From virtual assistants like Siri and Alexa to self-driving cars and generative AI platforms like ChatGPT, artificial intelligence (AI) and its subset, machine learning (ML), are changing how we live, work, and play.

In the five years McKinsey has been tracking AI use worldwide, adoption has more than doubled, although its use in business organizations has held steady between 50-60% for the past few years. While the first-mover advantage has passed, there’s still plenty of opportunity to gain a competitive advantage by implementing AI to help your business be more agile, responsive, and innovative than others in your field.

If you’re still on the fence about adopting AI for your business or are searching for new ways various AI technologies could benefit your business, read on. In this post, you’ll find a comprehensive overview of what exactly AI is and why it matters, a timeline of AI milestones, the advantages and disadvantages of various AI technologies, and how it’s being used in different businesses today. 

What is Artificial Intelligence?

Artificial intelligence enables computers to simulate human thought processes and behavior, such as making decisions, solve problems, understanding language, recognizing images and faces, and more. Using constantly learning and adapting algorithms, AI systems can provide near-human accuracy and dramatically scale operations across many tasks and industries.

AI is one of our most significant technological advances, and its applications are becoming increasingly widespread. Businesses of all sizes are taking advantage of AI’s potential to improve customer service, increase efficiency and productivity, reduce costs, make better predictions about markets or customers, automate time-consuming and redundant tasks, analyze vast amounts of data, and develop new products and services faster than ever before. 

Recommended reading: AI’s Impact on Software Development: Where We Are & What Comes Next

In addition to being an effective tool for improving efficiency and productivity, intelligent systems can anticipate user needs and provide tailored solutions quickly and accurately by leveraging deep learning algorithms.

Additionally, AI can help organizations identify trends in data faster and more accurately. With access to large amounts of data from both inside and outside a company’s own network, AI can uncover insights that would otherwise remain undetected. This enables companies to make better decisions about allocating resources and gain a competitive edge in their industry. AI is fast becoming essential for any business looking to stay ahead of the competition.

A Brief History of AI Development

Artificial intelligence has come a long way since its inception in the 1950s. Some of the key dates in AI development include:

1956: The term “artificial intelligence” was coined by John McCarthy at the first AI conference at Dartmouth College.

1967: Frank Rosenblatt created the Mark 1 Perceptron, the first computer utilizing a neural network. It was able to quickly learn through continued experimentation. 

1980s: Symbolics Lisp machines are commercialized, and neutral networks using the backpropagation algorithm became common in AI applications.

1997: IBM’s Deep Blue defeated world chess champion Garry Kasparov.

2008: Google achieved significant advancements in speech recognition technology, which it incorporated into its iPhone application.

2011: Apple introduced Siri, a virtual assistant powered by artificial intelligence, to its iOS operating system.

2018: Google launched BERT, a natural language processing engine that made it easier for machine learning applications to translate and understand conversational queries.

2022: OpenAI released ChatGPT, a conversational AI that utilizes a large language model.

2023: Microsoft has recently released a new AI-powered version of its search engine Bing, which utilizes the same technology as ChatGPT. In response, Google has introduced its own conversational AI called Bard, creating competition in the market.

Thanks to advances in machine learning models such as deep neural networks and reinforcement learning algorithms, AI technology is constantly improving. These milestones in AI development demonstrate AI technology’s increasing sophistication and capabilities and its potential to revolutionize various industries.

Types of Artificial Intelligence

There are two main categories of artificial intelligence: narrow AI and strong AI. Narrow or weak AI focuses on specific tasks and can be used for language processing, facial recognition, and natural language understanding. On the other hand, strong AI or artificial general intelligence (AGI) has the potential to emulate human-level intelligence across a wide range of skills and tasks.

Weak AI (Narrow AI)

Weak AI, also known as narrow AI, is artificial intelligence that focuses on one specific set of tasks and is limited to the task for which it was designed. It cannot be applied to different problems. This makes it ideal for applications where speed and accuracy are essential, such as language processing, facial recognition, and natural language understanding.

One of the most significant advantages of weak AI is that it can quickly process large amounts of data while making fewer mistakes than humans. Businesses can use weak AI to automate mundane tasks or uncover insights from large datasets more accurately than manual labor. Additionally, weak AI can be trained rapidly due to its narrow scope.

Strong AI (Artificial General Intelligence)

Strong AI or Artificial General Intelligence is the next step in artificial intelligence. It refers to machines that can perform a specific task and possess a human-like level of understanding and reasoning. 

Unlike weak AI, strong AI has the potential to think for itself and solve complex problems without needing any kind of external programming or instruction. This means it can learn from its environment and even develop an understanding of its capabilities without human intervention.

Deep Learning vs. Machine Learning

Deep learning and machine learning have become increasingly popular in recent years as companies of all sizes seek to leverage the power of AI for their businesses. But what’s the difference between deep learning and machine learning? While both are branches of artificial intelligence that use algorithms to learn from data, there are essential differences between them.

Machine learning focuses on identifying patterns in data and using those patterns to make predictions or decisions. 

Deep learning takes this concept further by using layers of “neurons” to simulate how a human brain works and improve its ability to recognize patterns. This allows for much higher accuracy when making predictions or decisions based on data.

Deep learning is often used for tasks such as speech recognition and natural language processing, which require understanding complex relationships between words and concepts — something machine learning alone cannot do. 

Machine learning and deep learning each have unique advantages that make them useful for different applications. Companies should consider carefully which is best suited to their needs before investing in either technology. With the right guidance, companies can seamlessly integrate these AI capabilities.

Advantages of Using AI in Business

The advantages of using AI are numerous; here are some examples.

Personalization: AI can help businesses personalize customer interactions by analyzing customer data and tailoring marketing and sales efforts accordingly. This can lead to better customer experiences and increased customer loyalty.

Enhanced decision-making: AI can analyze vast amounts of data quickly and accurately, providing insights that can inform business decisions. This can lead to better decision-making and more informed strategies.

Cost savings: AI can help businesses save money by automating tasks and reducing the need for human intervention. For example, AI-powered chatbots can handle customer inquiries and support requests, reducing the need for human customer service representatives.

Improved efficiency: AI-powered systems can automate repetitive and time-consuming tasks, allowing employees to focus on higher-value tasks. This can lead to increased productivity and efficiency in the workplace.

Competitive advantage: Businesses that adopt AI early on can gain a competitive advantage over their peers by leveraging the technology to improve their operations, products, and services.

Predictive analytics: AI can be used to analyze historical data and identify patterns and trends. This can help businesses predict future outcomes and make more accurate forecasts.

Fraud detection: AI can detect fraudulent activities and transactions in real time. This can help businesses prevent financial losses and protect their reputation.

Improved customer service: AI-powered chatbots and virtual assistants can provide round-the-clock customer service, responding to inquiries and providing support at all hours.

Automation of complex tasks: AI can automate data analysis, financial modeling, and supply chain optimization tasks to save time and reduce errors.

Improved cybersecurity: AI can detect and respond to cyber threats in real time, helping businesses protect their data and infrastructure from cyber-attacks.

AI Disadvantages & Limitations

Despite the numerous benefits of artificial intelligence, there are also some potential drawbacks. One of the most prominent disadvantages is that AI systems require significant amounts of data to function correctly. This means that if a company does not have access to enough data, it may not reap AI’s full benefits.

AI-powered systems can sometimes make mistakes due to errors in programming or incorrect data input. This could lead to problems such as inaccurate customer service information or even security breaches if sensitive information is compromised due to an AI system’s mistake.

Overall, while AI offers numerous advantages for businesses, companies must consider the potential benefits and risks of using these systems before investing time and money into developing one. GlobalLogic can help you assess where to incorporate AI technology and help with the transition management.

How Businesses Use AI in Various Industries

Intelligent automations can augment and amplify the best of human performance, enabling a business to scale and grow at a rate that would otherwise be impossible. 

As Sanjeev Azad, Vice President of Technology here at GlobalLogic, shared with CXO Today recently, “Contact-center automation, customer segmentation & service analytics, business process automation and services optimization, predictive maintenance and remote assistance, risk modeling and analytics, and fraud detection and analytics are few businesses use cases where adoption of AI is playing a significant role.”

• GlobalLogic Intelli-Insights helps companies in all industries activate the power of their data by providing pre-defined standard AI apps and custom app-building capabilities inside our AI-powered data analysis platform. This digital accelerator enables companies to quickly transform data into actionable insight without having niche data science skills in-house. 

Here are several more examples of how companies use AI to their advantage in different industries.

Finance

In finance, AI is used for fraud detection, risk assessment, regulatory compliance, investment strategy, and more. Anywhere data can be analyzed and used to make predictions and decisions, AI can help. 

You can read about a specific application of AI in fintech here. In this example, a well-trained machine learning model constantly analyzed market data and made appropriate portfolio adjustments to continuously improve performance.

AI is being used to help insurers identify and mitigate risks by analyzing data from various sources, including social media, weather reports, and satellite imagery. Using AI to analyze customer data and predict future needs or behavior can help banks offer personalized services and products. It works to detect fraud and prevent financial crimes, saving banks money, and can automate repetitive tasks such as data entry for companies in insurance, investments, fintech, cybersecurity, and more.

Healthcare

One of the most impactful ways AI is used in healthcare is in diagnostic imaging. AI algorithms can analyze CT scans, MRIs, and X-rays to process results faster and detect anomalies that may not be visible to the human eye. AI can help doctors diagnose diseases earlier and more effectively manage patient care by analyzing patient data to predict disease progression and identify potential complications.

AI is used to develop personalized patient treatment plans based on their medical histories and genetic makeup. It’s also valuable for creating new drugs and treatments, and analyzing clinical trial data to help researchers identify new treatments and therapies. 

Check out other ways AI is used in healthcare here:

Click to read How Digitization Is Changing Medtech, Life Sciences, and Healthcare

Media

AI is used in the media industry in various ways, from content creation and audience targeting to creating personalized news feeds and analyzing social media data to determine what topics are trending.

AI can be used for transcription, translation, and image and video analysis tasks. Major media and entertainment brands have used AI for video encoding, augmented reality projects, and analyzing and predicting consumer content.

Recommended reading: AI is the Future of Media

Retail

AI is used in the retail industry in various ways, such as personalized customer experience, inventory management, and supply chain optimization. For example, retailers use AI to gather data about their customer’s preferences and behaviors and then use that data to offer personalized product recommendations and promotions. AI-powered chatbots also provide customer service and support.

Additionally, AI optimizes inventory management by predicting demand and ensuring that the right products are available at the right time. AI is also used in supply chain optimization to improve logistics, reduce costs, and increase efficiency. Here is a case study of how AI was used to create a next-gen retail product that blends online and in-store shopping.

Manufacturing

AI is used in the manufacturing industry in several ways. One of the most common applications of AI in manufacturing is predictive maintenance. By using sensors and data analysis, AI can predict when a machine is likely to fail and schedule maintenance before it does. This can save companies money in unplanned downtime and repairs.

AI can also optimize production processes by analyzing data on everything from raw materials to energy consumption to identify opportunities for improvement. Additionally, AI can improve quality control by analyzing data from sensors and cameras to identify product defects and anomalies as they are manufactured. 

Today’s business landscape is changing rapidly, and those that can take advantage of AI have the edge over their competitors. By leveraging AI’s power, businesses can better understand their customers and increase productivity while reducing costs and creating new efficiencies.

Final Thoughts 

Artificial intelligence is a potent tool for businesses of all sizes. AI can help streamline processes, improve efficiency, and save time and money. Additionally, AI can provide real-time insights into customer and user behavior to inform marketing campaigns or product development. 

Businesses need to take advantage of these benefits to remain profitable in the long run. While a wide variety of AI applications are available, it’s essential to thoroughly assess each before deciding which suits your company. Training employees on how to use these tools effectively to get the most out of them is also critical to the success of each AI implementation.

GlobalLogic developed our AI/ML Center of Excellence to help customers make informed decisions about and implement AI to increase business efficiency, continuity, and profitability. The best practices, tools, and proven processes available via our CoE are based on our extensive experience helping customers transform their businesses with AI-powered solutions and developing AI products.  

Get in touch today and see how we can put this experience and expertise to work for you.

A recurring challenge in software — especially for newly developed systems or major upgrades — is knowing when you’re ready to deploy or ship for the first time in production. When is “good enough” actually “good enough?”

In this article, we’ll explore this concept of good enough and the nuance around it. You’ll learn key differences between internal and external success, how ship decisions are made by various stakeholders, and where teams and individuals often disagree on precisely what makes a product or update “good enough” and ready to go. Finally, we’ll work through different approaches using real-world use cases and see what lessons can be learned from those.

Part 1: Internal vs. External success

Perceptions of risk depend heavily on company culture, with fear of failure (individually or corporately) being a major driving factor. Most companies agree that a reputation for secure, high-quality products is important to achieve market and business success. 

What differs is how success is measured: internally or externally. 

Companies with an internal focus generally use key performance indicators (KPIs) and other internally-defined metrics to provide incentives or disincentives to drive desired employee and departmental behaviors. These metrics are proxies for desired outcomes; for example, “minimize the number of security penetration vulnerabilities detected post-ship.” 

Causes and risks of an internal focus

Many large companies, in particular, see proxy metrics as the only way to effectively communicate goals throughout a complex organization, to achieve alignment, and to measure success business function-by-function or department-by-department.

Unfortunately, this focus on modeling desired outcomes using proxy metrics often results in the individuals within the company seeking to maximize (or minimize) the metrics that apply to them, rather than focusing on the overall success of a given product in the marketplace. 

No one in this system has the wrong intent. Who could argue that it’s good to ship with more security vulnerabilities? By seeking to eliminate security vulnerabilities, the security group (for example) is merely seeking to do its job. With an internal focus, they will see their job as ensuring compliance to the policies the team has established to achieve the goals set by upper management. 

However, a department-by-department optimization approach can lead to a bad outcome for the company. It is impossible — or at least economically infeasible in a finite time — to create a product of any kind, software or hardware, that is literally perfect in every dimension. And even in those areas where the product is “perfect,” time:

• exposes new patterns of usage
• introduces new vulnerabilities in underlying infrastructure or integrated systems
• results in changes in the operating environment
• spreads new usability paradigms
• and introduces other factors that will make even an initially ‘perfect’ product imperfect. 

Successful products are a balance of features, time to market, and non-functional requirements like usability, security, quality, and many others. Which aspect of a product should be emphasized at a given time to achieve a given business outcome requires balance across multiple factors that are sometimes — in fact, often — in conflict. 

In an internally-focused company, ship decisions are often made by executives based on the input from the various business functions and departments. Each of those stakeholders is trying to follow corporate direction by maximizing (or minimizing) the appropriate metrics. 

This can result in some groups having an incentive to avoid shipping a product at all, in order to minimize their department’s or their own exposure to a potentially negative impact on their group’s KPIs. And sometimes, not shipping is the right thing to do. 

On the other hand, in an internally-driven culture, a product that might otherwise be highly successful in the market might never be seen as “good enough” by all its internal stakeholders because shipping it poses a risk to a particular group’s KPIs or other departmental incentives. No one is doing anything ‘wrong’ or ‘dishonest’ in this scenario;in fact, the groups are responding to the direction they have been given by upper management in the form of these metrics. 

However, the net effect is that such companies tend to be highly conservative. They tend not to ship innovative products because they pose a risk of failure to meet internal metrics for one group or another.

How externally-focused organizations differ

Externally-focused companies, on the other hand, are market-focused and look for their signs of success or failure from their buyers, customers and end users. They, too, are sensitive to the risk of failure, but it’s commercial failure in the marketplace that keeps them up at night. 

No single group can afford to place its individual departmental success or mission above the goal of shipping a successful product. This is not out of altruism, or because the individuals involved have different skill sets, quality standards or personalities than their counterparts in an internally-focused company. Rather, it’s because of the way success is measured. 

Recommended reading: Culture at the Core: A Playbook for Digital Transformation

In an externally-focused company, the whole team — all departments — succeed or fail based solely on the success of the product in the market. No department is viewed as successful, even if it hits its individual metrics, if the company or the product fails. Market success is a shared goal, and all teams in an externally-focused company have no choice but to work together to achieve it.

That doesn’t mean that teams within a market-focused company always agree with each other. Far from it. 

Product Management and Engineering may disagree on dates and feature sets; the quality and security groups may raise red flags that are heeded or ignored; DevOps and Ops may fight with the business over tool choices and FinOps. What is different in an externally-focused company, though, is that all of these disagreements are in pursuit of a common goal rather than a departmental goal. 

That shared goal is to quickly ship a featureful-enough, secure-enough, sufficiently high-enough-quality product that succeeds in the marketplace. In this context, maximizing the metrics of an individual group is irrelevant if the product fails, because all lose. 

When all stakeholders are focused on a common goal, disagreements tend to be healthy and rapidly resolved because all are in pursuit of a common goal: product success.

Small companies, and startups in particular, tend to be externally, market-focused. The major incentive and KPI at a startup is stock options, and these only become valuable if the product and company succeeds in the marketplace.

Differences in focus between small and large companies 

Small companies tend to have only a small number of products — sometimes just one. If the product and company do NOT succeed in the marketplace, then a small company will likely close down, making the stock options worthless. Despite the market risk, these factors and others align to make startups externally focused (which is part of their attraction to many good engineers and investors).

Large companies may be or become externally focused, as well. Apple is a very good example. Before Steve Jobs rejoined as interim CEO in the late 1990s, Apple had become very much an internally-focused company. Jobs’ and Apple’s tremendous achievement was due in large part to Steve’s success at flipping Apple’s focus from internal to external. 

He did that by getting a critical mass of people focused on their product’s success with consumers, and in the marketplace. Jobs did this while maintaining what most of us would agree were high quality and security standards. “Externally focused” does not mean “sloppy.” 

Externally focused does mean “risk-based,” though. As a market-focused company, it does not make sense to put effort into areas that fail to generate business value. 

This means that instead of absolute, unalterable, KPI- or departmentally-driven standards for each individual aspect of a product, an externally-focused company instead looks at how best to maximize the business value of the entire product for each release. That is the essence of creating a product that is “good-enough,” “secure-enough,” “featureful-enough,” and so on. This does not mean ‘sloppy,’ but it does mean a ruthless focus on creating release-by-release business value.

Now, how do you quantify “good enough” when making risk-based ship decisions?

Part 2: Approaches to “Good Enough”

With this understanding of how market-focused companies make risk-based ship decisions based on maximizing the business value of each release, we can begin to explore different ways to quantify “how good is good enough.”

Achieving external market-based success often requires a balancing act. In general, you want to get a new product in front of real, live customers as soon as possible. This not only delivers value to your users, but also allows you to gather their feedback so you can improve the product (“pivot or persevere” in Lean/Agile terminology). Success here also lets you unlock potential revenue streams from investors, internally from your own company, and from customers themselves. 

On the other hand, if your product is not “good enough” when customers first see it, you may lose them irreparably and fail in a different way to unlock your revenue stream. In other words, there are risks in shipping, and also risks in not shipping. 

How do externally-focused companies make the tradeoff?

I started studying this issue back in the 1990s. At that time, I read a book that described Hewlett Packard’s quality criteria for at least some of their products at the time (this is pre-split, before HP became “HP” and “HPE”).  

The book said that HP’s quality criteria was met for a given product when it became more expensive to find the next critical bug internally through testing than the cost of letting the customers find that critical bug post-ship. In other words, they would ship when it was more costly to find the bug through testing than to let the customers find it, reputation and remediation costs included.

This was a very high bar in the ‘90s. In those pre-cloud days, systems were deployed on premises, on customer equipment. For at least some systems, replacing them in the field required flying technicians to multiple points of the globe to physically install from media, apply configuration options, transition data, and perform other operations. Replacing a critical system in the field was expensive, so this quality bar was very high. Today, of course, we can deploy in production automatically multiple times a day, so it might not seem like a big deal. But it was then.

Future events are always probabilistic; we don’t know for sure how much testing will be required to find the next critical bug, or how much customer use will uncover it. We can use tools like the defect discovery rate and projections based on past releases to put numbers on these events. But in the end, we are balancing one uncertainty or “risk” against another. This 1990s era HP criteria was therefore “risk based;” that is, they shipped when the probabilities dictated that it was likely that it had become more expensive to continue testing, than the likely cost of finding and remediating a serious quality issue found after shipping. 

I followed HP’s example with a number of products in that timeframe, and for many of them I was accused of being too conservative as the customer never did find that next critical bug. Still, we were playing the odds and very conscious of it.

HP’s approach appealed to me because it made sense, business-wise and quality-wise. You ship when it’s more costly not to ship. 

The potential cost of shipping could be defined so as to include all the factors that keep people up at night: security incidents, production-down incidents, reputational damage — even loss of life. These would be weighted by their probability of occurrence. The cost of not shipping, on the other hand, would include:

• loss of revenue, 
• loss of opportunity and competitive advantage, 
• the cost of continued development and testing, 
• development infrastructure costs, 
• and everything else that goes into keeping a product under on-going development without a supporting revenue stream.

Steve Jobs is quoted as saying to the original Apple MacIntosh team, “Real artists ship.” What he meant by this, I believe, is that software systems (or anything else) must be put in the hands of end users to be valuable to anyone. A system delivers no business value while it is under development. 

A system that never ships, because its developers or other stakeholders believe it’s imperfect, delivers no value at all. And, of course, no system is ever perfect. Even if it did start out being perfect, a real-life system is unlikely to remain perfect given emerging security threats, changes to the underlying software components a given system depends on, and many other factors outside the boundaries of the system itself.

Recommended reading: If You Build Products, You Should Be Using Digital Twins

Years ago, I was lucky enough to work with some PhD-level experts on economically-based business decision making. One then-widely accepted approach to such decision making was probabilistic and risk-based. I was surprised and a little shocked to learn that this often included putting a dollar value on lives lost, in order to calculate potential risk. 

For example, if an airline company wanted to determine whether a potentially life-saving improvement to one of their jets was worth the investment, they would go through a thought process something like this simplified illustrative example using fictitious numbers:

• Replacement cost of the plane is $100M.
• The plane carries 200 passengers.
• Our expected liability for each passenger death is $10M USD (this was in the early 2000s, so today this figure would be closer to $20M USD per passenger).
• The probability of a fatal crash over the 150,000 flight-hours expected service life of the airplane is 1.78%.

Using these made-up figures, we see that the “expected loss” (probability of loss times the $2.1B potential amount of loss) is $37M USD over the life of the aircraft. 

In other words, if you were going to set aside a reserve to cover future losses from the crash liability for “N” number of airplanes of the same type, you should set aside “N x $37M USD.” If the lifetime probability of loss could be reduced to zero through an investment of $37M per plane or less, then it would be economically justified to spend it on improving the safety of the aircraft. 

If it costs more to reduce the risk to zero, or in general if the amount at risk would be reduced by less than the amount invested, the additional investment to improve safety would not be justified in purely economic terms.

This is a very simplified analysis and perhaps morally repugnant because human life is involved. How can one put a financial value — even double the original value, or $20M in today’s dollars — on an individual human life? 

Yet the alternative is to never ship. If we had to reduce the risk of a fatal airplane crash to zero, no one would ever be able to fly. 

The cost of creating such a plane would so far exceed the potential losses from the current generation of planes that no company (or group of companies, or (probably) nations) would ever be able to make the investment. And even if it were possible to build a perfect plane, external factors like deliberate malice, meteor strikes or other non-engineering factors would mean that some people will still die. The cost of the added investment in safety would also have to be passed along to the consumer, raising the price of travel for the end user — perhaps raising it more than the decreased risk would be worth even to the traveler themselves. 

It’s heartless, but if one wants the benefit, one needs to take the risk. The art is making the risk of loss smaller than the expected reward, to the company and to the customers, who follow their own risk/reward calculus.

We each make risk-based decisions every day, yet we hardly think about them. We risk our lives, to a greater or lesser extent, every time we commute to work. For those who drive in the U.S., the cumulative lifetime risk of dying in a car accident is about 1 in 93. Yet we continue to drive and take other risks, because we believe the benefit outweighs the risk. We also put the lives of our loved ones at risk each time they accompany us in a car, ride a bike, or even go for a walk. We rarely worry about it or think about it, but at some level our brains do the math and decide to take a risk to get a reward. 

Even following a risk/reward model, we can still set the quality bar as high as we like by making the expected cost parameters as high as we choose. Instead of $10M liability per life, we can increase it to $100M or $1B per life, for example. We can continue to invest until an arbitrarily high degree of perfection is achieved — or until we run out of money. However, in the meantime, both the customer and the company are denied the benefits they would get through real-life use of the product. This is also a very real cost. 

We can safely assume you believe your product will deliver value to its users: If you didn’t think so, why would you build it in the first place? If that is your belief, then withholding your product from the market hurts its potential users by denying them the benefits that using your product would bring. While you obviously don’t want to ship them something bad or that doesn’t work, bringing something to market that delivers positive benefits to its users is valuable to them, and therefore worthwhile. 

Considering only the potential risks associated with shipping a product, without weighing the potential benefits internally and externally, is not a sound or a balanced ‘engineering’ approach to ship decisions. Weighing both costs and benefits when making a risk-based ship decision is not a compromise. It’s the essence of a value-based business and engineering approach.

Learn more:

• Software Engineering in the Age of Creative AIs
• AI’s Impact on Software Development: Where We Are & What Comes Next
• Software, the Last Handmade Thing