, we would work only 15 hours per week. Alas, we now know economic prosperity does not necessarily lead to more kindness. Nor that automation has led to less hours of work per day. It serves us well to prepare for the future than to predict it.

Gain Perspective
However, if we are to construct a view of future work and skills landscape, we better step back in time to gain a broader perspective, using the lens of our evolutionary past. James Suzman, through his fascinating book: Work – A History of How We Spend Our Time – takes us back by 300 thousand years to illustrate a strong correlation between work and our evolution.

He explains how for our ancestors, work wasn鈥檛 a way to spend energy, rather to capture it from the environment (by mastering fire and cooking). The work they did and tools they created shaped Homo Sapiens physically and neurologically. As a species we “became skilled at acquiring skills”. Another key insight is how we adapted to change by being generalists.

This is relevant for us here and now. David Epstein’s recent book: Range – how Generalists Triumph in a Specialised World – extols the virtues of ” breadth, diverse experience, interdisciplinary thinking, in a world that increasingly incentivises, even demands, hyperspecialisation”. Meta-learning, cross-domain knowledge, curiosity, and grit are essential for the new age worker.

Radar and Navigator
My own radar equipment is my . I pay attention to contemporary best practices and practitioners – books like Tribe of Mentors and podcasts like The Knowledge Podcast. One cannot fail to notice frequent references to neurochemistry, with fresh ideas on personal mastery and peak performance made easily consumable by the likes of Andrew Huberman and Steven Kotler.

To navigate the unknown and uncertain, I leverage my network. Being part of a large and diverse organisation enriches the individual. Reaching out and tapping the collective intelligence has helped me deal with complex situations at work. After all, we are social animals, and we thrive when our peers validate our ideas.

Organisations that facilitate serendipitous networking in teams across boundaries stand to gain in the long term. I particularly remember my earliest trip to the 麻豆原创 headquarters when my manager made me work out of the coffee area for a week, to nudge me to connect with many team members. 麻豆原创鈥檚 recent research into this topic includes Network as one of the six drivers for .

In the new age of work, individuals accomplish personal mastery, while the team achieves collective differentiation to stay ahead of time.

Back to the future
In the end, there is no crystal ball to predict what the future of work holds for each of us. Learning from history, we know we are learning machines. And we have the radars and navigators to rely on. With a refined focus and clear vision, possessing the curiosity of a child, let us embrace the new age of work.

William Gibson said, “The Future is already here, it is just not evenly distributed”. It is time to bring everyone, across organisations to be ready for the future. Let us ride along with Marty McFly and Doc Brown, powering up the DeLorean to get Back to the Future.

The post New Age Of Work 鈥� A Blast From The Past appeared first on 麻豆原创 Australia & New Zealand News Center.

Creative machines are the future
Noting that South Africa granted the first patent to a creative AI system in 2021, researchers predicted creative AI systems will win dozens of patents in 2022. They were quick to point out that 鈥淎I will not own the products in the traditional sense 鈥� the developers of the AI systems鈥ill still enjoy commercial benefits. But companies will experiment with creative AI, knowing that these innovations may be legally protected.鈥�

analyst Diego Lo Giudice looked ahead to 鈥淎I 2.0鈥�, which he saw fueling the development of new creative business applications, exceeding basic expectations of AI to free up workers for greater creativity. He predicted that AI would connect business processes across silos to boost business creativity, and wrote that 鈥淎I鈥檚 new business models can optimize the convergence of the digital world with the physical world and drive the 鈥榓nything as a digital鈥痵ervice鈥� trend. The impact on customer experience could be huge.鈥�

Explainable AI is not your typical IT project
Explainability and ethics were among the most widely discussed AI-related issues. analyst Srividya Sridharan, saw the market for responsible AI solutions doubling as industries adopt 鈥渞esponsible AI solutions that help companies turn AI principles such as fairness and transparency into consistent practices.鈥� And for those who fear these algorithms will run amok, analysts predicted that by 2025, to reduce reputational risks, 40% of G2000 companies will be forced to redesign their approaches to algorithmic decision-making, providing better human oversight and explainability.

AI explainability is one of topics that Ian Ryan is exploring as global head of the 麻豆原创 Institute for Digital Government. It鈥檚 part of a for the Australian public sector on the value and impact of technologies for employees, citizens, and society at-large. Ryan said that while AI can help deliver citizen services with that coveted Amazon-like experience provided organisations set value-based, measurable objectives.

鈥淎pplications of AI consist of the person using it, the AI model itself, and the information that comes from reality; these three pieces have to be sync, targeted towards driving a particular outcome,鈥� said Ryan. 鈥淵ou need to train the AI model continuously, keeping it updated so it can support workers as situations evolve, engineering out any bias while preserving data protection.鈥�

For business value, AI algorithms cannot function as an inscrutable black box or operate autonomously. Ryan said organisations need to build explainability into complex AI models, and involve targeted employees in AI model development.

鈥淎I supports employees by taking tasks away, allowing people to be more productive and focus on high-value activities,鈥� he said. 鈥淎l can access huge amounts of seemingly disconnected data from different places, and identify patterns in seconds, far faster than humans. Explainability provides the transparency people can trust, so they鈥檒l share their data knowing the organisation will protect it and deliver value.鈥�

Indeed, analysts warned that while AI will help companies emerge from the pandemic in a strong position, merely adopting AI won鈥檛 be enough. Companies need to operationalise updates to AI models, 鈥渦sing integrated data and model and development pipelines to deliver consistent business value from AI. It combines automated update pipelines with strong AI governance.鈥�

AI becomes ubiquitous
Numerous analyst predictions tout the benefits of AI in strengthening human decisions. researchers said that 85% of enterprises will combine human expertise with AI, machine learning (ML), natural language processing, and pattern recognition to augment foresight across the organization, making workers 25% more productive and effective by 2026. researchers saw 30% of organisations using forms of behavioral economics and AI/ML-driven insights to nudge employees’ actions, leading to a 60% increase in desired outcomes by 2026.

More near term, expected traditional businesses with take a page out of digital native practices during the pandemic with an AI-first approach to platform and digital transformation. He said that 鈥渢he more AI inside, the more enterprises can shrink the latency between insights, decisions, and results.鈥�

Unlike many previous innovations, AI is not an isolated technology that organizations can surgically apply and wait for the magic to happen. AI is an extraordinarily powerful technology with profound ramifications for organizations and people as they discover it鈥檚 true business potential.

This article originally appeared on

The post AI Trends 2022: Spare Us The Hype, We Want Business Results appeared first on 麻豆原创 Australia & New Zealand News Center.

Yet compared with many private sector industries, it鈥檚 fair to say that public sector adoption of AI technology has been more measured. Governments and other public sector organisations face a number of significant challenges, from the availability of skills and investment funding, to demonstrating value and ensuring transparency about how decisions are made.

These challenges are reflected in the 麻豆原创 Institute for Digital Government鈥檚 latest report 鈥撀犅犫�� developed in partnership with the University of Queensland. While 80 per cent of public sector organisations are actively working towards data-driven transformation, fewer than 15 per cent have progressed beyond prototypes.

In order to drive greater uptake, the public sector needs to develop best practice frameworks and solutions for the development and use of AI systems that are accurate, robust, and scalable, but also reliable, fair, and transparent.

When building AI systems to meet these high levels of expectation, it鈥檚 vital that public sector workers are able to understand how these systems generate decisions and explain how this impacts results. This is known as AI explainability.

Read more of the article on Government News .

The post Opinion: Building explainability into AI projects appeared first on 麻豆原创 Australia & New Zealand News Center.