13 Oct 2021
Artificial Intelligence, Business, Startup Permalink

AI and Personality

October 13, 2021
Artificial Intelligence has supercharged our ability to analyze the world around us. Much like the human brain, artificial intelligence can analyze data given to make predictions or decisions. However, unlike the human brain, AI can do this at much faster and more accurate rates, making it a valuable tool for businesses hoping to use data analysis to make smarter decisions. In recent years, researchers have managed to create a form of AI that can make accurate predictions about the personalities of individuals. The machine can do so by processing data from social media posts and personal images to create a personalized profile of an individual.

Benefits of Personality AI

If you are a business that wants to be successful, you are always looking for innovative ways to engage audiences that result in converting them into customers. Traditionally, marketers achieved this by creating marketing strategies based on information they could gather on different demographics. For example, they might market differently if they wanted to appeal to a male audience versus a female audience. But in recent years, these tactics are no longer as valuable, as individuals are tired of companies generalizing them based on their demographic makeup.

Everyone is different. Even if we share some similarities and experiences with others based on certain demographics, businesses must recognize that everyone is unique. Personality AI can help marketers reach more potential customers by individualizing their market strategy based on someone’s online personality. Our social media habits are often indicative of what we are interested in viewing and sharing with the people around us, and personalized AI can leverage this information to generate a more accurate profile on who we are as individuals, making it easier for marketers to develop campaigns that potential customers will be interested in engaging. People are much more responsive if their voices are being listened to and their interests are considered.

The Drawbacks

AI has allowed us to process more about the world and each other than previously thought possible. This information can be used to innovate how we operate businesses, governments, healthcare, and more. However, Artificial Intelligence can have its downside, particularly when it comes to personal privacy. There is not much regulation on how private companies can utilize the data that they extract from us using this technology. With any new form of technology, we must consider the ethical dilemmas that it may cause. The information that AI can collect on us and how that information is used can be just as harmful as helpful. In a world where misinformation and conspiracy theories are widespread, new technology can be used to foment distrust in people and institutions by exploiting individuals based on their interests and social media habits. With any new technology we create, we must also create ways to protect ourselves and each other from the negative ramifications of the misuse of that technology.

To survive, marketers must keep up with modern technology to keep in touch with their audience. AI can help them stay ahead of the curve by allowing them to cater their content to specific individuals rather than whole groups. With AI, marketers can make more informed decisions about who they market to and how they market to them. This means that the advertisement we see will be much more relevant to our lives for potential customers. As long as the technology is used ethically, AI can continue to improve our lives and decision-making, allowing us to be more innovative and creative in the process.
Maria is a writer at Enki Tech, a Downtown Santa Monica technology company that specializes in the development of high-quality, user friendly software, web platforms and mobile apps.
12 Oct 2021
Business, Startup, Strategy, Technology Permalink

Software: Should You Make your Own or Buy It?

October 12, 2021
Every company must make internal decisions about whether they can create the tools they need in-house or if they need to purchase the tools from an outside company. This is called a make-or-buy decision. When making a make-or-buy decision, companies must weigh the cost of creating the tools they require in-house against outsourcing. Whether it is building a car or software, the cost-benefit analysis companies m¬¬ust do to make their decisions is the same.

Making Software In-House

One of the benefits of building software internally is the ability to customize said software to meet any challenges. Many SaaS (Software as a Service) providers often purchase new software every few years or when the software available no longer suits their needs. Frequently updating and installing new software from an external provider is expensive. Building software in-house means that a company can customize the software to fit their requirements and update it whenever they please. They can also build software that is not yet available on the market that can be adjusted to meet specific demands for the company.

However, building and maintaining one’s software can be costly and time-consuming. It may take a company upwards of a year to complete the software, and funds must be dedicated towards maintenance costs and supporting an internal software team. There is also the possibility that the software that a company needs already exists, eliminating the need to create their own software.

Outsourcing Software

Many software developers create SaaS for organizations looking to outsource the software building process. SaaS provides cheaper upfront costs to the user as well as shorter implementation times for software. In addition, many SaaS providers offer subscription plans monthly or yearly, including both software implementation and subsequent maintenance services. Buying a subscription is far cheaper than building one’s software, as one does not have to hire an internal software team to develop and maintain the software.

However, buying from a SaaS provider also has its drawbacks. Outsourcing requires a company to become entirely dependent on external software developers to create and maintain the software. If the software developer cannot meet a company’s requirements, it can cause delays in implementation and create excess work for the company to remedy the issue. In addition, a SaaS provider might not develop software that is adaptable enough to fit the company’s changing needs. Subscription fees can also add up over time and can even cost companies more on the backend than just developing their software. Buying from a SaaS may also reduce a company’s competitive advantage, as they would be utilizing software that many other companies are already using.

There are advantages and disadvantages to building software in-house versus outsourcing software development. Every company’s requirements for the software they utilize are different depending on what those. Some may find that building software internally might be the best option, while others choose to outsource. It is essential that when planning one’s software implementation strategy, a company is clear on its goals technologically and financially.
Maria is a writer at Enki Tech, a Downtown Santa Monica technology company that specializes in the development of high-quality, user friendly software, web platforms and mobile apps.
08 Sep 2021
Business, Startup, Strategy, Technology Permalink

Customer Development Process in Software

September 8, 2021
The customer development process is essential for any company hoping to create a viable product that the public needs and wants. In software, developers must take steps to identify the needs of the public, create a product to satisfy those needs, develop a strategy to attract customers, and ensure that their organization can support customer needs. According to Steve Blank of Agile Alliance, a global non-profit dedicated to helping organizations apply the Agile manifesto principles to their organizations, the four-pronged process can be broken down into four categories: Customer Discovery, Customer Validation, Company Creation, and Company Building. These categories are part of what is called the “Lean Start-up” approach. Through this approach, startups can learn through feedback which solutions will best help build and maintain their customer base and how best to support them.

Customer Discovery

Customer development starts with customer discovery, which begins with identifying the initial need that can be solved. Market forces are dictated by what the public does and doesn’t demand, so any startup hoping to succeed must address an unaddressed public need or create an alternative solution that improves upon an existing one. Some startups already have a product idea in mind, and in this case, they must work backward to discover what needs their current software can fulfill. After that, startups must make assumptions about how their product may fare in the business environment, what the product dependencies are, what minimum requirements the product must meet, and what management changes are required.

Customer Validation

Customer validation involves making assumptions about what a potential customer may need from a solution and building a customer development strategy around that. Doing a market assessment is a great way to get an idea of what the public is most concerned about and how best to fulfill their current needs. This step will further help startups understand the unmet needs in the market or the needs that can be better served given existing solutions.

Company Creation

In this step, companies can begin developing their solutions and ensure that they meet customers’ needs. Their job is to create a solution that will best fit the customers’ desires. Developers must question which markets are most apparent and then address them all with their answers. Sometimes the need that needs to be addressed is entirely different from the initial assumptions about it. Once the solution is complete, it can be delivered to the public.

Company Building

The relationship between customers and startups doesn’t stop with releasing their solution to the public—it should be an ongoing relationship. To support and satisfy future needs, startups must build an organization with the ability to address these needs. Customer feedback is essential, and solutions like surveys can help give startups an idea about how the public feels about their solution. They should have the ability to update software that no longer satisfies the customer’s intended needs. In turn, they should continually be reevaluating if their software is genuinely measuring up to expectations.

Maria is a writer at Enki Tech, a Downtown Santa Monica technology company that specializes in the development of high-quality, user friendly software, web platforms and mobile apps.
18 Aug 2021
Business, Startup, Technology Permalink

Software: Alpha vs. Beta vs. MVP

August 18, 2021
When a new piece of software is being developed, it goes through the software development life cycle. Before a piece of software is fully developed, it often goes through several phases where the software is improved, and bugs are fixed. The main phases for this life cycle include the alpha phase, the beta phase, and the MVP. But what do these life cycle phases mean?

Alpha Phase

One of the initial phases of the software development cycle, the alpha phase, is the first phase where software is tested. In this phase, software developers test the internal structure, known as white-box testing. Developers take an internal approach to the software and utilize their programming skills to design test cases that test the software’s code. Software is akin to a system of interconnected networks that will create a specific outcome when programmed and connected correctly. Developers will input certain information into a software’s code. If the software is designed well, the code will put out a specific output. With white-box testing, errors in the code can be found and fixed. White-box testing occurs in three levels: unit testing, integration testing, and regression testing. In unit testing, a code is tested for defaults before it is integrated with other previously tested code. In integration testing, the interaction between integrated code is tested. Finally, in regression testing, previously tested software is tested again to ensure proper functionality.

Beta Phase

In the beta phase, the software is known as betaware. For betaware, the software component is technically completed software, but betaware often contains many unknown bugs that must be accounted for to avoid software crashing and data loss. Beta testing is used to help track down these bugs, and usability testing is utilized to help mitigate user risk when operating the software. In addition, software developers will have a limited release of the beta version of the software either to the public or a private select group, giving the public a snapshot of the new software. Beta software can also be called a prototype. Its limited release to the public is vital to the beta testing phase to work out any usability error in finished software.

MVP Early-Stage Development and the Final Software

MVP stands for “Minimum Viable Product.” In the MVP stage of the software development life cycle, software developers successfully developed and beta tested the software. At this stage, the software is ready for its initial release to the public. It has been checked for the bugs that would otherwise cause usability problems for the general public and is prepared to be tested for feedback on the initial software. User feedback is crucial to the development cycle, as it gives developers an idea about how the public will feel about the initial products and what changes to the product may need to occur going forward., The MVP is a basic version of the product. It will be updated according to the feedback of public users deliver.

Maria is a writer at Enki Tech, a Downtown Santa Monica technology company that specializes in the development of high-quality, user friendly software, web platforms and mobile apps.
04 Aug 2021
Artificial Intelligence, Business, Finance, Technology Permalink

AI in FinTech

August 4, 2021
There is no doubt that AI has become quite a popular tool for businesses to wield. Over the past twenty years, many industries have begun integrating AI and machine learning technology into their businesses, utilizing the capabilities of this smart technology to better their businesses. The financial sector is no exception. Many financial institutions and FinTech companies were early to adopt AI. They recognized the potential in the technology to save their businesses money and allow them to operate more efficiently.
In 2019, the global FinTech market was valued at over $5.5 trillion. Over the next five years, its CAGR is expected to increase by 23.58%. This incredible industry growth is mainly due to the integration of AI, machine learning, predictive analytics, and other innovative technology to solve major industry problems.

Improve Customer Service

Before adopting AI on a large scale, many financial institutions like banks dealt with expensive operating costs and customer dissatisfaction with their services. AI has aided FinTech in solving complex problems by allowing companies to process more data and tailor their strategies to optimize consumer needs. It also enables FinTech to lower overall operating costs as well. By integrating more data into their practices, FinTech can reduce costs for customers, offering them precisely what they need with less hassle. In addition, AI’s ability to process data quickly gives companies much more insight into potential customer behavior and needs, allowing them to make better decisions earlier and faster and simplifying the process for customers.

AI-powered chatbots on websites and apps allow more customers than ever to access the resources they need to resolve their issues and get results in a timelier fashion. FinTech companies can learn from customer interaction with smart technology, which allows them to better market and deliver results to customers.

AI can even be programmed to help customers make smarter financial decisions. For example, digital assistants that specialize in finance can help track spending and saving. Then, utilizing the information they are given, they can create a financial plan for the customer, showing them exactly how much they should spend and save to make the most of their finances.

For many years banking customers were unhappy with the way banks did business. The process of banking could often be long and arduous, leaving customers confused. Before integrating AI into banking, banks would offer services that customers didn’t need. Customers would even end up paying for expensive services they didn’t want because the banks lacked insight into the unique needs of individual customers. With AI, banking for the average customer is a much easier and more accessible process. For example, customers can conduct their business almost entirely online. In addition, banks utilizing the power of predictive analytics can target customers more directly and provide them with the necessary services they need, avoiding the hassle of a complicated process and reducing the overall cost to the customer. AI has also allowed banks to expand their global reach, as the internet enables them to conduct business efficiently and effectively regardless of time zones. FinTech powered by AI also reduces the need for lengthy paperwork as more business is conducted digitally.
Maria is a writer at Enki Tech, a Downtown Santa Monica technology company that specializes in the development of high-quality, user friendly software, web platforms and mobile apps.
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