The world of big data, machine learning (ML) and AI have developed rapidly over the last 5 years with new technologies, processes and applications changing the way organisations are managing their data. There is a good barometer of what the state-of-the-art is in big data manipulation as well as the concerns of developers and users. AI and machine learning combined with ever-increasing amounts of data are changing our commercial and social landscapes. A number of themes and issues are emerging within these sectors that CIOs need to be aware of.
Communicating with your Board of Directors about AI/ML is different from conversations with top operating executive. It’s increasingly likely your Board will want to know more and planning that communication in advance will make your presentation more successful. It’s important to start with understanding what your Board of Directors does and doesn’t do. While Board members may be very senior in their experience of your and other industries, they may not be as well informed as you and senior management on new technologies and how to exploit them.
Data-driven marketing has been an effective marketing approach for several businesses. Organizations need to address customer concerns and generate a trustworthy and transparent approach towards data collection. Data has become so essential for businesses that startups are generating synthetic data to eliminate the cold start problem. Hence, numerous organizations in every industry sector are adopting a data-driven marketing approach to offer better products and services and ensure customer satisfaction. But, are there any negative implications of implementing data-driven marketing?
Leaders who figure out how to leverage increasing data trove to improve their decisions and outcomes will produce superior returns, just like the best investors do that have long relied on machines and “quants.” Failing to make use of the growing surge of data will mean a significant handicap for any leader and their team just like it does in the financial markets. The answer is for corporate leaders to use artificial intelligence to facilitate and speed up the steps above and in the process, make faster, better decisions.
When AI infiltrates our corporate and government networks, IT Service Management (ITSM) organizations will be responsible for keeping these systems up and running. With enormous workloads, limited budgets, and an influx of IoT, ITSM departments are struggling to keep up with demands. To help service management organizations keep up, we will see ITSM solution providers incorporate AI into their solutions as a way to improve efficiency while reducing costs. We should expect AI technology to disrupt current ITSM technology in three key areas.
The proper use of data can make you and your organization very successful. Being aware of the areas that need to be improved and the areas that your customers love is a good thing. If you ignore the signal in your data, you risk seeing your operations and your products wither away before your eyes. Data can be your ally and it is now widely recognized as the most important asset that any organization, public or private, possesses. However, we need more leaders with the ability to shepherd the good and virtuous process of executing on a data mission.
Tax fraud is already prevalent, and fraudsters are more sophisticated and automated than ever. To get ahead of the game in detecting fraud and protecting revenue, tax agencies need to leverage more advanced and predictive analytics. Legacy processes, systems, and attitudes need not stand in the way. What’s new in fraud prevention and what does a complete capability look like? What can Tax agencies do differently and better today than they could a few years ago? This blog explores the challenges, opportunities, and value of tax fraud analytics
When talking with CIOs and other senior executives, cloud computing is often cited as the foundation of a given digitization strategy. Cloud computing and the costs don’t have to remain a love-hate relationship. When planned well beforehand and deployed in a smart fashion, the cloud will make perfect economic sense. This doesn’t just apply for possible cost savings but also — and perhaps even more importantly — for enabling top-line growth. Thinking along the categories people, processes, and tools, will enable companies to come up with a comprehensive game plan that helps overcome the challenges.
Are you interested in Machine Learning? Are you asking yourself which are the key skills within this profession? If you are interested in machine learning, you are not alone. In fact, machine learning is one of the hottest fields right now and more and more people get interested in it everyday. This blogpost will tell you about the most valuable skills within the field and what you really need to have in your arsenal to call yourself a machine learning engineer.
If bots could learn, they would require less up-front effort in RPA deployment. Thanks to advances in applied artificial intelligence (AI) and machine learning algorithms that have the ability to detect patterns and make predictions and recommendations, bots do not have to receive precise programming instructions to adapt to changes in business processes. Bots will be able to be used to automate a far wider range of business processes than is currently possible, which could drive demand for the technology.
Cloud supports rapid experimentation and innovation by allowing companies to quickly try and even adopt new solutions without significant up-front costs. The #Cloud can be a highly agile wrapper around different systems, different behavior and bringing it all together in an engagement cycle. By changing the way people interact with technology, cloud enables new forms of consumer engagement, expand collaboration across the value chain and bring innovation to companies’ core business models. However, there are myths surrounding cloud computing and clouding the reality of the cloud.
As teams strive to mature Agile and DevOps processes, there is a need to change culture, processes and technologies. From a process perspective, understanding the route to introducing automation, and how to revise processes across an entire software development life cycle (SDLC), is vital. The final hurdle is in selecting the right technology to facilitate a move to DevOps – such as configuration management tools, continuous delivery platforms and automated testing itself. To help streamline the process and implement change, let’s explore six different steps organizations should take to achieve success.
In a data-driven business world it’s clear that machines are beginning to play, and will play, an ever-larger role in C-suite decision making. The best leaders of today and tomorrow will no longer rely on the instincts of a few decision-makers and will instead use insight driven by machine and deep learning solutions. With new competitors changing the market at rapid pace, companies seeking to achieve ‘superstar’ status and dominate the top of the profit and value ladder will need AI to guide the way forward.
Machine learning not only saves time on building a fraud detection routine but also can remove bias against certain taxpayers if done properly. If supervised learning is fishing where people have fished before, then another type of machine learning—unsupervised learning—is fishing where no one has fished before. Both supervised and unsupervised approaches provide tremendous value for government tax authorities, especially when used upon complex data sets like tax returns, financial transactions, taxpayer contacts, accounts receivables, network traffic, and even employee activities.
Privacy and security considerations are the key ingredients of digital trust and must be at the heart of any industry’s digital transformation. The necessarily transversal nature of security and privacy matters needs to be woven into the fabric of an organisation for the digital transformation to succeed over the long-term. At this junction, the traditional role of the CISO – heavily influenced by a technical bias, tactically-oriented and project-driven in many firms – could become exposed.
Blockchain helps insurance sector in providing privacy, borderless reach to make smarter decisions and also deliver insurance with quality. This is due to the features of blockchain that make it possible with the decentralized network. Though blockchain in insurance sector helps the industry to provide a much better service, there are many challenges faced while adopting to blockchain. The question is how far is your insurance company would go to break down these challenges and survive in the market and how well do use these challenges to your advantage.
The Internet of Things (IoT) is increasingly part of our everyday lives, with so-called “smart” devices. But for all their undoubted technical merits, they also represent a growing threat to privacy. There are several aspects to the problem. One is that devices may be monitoring what people say and do directly. Another is the leakage of sensitive information from the data streams of IoT devices. Finally, there is the problem summed up by what is called by some “Hyppönen’s law“: “Whenever an appliance is described as being ‘smart’, it’s vulnerable”.
Fintech firms are increasingly escalating the pace of revolution with the help of cutting-edge technologies. They are now looking to combine two incredible technologies to become a differentiator in the competitive market. Robotic process automation (RPA) and AI have become a disruptive force in the fintech sector. Augmenting AI to a rule-based robotic process automation system gives rise to another tool that not only automates tasks but also possesses decision-making capabilities. All of this will in turn increase accuracy, boost productivity, and increase a company’s bottom line.
Efforts are underway among the ERP vendors to consider more innovative ways to handle the massive data demands of ERP and in fact surface more obvious value from ERP through machine learning and ERP algorithms. AI-enabled ERP can ultimately mean more intuitively surfacing access to all ERP data services through the methods that users would feel most comfortable using. Moving ERP systems from reporting on historical activity and getting them to be helpful with predicting and forecasting behaviour and outcomes become much more of a likelihood when AI and machine learning is applied to ERP systems.
There is great potential in leveraging AI and deep learning to help with the tax process. Tax fraud has existed in many forms for year, but it has become particularly widespread with the huge increase in identity theft. Taxpayers and tax agencies want transparency. Compared to other types of data-driven analysis, the amount and quality of the data is more important with deep learning models. While the picture of deep learning for tax preparation sounds bleak and ominous, there are places in tax administration where deep learning is appropriate and beneficial.
It seems strange then that the United Nations has coined digitization as an instrumental component for achieving sustainable development goals. After all, the concept does have some requirements for moving away from digital mediums as a means to lower dependency. The reality is, many digital tools exist for improving sustainability levels, particularly when it comes to tracking resource consumption. Certain IoT devices, for example, can monitor and report water and energy usage. The incoming information can then improve processes or operations, cutting down on total consumption.
To meet growing service and asset requests, agencies are looking to IT asset management solutions that incorporate robotic process automation and artificial intelligence. In recent years, IT asset management has become an important part of an overall security strategy for many agencies after several highly publicized security breaches. Incorporating RPA with AI into next-generation IT asset management solutions will also help agencies that are struggling to meet IT asset management objectives due to limited resources. Several key areas will see changes as a result of incorporating RPA and AI into IT asset management.
Automation, from robotic process automation to artificial intelligence, is transforming every function of every business in every industry. Despite the many indicators of a transforming marketplace, almost all legacy leaders and board members still hesitate to apply artificial intelligence to corporate strategy. Leaders of businesses that don’t move quickly to capitalize on the power of AI will be left behind. Adopting an AI powered strategy is the natural next step. No matter the application, the process is similar. Here are the four steps of AI powered strategy.
Going into business for yourself by forming a corporation or sole proprietorship is an exciting endeavor. Of course, from a taxation standpoint, it can also be a little overwhelming. These seven tips will help you navigate your tax requirements. You can begin to implement some of these tax tips for businesses practically overnight. Others are slightly more involved and might require input from your lawyer or accountant. All of them, however, have the potential to help your business.