Key Insights听

1. The AI Revolution听The emergence of like ChatGPT-3 has revolutionized how businesses approach AI. The public鈥檚 access to advanced AI tools has sparked innovation and competitive growth. According to Deloitte, 94% of organizations recognize AI as critical to their future success, with 65% already integrating AI into their operations. This shift signifies not just a trend but a fundamental transformation in business operations and strategies.

2. Benefits of AI in Business听麻豆原创 identifies five major benefits of AI:

• Business Resilience:听AI helps organizations adapt to market changes and disruptions swiftly.听

• Enhanced Customer Service:听AI-driven chatbots and support systems provide instant, efficient customer service.听

• Confident Decision-Making:听AI analytics offer deep insights, enabling better strategic decisions.听

• Relevant Products/Services:听AI helps in tailoring products and services to meet customer needs more effectively.听

• Engaged Workforces:听AI automates routine tasks, allowing employees to focus on more engaging, value-added activities.听

By 2025, 50% of end-users are expected to use AI-infused applications, transitioning from systems of record to systems of intelligent planning. This shift underscores the importance of integrating AI into systems like 麻豆原创 for sustained business growth and efficiency.听

3. The Importance of Data Quality听AI鈥檚 effectiveness hinges on the quality of data. Poor data quality leads to inaccurate AI outcomes, emphasizing the adage, 鈥淕arbage in, garbage out.鈥� A coherent data strategy, underpinned by robust data governance and management, ensures that AI delivers consistent and valuable results. Ensuring data accuracy, consistency, and reliability is paramount to achieving meaningful AI-driven insights and operations.

Practical Application and Examples听

SimpleMDG听is a powerful solution for managing master data across 麻豆原创 and non-麻豆原创 systems. Its simplicity, integration capabilities, and supportability ensure data quality, particularly in 麻豆原创 environments, making master data accurate, consistent, and AI-ready, thereby enhancing AI insights鈥� reliability.听

While some examples below may not be in the whitepaper, they illustrate how high-quality master data within 麻豆原创 drives innovation. Integrating SimpleMDG with 麻豆原创 can be the cornerstone of a robust data strategy, enabling exceptional results and continuous innovation with Business AI.听

Real-world Applications听

• Multinational Corporation:听A multinational corporation used SimpleMDG to streamline master data management, significantly improving data quality. This led to more accurate AI-driven forecasts and better decision-making processes.听

• Global Manufacturing Firm:听A global manufacturing firm integrated SimpleMDG with its 麻豆原创 ERP system to automate data validation processes, reducing errors and operational inefficiencies.听

Predictive Maintenance in Manufacturing听

• Leading Automotive Manufacturer:听A leading automotive manufacturer used AI to predict equipment failures before they occurred. By leveraging high-quality master data with 麻豆原创 solutions, the company reduced downtime, optimized maintenance schedules, and saved millions in operational costs. The predictive maintenance model relied on accurate data from sensors and historical maintenance records, showcasing the power of a robust data strategy in enabling effective AI solutions.听

Customer Insights in Retail听

• Major Retail Chain:听A major retail chain utilized AI-powered analytics to gain deeper insights into customer behavior. By integrating customer data with 麻豆原创 and ensuring quality Master Data, the retailer created personalized marketing campaigns, improved inventory management, and enhanced customer experiences. The ability to analyze large volumes of accurate data allowed for more targeted and effective business strategies, resulting in increased customer loyalty and sales.听

Supply Chain Optimization听

• Global Shipping Company:听A global shipping company used AI to optimize its supply chain operations. Employing a robust data strategy with 麻豆原创 and quality master data, the company analyzed real-time data from various sources to predict demand, manage inventory, and streamline transportation routes. This improved efficiency, reduced costs, and enhanced customer satisfaction by ensuring timely deliveries.听

Financial Forecasting in Banking听

• Leading Financial Institution:听A leading financial institution applied AI to improve financial forecasting and risk management. With a strong data strategy ensuring high-quality data inputs, the bank used AI algorithms to analyze market trends, predict financial risks, and make informed investment decisions. The integration of AI with 麻豆原创 systems facilitated real-time data analysis, providing the bank with a competitive edge in the fast-paced financial market.听

Conclusion听

In today鈥檚 fast-paced business environment, integrating AI into strategies is essential for transforming operations, driving innovation, and enhancing decision-making. However, the success of AI applications hinges on the quality of data.听

To maximize AI鈥檚 potential, organizations must implement a robust data strategy ensuring accuracy, consistency, and reliability. Master data governance tools like SimpleMDG are crucial for managing data effectively, supporting AI with high-quality information.听

Key aspects of master data governance include:听

• Data Accuracy: Essential for AI algorithms to deliver correct insights. Inaccurate data leads to flawed results and poor decisions.听

• Data Consistency: Crucial for reliable AI outcomes. SimpleMDG helps maintain consistency across systems, avoiding disruptions.听

• Data Reliability: Foundational for effective AI applications. With SimpleMDG, organizations can trust their data to provide actionable insights.听

As digital transformation progresses, prioritizing data quality is essential to unlocking AI鈥檚 full business potential. A robust data strategy not only boosts AI capabilities but also secures a competitive edge and fosters ongoing innovation.听

To conclude, integrating AI with effective master data governance is vital for leveraging AI鈥檚 full potential and by ensuring data accuracy, consistency, and reliability, we enable the path for significant success in business outcomes and staying ahead in the听鈥楢I Everywhere Era鈥�.听

Call to Action

To delve deeper into how a robust data strategy can unlock the full potential of AI in your organization, download our comprehensive whitepaper, 鈥淯nlocking AI Opportunities with a Data Strategy through 麻豆原创 and SimpleMDG.鈥� Discover actionable insights, practical examples, and expert recommendations that will guide your AI journey.听

Acknowledgements

Author:听

• Jon Simmonds.听VP Professional Services, Laidon Group.听

Contributors:听

• Matthew Phu.听Founder and CEO, Laidon Group.听

• Robin Shadgett.听Customer Success Director ANZ, Laidon Group.听

Title:听

• Cathy McGurk.听Director of Solution Advisory, 麻豆原创 BTP, Australia & New Zealand.听

Additional Contributors 鈥� Review and Feedback:听

• Varun Thamba.听Regional Director, 麻豆原创 Business AI Strategy: Asia Pacific & Japan.听

• Jared Yabanci.听Solution Advisor, 麻豆原创 BTP (Data), Australia & New Zealand.听

• Liam Mischewski.听Solution Advisor, 麻豆原创 BTP (AI, Innovation), Australia & New Zealand.听

This content was originally published on the SimpleMDG blog on June 29, 2024. The original article was written by Jon Simmonds, VP Professional Services, Laidon Group. Republished with permission from SimpleMDG.

The post Unlocking AI Opportunities: Why a Robust Data Strategy is Critical in a 麻豆原创 World appeared first on 麻豆原创 Australia & New Zealand News Center.

AI serves as a powerful tool for extracting actionable insights from the vast amount of reliable data generated and stored within 麻豆原创 systems.听 Combining AI with 麻豆原创 BTP, advanced data analytics and machine learning algorithms becomes possible, allowing organisations to tap into the potential of their 麻豆原创 data along with the AI technologies available in the market.

By harnessing artificial intelligence, 麻豆原创 BTP combines business data from S4 with external data, enabling the creation of increasingly precise models in real-time.听 This ensures a versatile and agile platform that propels innovation while retaining a clean digital core. This demonstrates a shift from traditional systems of record to systems of intelligence.

The Pillars of 麻豆原创 BTP

Five pillars support 麻豆原创 BTP: App development, automation, integration, data analytics, and AI. These pillars interplay with embedded intelligent technologies like situation handling, machine learning, and analytics, all fully integrated within the 麻豆原创 S/4HANA Cloud. Furthermore, side-by-side capabilities through 麻豆原创 BTP offer additional intelligent industry functionalities like Intelligent Situation Automation, 麻豆原创 Build Process Automation, and chatbot technology.

听

AI-Powered Capabilities听

麻豆原创 has embedded AI into its products for many years, from journal reconciliations in S4 to AI-powered writing assistants aimed to streamline HR-related tasks in Success Factors.

These innovative functionalities are not merely theoretical but are practically applicable, ensuring HR admins, managers, and employees can operate more efficiently.听 In fact, these innovations exist across all the functions in your 麻豆原创 landscape such as procurement, finance, and human resources.

How to get started?

Automation is pivotal for managing manual and repetitive tasks, especially those involving the consolidation and manipulation of data from diverse sources like MS Excel, vendor portals, and 麻豆原创 systems. High-volume processes, often exceeding 1000 steps a day鈥攕uch as data migrations and approvals鈥攁nd those requiring access to multiple applications, can be streamlined, ensuring seamless operation across your 麻豆原创 environment.

麻豆原创 has provided templates across all the business functions to accelerate these initiatives, as shown below.

A More Advanced AI Use Case

The transition from a rules-based approach to an AI-empowered, data-driven model is illustrated through an example case study of an Australian customer.听 A decade ago, an employee scripted manual 鈥渋f:then鈥� statements for road upgrades; a process that has now been revolutionised by AI. AI can now analyse these rules and infuse them with real-time data like weather, road usage, and vehicle types. As a part of their operations, this customer assesses road conditions using specialised trucks called profilometers, generating colossal data volumes that outpace their storage capacities. 麻豆原创 BTP, however, can house this data in expansive lakes, giving AI the agility to model exponentially precise 鈥渋f:then鈥� statements.

The shift will allow this customer to manage large datasets from disparate sources seamlessly, scaling memory and compute capabilities to handle big data without losing granularity. Moreover, unlike fixed rules, the AI algorithms continually evolve based on data, thereby ensuring maintenance and road upgrade strategies that are timely, relevant, and efficient.

In the realm of road maintenance, AI鈥檚 practical application is manifest, where even a small percentage improvement can result in significant savings for this customer.听 This financial efficacy, combined with the potential to extend the useful life of assets, underscores the tangible, impactful benefits of combining AI with 麻豆原创 BTP.

In Summation

Early AI integration can offer businesses a decisive advantage. 厂础笔鈥檚 AI vision isn鈥檛 just about pioneering technology; it’s about tangible, real-world applications. From simple tools deployable within days to intricate endeavours with broad impact.

If you鈥檇 like to find out about the value AI can bring to businesses through automation and explore other use cases, then visit the website.

The post Why Business Data is Fundamental to Artificial Intelligence appeared first on 麻豆原创 Australia & New Zealand News Center.

AI is revolutionising industries and ushering in a new era of work for users, businesses and more. While some organisations are already using it for small pilot projects, the real opportunity lies in exploring its ability to scale. 听

In June of this year the Australian Government published its discussion paper on AI technology, called . What it revealed is that AI is a critical technology that can help to drive productivity growth in Australia. From hospitals using it to consolidate large amounts of patient data, engineers using it to evaluate and optimise designs, to using AI-enabling improvements and cost savings in the provision of legal services, it鈥檚 clear all industries will benefit from AI.听

Australia鈥檚 economy is currently experiencing low productivity growth, its lowest for 60 years. We have an opportunity, even an obligation to, embrace this technology at all levels, across all industries to increase our economy鈥檚 output.听

At 麻豆原创, we鈥檙e bringing transformative intelligence to organisations through across both private and public sectors. For example, is using 麻豆原创 machine learning capabilities to predict those taxpayers that are at risk of defaulting. This is enabling them to proactively build personalised payment plans and deliver more timely revenue collection to the government. On the other hand, , one of Australia鈥檚 largest workplace solutions companies, is using 麻豆原创 intelligent robotic process automation to automate repetitive administrative tasks, freeing up its workforce to focus on more strategic and value-added activities.听

The need for AI regulation听

As the adoption of AI accelerates there is a growing discussion, prompted by the government鈥檚 recent discussion paper, around what steps must be taken to regulate it.听

While recent discussions suggest the technology industry opposes regulation, while citizens advocate for greater protections, the reality is more nuanced.听

For our part, 麻豆原创 does not oppose regulation if there is harm that can be best managed by it. In fact, in most cases existing regulations are suitable for addressing some of the issues raised by AI. For example, a core principle of Australian Consumer Law is that businesses must not engage in misleading or deceptive conduct. We do not need new AI specific laws to prohibit the use of generative AI to be used to create misleading images in a consumer protection context.听

Furthermore, while AI is a complex technology, the issues arising from the impacts of AI on intellectual property rights are very different from those related to businesses using AI to determine the suitability of a loan application.听

厂础笔鈥檚 view is that a broad-based regulatory approach to addressing potential harms is not suitable.听

厂础笔鈥檚 risk-based approach to AI听

To mitigate and manage the risks associated with AI, 麻豆原创 recommends the following:听

1. A risk-based sectoral approach rather than top-down broad-based regulatory intervention: 麻豆原创 recommends that regulators or policy makers undertake a risk assessment to determine whether to intervene based on end-user implications. For example, consumer protection policy makers and regulators should assess what the risks are facing a consumer who uses an AI-enabled driverless car.听听
2. Establish an AI centre of excellence: AI is a fast-moving technology with impacts across all government portfolios. To avoid overlap, inconsistency, duplication, and conflict, 麻豆原创 recommends establishing an AI centre of excellence to advise and coordinate whole of government policy responses and apply a common set of principles to its application. As Ed Santow Professor & co-director, Human Technology Institute, University of Technology Sydney noted 鈥�A significant part of the problem in the AI era is not the content of our law, but the fact it is not consistently enforced when it comes to the development and use of AI.鈥�听

Government accountability does not end there. You should not regulate what you do not understand. Government must also be an exemplar in its use of AI, investing in AI across every portfolio and using it to improve its operations and citizen services. 听

At the recent 麻豆原创 NOW event in Sydney the former NSW Minister for Customer Service Victor Dominello reinforced this, noting that all agencies must prioritise experimenting and trialling new applications of AI technologies in their operations and policy challenges.听

The path forward requires industry collaboration听

麻豆原创 looks forward to engaging with the Government鈥檚 engagement regulatory responses to AI. However, we should not lose sight of the pressing public policy challenge of low productivity facing Australia. We know that AI technologies help us do more with less, so let鈥檚 focus on how we can accelerate our economy鈥檚 adoption of this technology. 听

For more information, read 厂础笔鈥檚 response to the Australian Government鈥檚 Safe and Responsible AI discussion paper here.听

The post Regulating safe and responsible AI for Australian businesses and government appeared first on 麻豆原创 Australia & New Zealand News Center.

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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. 听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. 听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. 听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

厂础笔鈥檚 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. 听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听four听fundamental aspects to EQ (as measured by the听Emotional听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听the latest technology advances and rapidly shifting market trends, a different type of intelligence is required听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.听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.听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听

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听

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.听听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.听Reviews, likes, and suggestions from other shoppers often add a social component.

2.听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.听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.听Find out how听听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.

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

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The future of business is paperless, and it is coming faster than anyone imagined, with the rate of automation having increased fivefold during the pandemic lockdowns. Old fashioned Accounts Payable (A/P) models of pieces of paper flowing through businesses seeking approvals, coding or additional information have rapidly become impossible to operate with the large scale Work from Home movement.

will only continue to gather momentum as more businesses recognise the inevitable. Tremendous improvements in are making the decision to transition to an automated solution easier than ever.

Businesses understand that they must have leaner A/P staffing to reduce overheads, and the productivity gains and enhancement from mobile, cloud-based A/P automation tools is now impossible to ignore. The repetitive nature of A/P processing and the inherent time-consuming drudgery involved can be eliminated, releasing the employee to move to more value adding tasks, improving both employee morale and productivity in a single stroke.

and into the future will both expect and demand that businesses utilise the most modern technology available to empower them to do the role to their upmost ability. Only by doing so will businesses be able to attract and retain the best talent.

What are the key factors in successful

Mobility 鈥� As the need for people to conduct businesses wherever they are (office/home/caf茅/interstate) only increases, so does the importance of mobile capabilities in systems increases. Such mobility enables managers to approve invoices wherever they are, vastly speeding up the process which improves organisational efficiency. The mobility factor gives decision makers within the organisation real time visibility into exactly what is happening which enables better decision making. Both cash flow and P&L forecasting are vastly improved.

AI driven automation 鈥� Another important productivity enhancement tool is to utilise increasingly sophisticated machine learning in the pursuit of improved productivity and accuracy within the finance department. From extracting and populating key data, suggesting account coding or even working out complicated departmental splits the utilisation of A-I to facilitate the speed of transactions is a key part of the automation productivity story.

Supplier portals 鈥� Another key enhancement which adds value both internally and externally is the ability to give key suppliers visibility and insight into payment information, saving time for an organisation’s staff in answering queries and improving and deepening the relationship between businesses.

ERP integration 鈥� A necessary and standard feature required to ensure productivity gains are achieved is a seamless integration into the ERP system. This ensures accuracy of data and allows the real time visibility provided by A/P automation to flow through the entire organisation.

Further benefits accrue to businesses in regards increased transparency of data, ease of data storage and retrieval, minimising time and cost of audit, enhanced accuracy and importantly, reduced risk of fraud.

The future of successful business lies in automation of key manual process to provide efficiency. To scale and grow profitably manual paper-based processing must end. offers the means to achieve this digital transformation now. The pandemic has only accelerated the process.

Access to all the experience and expertise 麻豆原创 Concur has gained in assisting thousands of businesses to go paperless and move business processes to the Cloud is only a click away, as you can . Act now and a digital A/P process and an improved business is as little as 6 weeks away.

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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.

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What was your first job? Did you deliver? Newspapers? Babysit? mow lawns? Or work at the shops?

While I remember the smiling faces of the kids I got to work with, I also remember the frustration. Filling out a paper timesheet so I got paid on time, reviewing my schedule on a whiteboard, and picking up a physical check. These paper-based tasks led to inefficiencies, and I knew there must be a better way.

Bringing us back to 2021, we鈥檝e certainly come a long way. Hopefully, your timesheets are automated, your scheduling is digitised, and – do checks even exist in Australia anymore? Our smartphones, watches, voice assistants, computers, and myriad of devices ensure that our daily lives and movements are tracked. Recommendations for being more efficient are made at each moment. We鈥檙e now able to operate at a different pace.

With these advancements in technology, the ways that we work have evolved, and in a lot of ways, we could say that the future of work is already here. We鈥檙e humans and we’re constantly looking at what’s going to happen next, and that’s where the future of work truly comes in.

At 麻豆原创, we know that the way you work is constantly changing which is why we鈥檙e consistently looking at what the future of work means. Laptops outsold desktops for the first time in 2005, in 2015 麻豆原创 launched , and in 2020 virtual conferencing finally became more popular than traveling.

But as we shift beyond the now 鈥� what does our future hold?

First, we see people and computers working together with human computer augmentation. We’re putting resources into your hands for how to make better, smarter, faster decisions. No longer do you have to wait days for the reports that you need to find out the information about how your business is running.

The information you need to make real-time decisions for your business and keep you moving at scale is ready now. And nothing was more evident than when we had to transition an entire organisation from working in offices to working at home. Your teams are now made up of two things human intelligence and . These AI-Powered Superteams allow everyone to take the best role possible.

Transforming How We Work

Next, we see a transformation in where we work and whom we work for. People resources continue to be scarce, and we see a drive away from the traditional 9 to 5. The shift towards project-based hiring requires new approaches. We need to shift from billing for people in seats to delivering on organisational outcomes. Similarly, in COVID-19 we saw a massive flight out of cities. People are looking for business to move with them. This opens opportunities to connect people beyond geographies. This Jobful Future creates a collaborative structure where each contributor is equally valued, and each person feels deeply connected to solving the problem brought about.

Traditionally, we talk about diversity as having different genders, country of origin, age or identity. This leads to counting up differences, now our goal is to create work environments so people can Inclusively Belong. Beyond a simple checklist, inclusivity means we are acting to learn from the experiences that our differences harness.

In 2020 surveyed a cross sectional group of people and found that 1 in 5 identify as 鈥淎 Passionate鈥�. care not only about what businesses are providing but how they provide it. Across all topic areas, The Passionates require businesses to take meaningful action, and they are vocal about the change they want to see. As these groups enter boardrooms, they mandate social action, and businesses that don鈥檛 anticipate this change will be left behind. Inclusivity is paramount for any organisation in the future.

Finally, the biggest change we will feel in the shortest amount of time is how we react to recovering the 114 million jobs lost during the COVID-19 pandemic. These are some of the most conservative numbers for what 2020 looked like. For so many people this disruption was pervasive, many of these roles will revive like hospitality, entertainment, and transportation.

But in other industries will build back digital. The jobs that exist in the years to follow COVID-19 are ripe to be dramatically different than what we see today. There are new roles for people to connect networks, become the manager of our AI teammates, move outside a major city, and bridge the gap between a physical and digital world.

Organisations need to prepare to upskill staff, support new types of workers, create different ways of connecting to work, and realise the power of AI to adapt to the Future of Work.

Check out across Australia and New Zealand to find out more about how 麻豆原创 is creating the Future of Work.

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