Posted on January 23, 2025 by Hieu (Sam) To, Manager, UX/UI Design
Design thinking is about empathy—putting yourself in another person’s shoes to solve a problem they are facing. User-centered design is about, well, useability. It narrows in on the part of that solution that is tied to a digital or online experience, whether that’s a product, an application, or a website. The two concepts go hand-in-hand, and as Kopius went about building Tucson Medical Center’s (TMC Health) digital front door, both were at the forefront of our thinking.
The reason? Trust.
It’s important for people to be able to trust their health care system and its providers. And we wanted to send that message loud and clear in every digital interaction. Empathy and useability were the keys.
Key Strategies for Overcoming Complexity
Healthcare can be complicated, overwhelming—even scary. A digital front door, which is an online portal or platform where a health care system, its staff, patients, and even their families or other caregivers, can easily interact and access the information, should be designed to make it less so. Design thinking and user-centered design drove every aspect of our approach to building the new site, which we did using the Payload content management system.
Among the many strategies we used, three stand out. First, every decision we made was centered on the user journey, which was a bit tricky, since there was more than one user. Second, we made access to critical information as straightforward as possible. And third, we used visual branding to simplify and guide users.
While these are particularly critical in a health care setting, they are truly universal and applicable when developing any digital product or solution.
1. Prioritize the User Journey—Even on the Backend
When developing any digital product, the user journey should always be your top priority. But in TMC Health’s case, they needed to welcome both new and established patients, and their journeys are very different. For example, new patients are often looking for educational and marketing materials about what the health system offers while returning patients need to quickly find specific services and providers, schedule appointments, etc. We had to create pathways for both.
Digging deeper, we realized those aren’t the only two user personas that matter. TMC Health’s team uses the site to upload and manage content. They had their own user journey that had to be addressed. Not only did we need to structure the backend so they could work efficiently, but we also needed to build guardrails so that they uploaded new content, they didn’t make changes that would impact the user experience.
Websites often must address the needs of more than one user persona, both on the front end and the back. You may not be able to tackle everything at once. That was the case with the TMC Health project, so we took a phased approach. First, we addressed the established patient journey, then the needs of new patients.
2. Make Sure Important Information is Just Two Clicks Away
TMC Health’s previous website grew to include more than 1,300 pages of content. It was a maze to navigate. Our challenge was to simplify it so people could find what they needed with minimal effort. We started by conducting a content audit and inventory, then we built a restructured site map with improved hierarchy that prioritized important information. We also condensed content and sunset out of date information. In the end, we were able to get those 1,300 down to about 400, so that no critical information was more than two clicks away.
Next, we turned our attention to TMC’s internal users. To make sure they could add necessary content without overwhelming the site or patients using it, we developed content writing guidelines tailored for healthcare that focus on clarity, accessibility, and relevance. Then, we streamlined the back end to make it simpler for content writers to manage and update information across the network and reduce the need for training. We also added formatting and character count limits in the CMS to ensure new content was concise and is easy to skim.
While finding the information you need fast is critical when your health is on the line, it’s true on any website. Many companies, especially in the business world, overcomplicate their sites—they want potential customers to spend time on it. But I would caution to pick your moments. Customers come to your site for many reasons—sometimes they need information fast, and other times they’re there to learn. Prioritize accordingly.
3. Use Visual Branding to Create Cohesiveness—and Differentiation
TMC Health is comprised of 10 clinics and facilities. On their previous site, these were all visually branded the same. Typically, consistent branding is a best practice, but in this case, it created confusion for users. Our challenge was to find a way to create alignment with the primary TMC Health brand structure while making it easy for people to quickly differentiate between locations. We solved this by developing an overarching color scheme and using different but visually related colors for each location. Importantly, though, we kept the page layout consistent so users could quickly find or navigate to the information they needed.
This situation isn’t exclusive to healthcare—large corporations with multiple lines of business often face similar challenges. The big takeaway here is that color can provide cohesiveness, but in a situation where everything else is consistent, it can be a differentiator that helps the user—in this case a patient—quickly understand that they are in the right place.
Design Thinking: Empathy Builds Trust
Patient care begins at the digital front door. It’s a healthcare system’s first opportunity to build trust and demonstrate the level of care people can expect throughout their healthcare journey, from routine family care to urgent help in an emergency. A digital front door built on a solid foundation of design thinking and that prioritizes the user journey, can make a real difference in moments that matter, perhaps even saving lives.
JumpStart Your Technology Project—and Stay on Track—with Kopius!
At Kopius, we harness the power of people, data and emerging technologies to build innovative solutions that help our customers navigate continual change and solve formidable challenges. To accelerate our customers’ success, we’ve designed a JumpStart program to prioritize digital transformation together.
Posted on November 5, 2024 by Diego Anfossi, Vice President of Delivery, and Matias Mazzucchelli, Managing Director
Everywhere you go, emerging technology like generative AI (GenAI) is top of mind. Organizations are wisely racing to incorporate it into their workflows to gain insights and efficiencies that will drive customer value and give them a competitive edge. But many of the fundamental challenges that faced IT and development teams prior to the advent of GenAI remain, and chief among them are simply bandwidth and budget. In fact, in the Skillsoft 2023-2024 IT Skills and Salary Report, more than 5,700 respondents identified resource and budget constraints as the number one challenge their organizations face.
At Kopius, we hear this from our clients every day. Your senior team members are so bogged down with day-to-day responsibilities, they don’t have the time to address emergent business needs, much less innovate. But adding headcount is both time-consuming and costly. It takes time and effort to find the right people with the right skills. You don’t always have the budget for full-time staff, or you may only need extra help for a short period of time.
At Kopius, we are excited to introduce Kopius Labs, a new resourcing solution designed to meet you where you are, so you can quickly and cost effectively stand up a team for a pressing, usually short-term project.
Your Team, Your Way—Flexible and Cost-Effective Blended Talent Teams
If you’ve worked with Kopius before, you know our team of inspired realists is our superpower. What you might not realize is how much work we put in behind the scenes to identify the best talent. And we don’t stop there—we also provide continuing education to make sure they’re always at the top of their game. Our near shore, LatAm-based teams are a blend of experts in a broad range of technologies and principles, people with solid, mid-level experience, emerging talent fresh out of Kopius Academy, our certification program, and everything in between.
If you have a small project or short-term need, we can quickly and cost effectively stand-up a Kopius Lab—a team of people with blended levels of expertise, some who are between longer term projects, to close the gap.
Kopius Labs is a win-win for both you and our team members. You benefit from rapidly advancing design thinking, accelerated feature development, and groundbreaking R&D work, and our teams gain rewarding opportunities and valuable experience working on cutting edge projects. All of this is delivered through a cost-effective, blended team structure, ensuring high-impact results without the expense of high-priced resources.
Just tell us what problem you’re trying to solve, and we’ll spin up a custom Kopius Lab to resource it.
Kopius Labs—A Right-Sized Resource Solution
At Kopius, we’re still focused on digital leadership: developing digital products and custom applications powered by technology, data, and IoT. And we still deliver services through all our usual resourcing approaches: future-state workshops, end-to-end project delivery, managed services, and with embedded team members. Now, with the addition of Kopius Labs, we can help our customers quickly fill technical gaps between those larger scale and longer-term projects.
Here are just a few ways Kopius Labs can help:
Managing Daily Operations Every company has operational upkeep—tasks you must do to keep things running smoothly. But it shouldn’t keep your senior team members from contributing where you need them most. Kopius can spin up a Lab to handle the everyday so you can use your team more effectively.
Addressing Emergent Needs No matter how well you plan, something unexpected always comes up. Need to quickly ramp up your technical resources to handle an ad hoc project or augment your team during busy season? Kopius can spin up a Lab so you can scale your team quickly.
Experimenting and Innovating Sometimes, you just need to understand if something is the right approach for your company. Looking to explore a new idea, test something quickly, or whip up a quick proof of concept? Kopius, can spin up a Lab to make sure you’re headed in the right direction.
Kopius Labs is all about scale, flexibility, and speed—at a competitive price, of course.
Innovate and Scale—Quickly and Cost Effectively—with Kopius Labs!
At Kopius, we harness the power of people, data and emerging technologies to build innovative solutions that help our customers navigate continual change and solve formidable challenges. To accelerate our customers’ success, we’ve designed Kopius Labs so you can innovate and scale quickly and cost effectively
Posted on November 5, 2024 by Rob Carek, Vice President, Client Solutions
There is so much intellectual consideration given to the application development lifecycle.
Methodologies like Agile, DevOps, and DevSecOps, which are designed to drive more value by getting new features and enhancements to market faster, with fewer issues, are evidence of this. But very few people think about managing the code lifecycle, at least not beyond a single product. We often just accept the limitations and inefficiencies around code development.
AI-fueled CodeOps is here to change that.
CodeOps is designed to relieve developers of repetitive coding so they can focus on higher level work and accelerate the velocity at which they can get new features and enhancements to market.
Repetitive Coding—and Testing—Is Inefficient
Companies typically align development teams to a single product, and those teams rarely branch outside their own area of focus. The approach has both advantages and disadvantages. On the upside, developers have greater context for their work. They know their products and can build on code they created. The downside, especially for companies with multiple products, is that developers waste large amounts of time writing code that does things that already exist within other products. They’re writing code to do the same thing again and again.
It’s wildly inefficient.
And it’s just not developing code—it’s testing it, too. You develop the code, you develop the test code, you identify and address issues, you release, you fix bugs. The inefficiencies grow exponentially, especially across multiple products. You can see how this might open the company to greater exposure from a security standpoint, as well.
But what if you could find similarities between requirements, develop code to address them, and use it everywhere those requirements exist? What impact would it have on your company, customers, and development teams?
CodeOps Accelerates Velocity—and Value—at Scale
Enter CodeOps.
CodeOps is a methodology that prioritizes reuse of existing code wherever possible. Organizations can use it to reduce development time and get new products, features, and enhancements faster and more securely by reusing, repurposing, or building on code they already have. It entails adopting new ways of thinking, putting new practices and processes in place, and using technology like GenAI to match requirements with reusable, modular pieces of code stored in a code library, so new code is written only when it’s not in the library and/or is truly unique to a single product.
The obvious gains are consistency, efficiency, and security. Products are more structurally similar, and developers aren’t spending hours recoding the same thing dozens of times—or testing it. You already know it works. If your organization uses DevSecOps practices, you know security was a primary consideration in its development. And if there is an issue, once a patch is deployed, it is fixed everywhere it is in use.
But CodeOps is more than just an efficiency play. By using code from the library, even as a starting point, developers can put more time and effort into coding things that are going to have a big impact on your products—things that drive value to your customers and create value for your company. And from a developer’s perspective, that is more interesting, rewarding, and desirable work.
As with agile, DevOps, and DevSecOps, CodeOps requires cultural and process changes. Developers must adopt new ways of working, but they also must be willing to trust the code.
Ignite CodeOps Adoption with an External Catalyst
All the major code platforms—Jira, GitHub, Azure DevOps, Slack—are actively exploring how to integrate CodeOps into their solutions, and third-party tools are emerging, as well. They are all nascent, with some working better than others, which makes it difficult to determine which one will best serve you in the long run. In addition, adopting CodeOps is more than just bolting on a technology solution. Like Agile and DevOps before it, CodeOps requires a cultural shift. Developers must adopt a new mindset and new ways of working. And they must learn to trust the existing code modules enough to incorporate and build on them.
These technical, organizational, and cultural barriers make it challenging to figure out how to get started, especially when your teams have so much to do. Sometimes, it takes an external catalyst to make CodeOps real. At Kopius, we’ve developed a solution to help organizations adopt CodeOps without having to tackle the organization and cultural transformation or make a long-term commitment to a platform that is still figuring out its approach.
First, we use GenAI to intelligently review your backlog and identify commonalities in new requests. Next, we aggregate those requests and develop requirements to address them. Then, we develop code to cover the bulk of those commonalities and validate it with your developers to get their feedback and buy in. The code is stored in the code library and pushed to the right code repositories. Then, when you’re ready to tackle one of those new requests in a sprint, your developers simply pull the relevant code from the repository and use it as-is or as a starting point. As additional new requests come in, the process is repeated.
Companies gain the advantages that come with looking at code across their entire portfolio and maintaining it by feature and functionality, without disrupting existing development processes.
It’s a smart point of entry for any organization wanting to get started with CodeOps today.
CodeOps: A GenAI Approach to Working Smarter, Not Harder
Ultimately, CodeOps solves a fundamental problem that many organizations have—writing the same requirements and code for multiple products. It’s a hard challenge to overcome because the organizational constructs inherent in development teams lend themselves to a product-by-product approach.
But with a little help from GenAI and an external catalyst like Kopius developers can work smarter, not harder and accelerate the velocity at which they can deliver value.
JumpStart Your Technology Project—and Stay on Track—with Kopius!
At Kopius, we harness the power of people, data and emerging technologies to build innovative solutions that help our customers navigate continual change and solve formidable challenges. To accelerate our customers’ success, we’ve designed a JumpStart program to prioritize digital transformation together.
Posted on October 28, 2024 by Kopius Editorial Board with Rob Carek
Rob Carek Explains Why CodeOps is a Win for Businesses, Customers, and Developers
Across the enterprise, in every industry vertical and every operational and functional area, organizations are racing to take advantage of Generative AI (GenAI). It’s already in use in 65% of organizations, according to a McKinsey Global Survey. For software companies, much of the focus has been on using GenAI to generate code. But the approach has challenges. Depending on the tool developers use, the code accuracy rate is only between 31% and 65%, according to a Bilkent University study. The general consensus is it’s buggy and poses hidden security risks.
But software companies and developers now have another meaningful approach to GenAI at their disposal—CodeOps. GenAI fueled CodeOps is an approach that now enables developers to reuse internally owned, fully approved, modular coding building blocks—systematically. And it’s driving a transformational shift that creates business and customer value, unburdens developers of mundane and repetitive coding, and enables them to innovate.
We sat down with Rob Carek, Vice President of Client Solutions at Kopius, to introduce you to CodeOps.
Tell me about CodeOps. What is it and what problem does it solve?
Modern software development processes are wildly inefficient. A fundamental challenge, at least for companies with more than one product or application, is that there’s no practical way to reuse code. So, if you have a suite of 20 products, and every single one of them has a similar feature, your development teams have built that feature 20 different times—and they do it differently, every single time. In theory, a human could pour over requirements and search code repositories to find commonalities and reuse existing code, but that’s just not practical—it would be far more work than just rebuilding it.
But with the advent of GenAI, code reuse is NOW an addressable problem.
CodeOps is a code reuse strategy, and GenAI is not only the enabler, but also the accelerator. The idea is that companies can now develop reusable, modular code and store it in a library or repository. Then, GenAI can be used to search for existing code to use or build on instead of developing everything from scratch.
What are the big benefits of CodeOps?
There are four big benefits that I see: efficiency, innovation, faster time to market, and security. From an efficiency standpoint, since existing code is being repurposed, companies can save a ton of development and testing time. And when you think about how that is amplified across a whole suite of products—well, the gains are almost exponential. And all the time they save, they can spend innovating—building new features and enhancements that are unique to a given product and require original code. It’s the more challenging and interesting part of a developer’s job and where they really want to spend their time, so there’s a human benefit. It also means that things that really move the needle get to market and in customers’ hands sooner.
From a security standpoint, anything in the library is proven code—you know it meets organizational security and compliance standards. But, again, the impact really comes at scale. If you push a patch, everything updates, every vulnerability is closed wherever the code is in use.
Is CodeOps compatible with DevOps and DevSecOps?
Absolutely. The goal of DevOps is to break down silos between development and operations so new products, features, and enhancements get to market faster, more efficiently, and with fewer issues. DevSecOps prioritizes security at every step of the process. But both practices are focused on code development at the product or team level. CodeOps addresses a need at the organizational level, across multiple products. By reusing code wherever possible, CodeOps amplifies DevOps and DevSecOps outcomes—new things get to market even faster, even more efficiently, and with even fewer issues.
How can organizations get started with CodeOps?
Many of the major code platforms are starting to explore CodeOps and looking for ways to integrate it into their solutions, but it’s still very early days. I anticipate the first place they will start is using LLMs to identify commonalities in requirements. That doesn’t account for developing code that fulfills those requirements, and it’s going to be a long while before we see integrated, searchable code libraries. But that doesn’t mean you have to wait until they figure it out to get started.
At Kopius, we’ve developed a solution companies can use to adopt CodeOps today. We use GenAI to look at your backlog and identify commonalities in new requests and aggregate them. Then, we develop requirements and develop code to address them and validate it. The code is pushed to your code repository so when you’re ready to work those requests into a sprint, your developers can access it. It’s a more organic way to build a library of existing, pre-approved code that doesn’t require your teams to operate any differently than they do now.
What will it take to get developers to adopt CodeOps?
Modern development practices are simply not designed for content reuse at scale—there’s no precedent for it. And culturally, developers will look at someone else’s code and think, “I wouldn’t have done it that way.” So, like DevOps, getting developers to adopt CodeOps is going to take cultural change. Kopius’ solution takes that into consideration. It’s a hybrid human / technology approach that builds trust and buy-in by actively engaging developers in reviewing requirements and code and providing feedback. That way, they’ve contributed to it and have more confidence in it.
And as I mentioned earlier, CodeOps frees developers from the repetitive and mundane—things that are table stakes, so they have more time for developing things that are truly innovative. It’s a win-win.
What’s the single, most important thing companies should know about GenAI-fueled CodeOps?
GenAI-fueled CodeOps isn’t just an incremental improvement. It’s a truly transformational shift that will enable organizations to develop code at speed and scale, drive value into customers’ hands at speed, and free developers from the burden of repetitive, mundane work so they can focus on innovating.
Ultimately, GenAI-fueled CodeOps makes the most of what both technology and humans bring to the table—and rapidly scales it.
JumpStart Your Technology Project—and Stay on Track—with Kopius!
At Kopius, we harness the power of people, data and emerging technologies to build innovative solutions that help our customers navigate continual change and solve formidable challenges. To accelerate our customers’ success, we’ve designed a JumpStart program to prioritize digital transformation together.
Generative artificial intelligence (AI) adoption has grown exponentially in recent years, with more than half of United States employees already using AI to complete work-related tasks. Generative AI presents many possibilities for creation, from producing music and art to entire virtual worlds. It also has practical uses, including optimizing various business processes.
In this guide, you’ll learn how generative AI works, its applications, and how to leverage it for business success.
Generative AI is defined as a type of artificial intelligence technology that can create various types of content, including text, video, images, music, and audio. Types of generative AI include:
Text: Generative AI can produce essays, scripts, blogs, news articles, and even poetry. The training process involves consuming massive amounts of text from articles, books, and websites to find patterns and relationships in human languages. Examples of generative AI that can create text include Perplexity AI and ChatGPT.
Imagery: This type of AI learns by analyzing image datasets with text descriptions or captions. This way, it can understand different concepts and merge them together to create an image. These image technologies can produce diverse images in various mediums, from oil painting style to animation.
Sound: AI music generators are trained on various music tracks and metadata to find patterns and features across music genres. They can also learn lyrics to songs and create music.
Coding: Generative AI technology can be exposed to large code datasets in various program languages, like Java. Through this training, they can detect patterns, structures, and practices in these languages to write and improve code.
Video: Generative AI can create video from visual, text, and audio sources. They can even be trained on how to use video editing software and apply effects to existing videos.
Research discovery: Many generative AI strategies can automate the research process and decipher complex texts. This type of AI can analyze research patterns and identify key information or produce summaries.
How Does Generative AI Differ From Other AI Technologies?
Here are the main differences between generative AI and other AI technologies:
Generative AI vs. predictive AI: In contrast to generative AI, predictive AI uses patterns in past data to forecast outcomes and insights. Many organizations have used this technology to sharpen decision-making and develop data-driven strategies.
Generative AI vs. conversational AI: Conversational AI helps AI systems like chatbots interact with humans in a natural way. It uses natural language processing and machine learning to understand language and provide human-like text or speech responses.
The Evolution of Generative AI in Recent Years
Over several decades, we’ve seen several advances in generative AI. The language modeling techniques that help form the foundation of generative AI can be traced back to the 1950s and 1960s. In the 2010s, generative adversarial networks (GANs) — a type of machine learning algorithm — were introduced, helping AI create convincingly real images, videos, and audio of real people.
Transformers, a type of machine learning, have also led to breakthrough language models. Transformers have made it possible for researchers to train larger models without needing to label the data beforehand. As a result, newer models are trained on larger datasets — generating answers with more depth.
Transformers also allow these models to discover connections between words across pages or books rather than in individual sentences. They can even be used to track connections to analyze chemicals, code, proteins, and DNA.
How Does Generative AI Work?
Generative AI is powered by machine learning models or neural network techniques to learn the patterns and relationships of human-created content. Training involves tuning the model’s parameters for different use cases and then fine-tuning those results. For instance, to create a chatbot for an e-commerce site, you might train it on common questions customers ask and the responses often given to them.
Additionally, there are different types of AI models that work in different ways, such as discriminative models. While generative AI models dive deep into the underlying distribution of input data to generate new samples that closely resemble training data, discriminative models focus on learning the decision boundary separating classes within the input data. Rather than modeling the dataset, they target the conditional likelihood distribution of labels from the input data.
Key Mechanisms Behind Generative AI
Generative AI technology specifically relies on algorithms like Variational Autoencoders (VAE) and GANs that are trained to capture underlying structures and probabilistic distributes that define the data. It then uses these learned patterns to generate new content.
VAEs: VAEs consist of two neural networks, referred to as the encoder and decoder. The encoder is responsible for converting the input into a smaller representation of the data. With this compressed version, the decoder can then reconstruct the original input data and discard irrelevant information to generate novel data.
GANs: GANs pit two neural networks against one another — a generator that produces new examples and a discriminatory one that learns to distinguish the new content as real (from the domain) or fake (generated).
As the models get smarter, they will produce better content, and the discriminator will get better at spotting generated content. The procedure then repeats, pushing improvements until the generated content is indistinguishable from the existing content.
It’s also important to learn about the architecture of AI generative models. The most common is the transformer network, which consists of multiple layers — self-attention, feed-forward, and normalization layers. These layers work together to decipher and predict streams of data, which could include text, protein sequences, or even images.
Generative AI Applications
Generative AI has the potential to enhance the customer experience, speed up product development, and improve employee productivity, spanning use cases from retail to research and development.
Here are just a few ways industries are leveraging generative AI for success:
Customer Service
Generative AI has led to improvements in customer operations, improving the customer experience and employee productivity through digital self-service and augmenting agent skills. One study found that a company with over 5,000 customer service agents saw increased issue resolution by 14% per hour and reduced time spent handling issues by 9%. Examples of operational improvements generative AI can make include:
Customer self-service
Reduced response time
Resolution during initial contact
Increased sales
Generative AI-fueled chatbots can provide immediate and personalized responses to customer inquiries, regardless of the complexity of the problem, or the language and location of the customer. Generative AI can also enhance coaching and quality assurance by gathering user insights, thereby increasing productivity and sales.
Ultimately, automation through generative AI can improve the quality and effectiveness of interactions, freeing up time for customer care teams to respond to inquiries only solvable by human agents.
Marketing
Generative AI can also transform marketing processes with efficient and effective content creation, SEO optimization, and product discovery. Here are some use cases for this technology in the marketing industry:
Rapid content creation: Generative AI can reduce the time required to come up with content, saving teams time and effort. It can also ensure a uniform brand voice, writing style, and format, helping teams personalize marketing messages for different customer segments and demographics.
Search engine optimization (SEO): Generative AI can lead to higher conversion for a reduced cost through SEO optimization. The technology can synthesize key SEO words, support content creation, and distribute the targeted content to customers.
Product discovery: Generative AI can be personalized with text, images, and speech and a deep understanding of customer profiles. It can leverage customer user insights to help customers discover relevant products, helping companies achieve higher website conversion rates.
Sales
Generative AI also has the possibility to change how B2B and B2C companies approach sales by:
Increasing probability of sale: Generative AI technology can identify and prioritize sales leads by gathering customer data, creating profiles, and suggesting actions to improve client engagement.
Improving lead development: This technology can also help sales teams nurture leads by integrating relevant product sales and customer profiles to create discussion scripts for customer conversation. It can also automate follow-ups with customers, nurturing leads until the client is ready to interact with a human sales agent.
Software Engineering
Software engineering plays a significant role in many companies, and with generative AI, software engineers can use augmented coding and train large language models to generate code. Generative AI can lead to cost savings for companies by accelerating coding processes like creating initial code drafts and generating new system designs.
One study found that software developers using generative AI completed tasks 55.8% faster than those not using it. Another study found that by reducing the time needed for these tasks, engineers reported a better work experience, citing improvements in flow, happiness, and fulfillment.
Research and Development
The life sciences and chemical industries are already using generative AI foundation models in research and development. These foundation models can generate candidate molecules and accelerate drug development. In addition to increasing productivity in producing candidate designs, generative AI can optimize manufacturing designs, leading to cost reductions in production and logistics.
Generative AI can also be used to optimize health care processes, such as appointment scheduling and analytics, to help improve efficiency over time. Digital AI solutions can ultimately lead to improved patient care and hospital efficiency.
Limitations and Challenges of Generative AI
Since generative AI is so new, there are some inherent risks involved in using it — some recognized and some yet to be discovered. Examples of generative AI risks include:
Accuracy and reliability concerns: While the information generative AI produces sound convincing, sometimes the information can be wrong. These models are still in the early stages of development, so it’s important to assess responses for appropriateness, usefulness, and accuracy before using them to distribute information.
Copyright issues: Generative AI models are trained on large amounts of publicly available data. They are not designed to be compliant with copyright laws, making it important to pay close attention to your company’s use of AI.
Bias: It’s also important to enact policies or controls that can detect bias within the AI outputs. That way, you can deal with them in a way that is consistent with your company policy and legal requirements.
Cybersecurity and fraud: It’s important to be prepared for misuse of generative AI for cyber and fraud attacks, such as those that use deep fakes.
There are several ways to mitigate risks when implementing generative AI in businesses. By committing to guidelines and having safeguards in place, you can ensure the technology solutions are accurate, safe, and trusted to help your team flourish.
The Future of Generative AI
In the coming years, generative AI technology will continue to evolve, revolutionizing how we work. Reports predict that we will see $42 billion in annual spending by 2030 on generalized AI use cases, such as writing, researching, and summarizing strategies. More than 50% of this spending will be on communications platforms and chatbots — driving significant improvements in the customer and employee experience.
These tech solutions will make advancements in translation, drug discovery, and the generation of new content, from video and text to music and fashion design. We’ll also see a significant impact when integrating these capabilities with existing tools, such as grammar checkers and design programs.
More and more businesses will customize generative AI using their own data to improve communication and branding. Programmers will also use generative AI to enforce company-specific best practices for formatting and writing more readable, consistent code.
In the future, generative AI models will likely be expanded to support 3D modeling, product design, drug development, and business processes — making it easier to generate new product ideas and explore business ideas. When used for training, generative AI can automatically identify best practices to help train employees more efficiently in one part of an organization.
Preparing for What’s Next
Generative AI solutions are setting the pace for innovation and strategic leadership in the business world, fostering a culture of informed decision-making, optimized marketing strategies, and enhanced efficiency. With numerous advancements on the horizon for generative AI, you can stay ahead of the competition by implementing generative AI for your business.
To recognize the full potential of generative AI, consider collaborating with digital technology consultants. At Kopius, we can translate business problems into AI solutions. Collaborating with us can provide a competitive advantage, as we ensure AI aligns with your business objectives. We’ll identify areas where AI and machine learning can offer benefits, such as enhancing product offerings, improving customer service, or optimizing operational efficiency.
We can also help you mitigate generative AI risks like privacy concerns and potential biases in AI algorithms. Being proactive about these risks can enhance your organization’s reputation and give you a competitive advantage.
Partner With Kopius for Generative AI Innovation
Generative AI is much more than a technological strategy — it’s a transformative force reshaping the business landscape. As AI continues to evolve, its impact on executive leadership and business strategy will only increase. By effectively implementing AI, you can position your organization for success in the competitive business environment of the future.
Kopius can help you use generative AI to reach your objectives and achieve significant growth. Our consultants have the knowledge and expertise to maximize the benefits of AI and machine learning, allowing you to drive real and impactful results. By considering your unique needs and goals, we’ll develop a plan that works best for your organization. We can also help mitigate security risks associated with data breaches, regulatory violations, and data quality.
At Kopius, we’ve designed a program to JumpStart your customer, technology, and data success. Tailored to your needs, our user-centric approach, tech smarts, and collaboration with your stakeholders equip teams with the skills and mindset needed to:
Identify unmet customer, employee, or business needs
Align on priorities
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Data has experienced a metamorphosis in its perceived value and management within the corporate sphere. Previously underestimated and frequently discarded, data was often relegated to basic reports or neglected due to a lack of understanding and governance. This limited vision, combined with emerging technologies, led to an overwhelming influx of data, and nowhere for it to go. There was little to no governance or understanding of what data they had, or how long they had it.
In the early 2000s, enterprises primarily used siloed databases, isolated data sets with limited accessibility. The 2010s saw the rise of Data Warehouses, which brought together disparate datasets but often led to bottlenecks. Data Lakes emerged as a solution to store vast quantities of raw data and quickly became swamps without adequate governance. Monolithic IT and data engineering groups would struggle to document, catalog, and secure the growing stockpile of data. Product owners and teams that would want, or need access to data would have to request access and wait. Sometimes those requests would end up in a backlog and forgotten about.
In this new dawn of data awareness, the Data Mesh emerges as a revolutionary concept, enabling organizations to efficiently manage, process, and gain insights from their data. As organizations realize data’s pivotal role in digital transformation, it becomes imperative to shift from legacy architectures to more adaptive solutions, making Data Mesh an attractive option.
The Basics of a Data Mesh
The importance of personalized customer experiences should not be understated. More than ever, consumers are faced with endless options. To stand out from competitors, businesses must use data and customer behavior insights to curate tailored and dynamic customer journeys that both delight and command their audience. Analyze purchasing history, demographics, web activity, and other data to understand your customer, as well as their likes and dislikes. Use these insights to design customized customer experiences that increase conversion, retention, and ultimately, satisfaction.
When discussing data architecture concepts, the terms “legacy” or “traditional” imply centralized data management concepts, characterized by monolithic architectures developed and maintained by a data engineering organization within the company. Business units outside of IT would often feel left in the dark, waiting for the data team to address their specific needs and leading to inefficiencies.
First coined in 2019, the Data Mesh paradigm is a decentralized, self-service approach to data architecture. There are four central principles that Data Mesh is based on: Domain ownership, treating data as a product, self-service infrastructure, and federated computational governance.
With Data Mesh, teams (Domains) are empowered to own and manage their data (Product). This requires stewardship at the team level to effectively manage their own resources to ingest, persist and serve data to their end users. Data stewards are responsible for the quality, reliability, security, and accessibility of the data. Data stewards bridge the gap between decentralized teams and enterprise-level governance and oversight.
While teams enjoy autonomy, chaos would ensue without a federated governance approach. This ensures standards, policies and best practices are followed across all product owners and data stewards.
Implementing a Data Mesh requires significant investment in both infrastructure and enhancing teams with the resources and expertise required to manage their own resources. It requires a fundamental change in companies’ mindset of how they treat data.
While a Lakehouse would aim to combine the best of Data Lakes and Data Warehouses, Data Mesh ventures further by decentralizing ownership and control of data. While Data Fabric focuses on seamless data access and integration across disparate sources, Data Mesh emphasizes domain-based ownership. On the other hand, event-driven architectures prioritize real-time data flow and reactions, which can be complementary to Data Mesh.
When and Where to Implement Data Mesh
Large Organizations with Data Rich Domains: With large organizations, departments often deal with a deluge of data. From Human Resources to Sales, each team has their own requirements for how their data is used, stored, and accessed. As teams consume more data, time to market and development efficiency suffer in centralized architectures. External resources and time constraints are often the biggest issue. By implementing Data Mesh, teams can work independently and take control of their data, increasing efficiency and quality. As a result, teams can optimize and enrich their product offering and cut costs by streamlining ELT/ETL processes and workflows.
With direct control over their data, teams can tune and tailor their data solutions to better meet customer needs.
Complex Ecosystem: Organizations, especially those operating in dynamic environments with intricate interdependencies, often face challenges in centralized data structures. In such architectures, there’s limited control over resource allocation, utilization, and management, which can hinder teams from maximizing the potential of their data. Centralized approaches can curtail innovation due to rigid schemas, inflexible data pipelines, and lack of domain-specific customization. Data Mesh offers organizations the flexibility to adapt to evolving data needs and utilize domain-specific expertise to curate, process, and consume data tailored to their unique requirements.
Rapidly growing data environments: Today’s digital age sees organizations collecting data at an unprecedented scale. The sheer volume of data can be overwhelming with the influx of IoT devices, vendor integrations, user interactions, and digital transactions. Centralized teams often grapple with scaling issues, processing delays, and the challenge of timely data delivery. Data Mesh addresses this by distributing the data responsibility across different domains or teams. Multiple decentralized units handle the influx as data inflow increases, ensuring timely processing and reducing system downtime. The result is a more resilient data infrastructure ready to meet both current demands and future needs.
When Not to Implement Data Mesh
Small to Medium-sized Enterprises (SMEs): While Data Mesh presents numerous advantages, it may not be suitable for all organizations or projects. Smaller organizations typically handle lower data volumes and may not possess the resources needed to manage their data independently. In these cases, a centralized data architecture would be more suitable to minimize complications in design and maintenance with fewer resources to manage them.
Mature and Stable Centralized Architectures: Organizations usually only turn to new solutions when they are experiencing problems. If a well-established centralized architecture is performing and fitting the needs of the company, there isn’t a need necessarily for Data Mesh adoption. Introducing a fundamental change in how data is managed is an expensive and disruptive undertaking. Building new infrastructure and expanding team capabilities changing organizational culture takes time.
Short-term Projects: Implementing a Data Mesh requires significant time and resource investment. The benefits of a Data Mesh won’t be seen when building or designing a limited lifespan project or proof of concept. If a project’s duration doesn’t justify the investment of a Data Mesh or the scope doesn’t require domain-specific data solutions, then the benefits of a Data Mesh aren’t utilized. Traditional data architectures are usually more appropriate for these applications and don’t need the oversight/governance that a Data Mesh requires.
Opportunities Offered by Data Mesh
Scalability: Data Mesh enables organizations to scale their data processing capabilities more effectively by enabling teams to control how and when their data is processed, optimizing resource use and costs, and ensuring they remain agile amidst expanding data sources and consumer bases.
Enhanced Data Ownership: Treating data as a product rather than a byproduct or a secondary asset is revolutionary. By doing so, Data Mesh promotes a culture with a clear sense of ownership and accountability. Domains or teams that “own” their data are more inclined to ensure its quality, accuracy, and relevance. This fosters an environment where data isn’t just accumulated but is curated, refined, and optimized for its intended purpose. Over time, this leads to more prosperous, more valuable data sets that genuinely serve the organization’s needs.
Speed and Innovation: Decentralization is synonymous with autonomy. When teams have the tools and the mandate to manage their data, they are not bogged down by cross-team dependencies or bureaucratic delays. They can innovate, experiment, and iterate at a faster pace, resulting in expanded data collection and richer data sets. This agility accelerates data product development, enabling organizations to adapt to changing needs quickly, capitalize on new opportunities, and stay ahead of the curve in the competitive market.
Improved Alignment with Modern Architectures: Decentralization isn’t just a trend in data management; it’s a broader shift seen in modern organizational architectures, especially with the rise of microservices. Data Mesh naturally aligns with these contemporary structures, creating a cohesive environment where data and services coexist harmoniously. This alignment reduces friction, simplifies integrations, and ensures that the entire organizational machinery, services, and data operate in a unified, streamlined manner.
Enhanced Collaboration: As domains take ownership of their data, there’s an inclination to collaborate with other domains. This cross-functional collaboration fosters knowledge sharing, best practices, and a unified approach to data challenges, driving more holistic insights.
Constraints and Challenges
Cultural Shift: Teams may not want to own their own data or have the experience to take on the responsibility. Training initiatives, workshops, and even hiring external experts might be necessary to bridge these skill gaps.
Increased Complexity: Developing an environment that supports a Data Mesh architecture is not without its challenges. As the Data Mesh model expands, managing the growing number of interconnected resources and solving integration issues to ensure smooth communication between various domains can be a considerable obstacle. Planning appropriately to support teams with access, training and management of a Data Mesh is critical to its evolution and success. This includes well defined requirements for APIs, data exchange, and interface protocols.
Cost Implications: Transitioning to a Data Mesh could entail substantial upfront costs, including hiring additional resources, training personnel, investing in new infrastructure, and possibly overhauling existing systems.
Governance: Data Governance has become a hot topic as data architectures grow and mature. Ensuring a consistent view of data across all domains can be challenging, especially when multiple teams update or alter their datasets independently. Tools to manage integrity, security and compliance are a requirement in a Data Mesh architecture. The need for teams to have autonomy in a decentralized environment is balanced with a flexible but controlled governance model that is the foundation for federated governance. This can be a challenge when initially designing the model based on team requirements, but it’s an important step to take as early as possible when building a data platform.
Skillset: Evolving with the Data Mesh Paradigm
With an evolved mindset, the Data Mesh paradigm demands expertise that may not have previously been cultivated within traditional data teams. This transition from central data lakes to domain-oriented data products introduces complexities requiring a deep understanding of the data and the specific use cases it serves, both internally and externally. Skills such as collaboration, domain-specific knowledge translation, and data stewardship become vital. As data responsibility becomes decentralized, each team member’s role becomes more critical in ensuring data integrity, relevance, and security. As data solutions evolve, teams must adopt a mindset of perpetual learning, keeping pace with the latest methodologies, tools, and best practices related to managing their data effectively.
Embracing the Data Mesh
In the evolving landscape of data management, the Data Mesh presents a promising alternative to traditional architectures. It’s a journey of empowerment, efficiency, and decentralization. The burgeoning community support for Data Mesh, evident from the increasing number of case studies, forums, and tools developed around it, underscores its pivotal role in the future of data management. However, its success hinges on an organization’s readiness to embrace the cultural and operational shifts it demands. As with all significant transformations, due diligence, meticulous planning, and an understanding of the underlying principles are crucial for its fruitful adoption. Embracing the Data Mesh is more than just a technological shift; it’s a paradigm transformation. Organizations willing to make this leap will find themselves not just keeping up with the rapid pace of data evolution but leading the charge in innovative, data-driven solutions.
JumpStart Data Success
Innovating technology is crucial, or your business will be left behind. Our expertise in technology and business helps our clients deliver tangible outcomes and accelerate growth. At Kopius, we’ve designed a program to JumpStart your customer, technology, and data success.
Kopius has an expert emerging tech team. We bring this expertise to your JumpStart program and help uncover innovative ideas and technologies supporting your business goals. We bring fresh perspectives while focusing on your current operations to ensure the greatest success.Partner with Kopius and JumpStart your future success.
The core of future-proofing your business lies in the incorporation of cutting-edge technological trends and strategic digitization of your business operations. Combining new, transformative solutions with tried-and-true business methods is not only a practical approach but an essential one when competing in this digital age. Using the latest digital transformation trends as your guide, start envisioning the journey of future-proofing your business in order to unlock the opportunities of tomorrow.
#1 Personalization
The importance of personalized customer experiences should not be understated. More than ever, consumers are faced with endless options. To stand out from competitors, businesses must use data and customer behavior insights to curate tailored and dynamic customer journeys that both delight and command their audience. Analyze purchasing history, demographics, web activity, and other data to understand your customer, as well as their likes and dislikes. Use these insights to design customized customer experiences that increase conversion, retention, and ultimately, satisfaction.
#2 Artificial Intelligence
AI is everywhere. From autonomous vehicles and smart homes to digital assistants and chatbots, artificial intelligence is being used in a wide array of applications to improve, simplify, and speed up the tasks of everyday life. For businesses, AI and machine learning have the power to extract and decipher large amounts of data that can help predict trends and forecasts, deliver interactive personalized customer experiences, and streamline operational processes. Companies that lean on AI-driven decisions are propelled into a world of efficiency, precision, automation, and competitiveness.
#3 Sustainability
Enterprises, particularly those in the manufacturing industry, face increasing pressure to act more responsibly and consider environmental, social, and corporate governance (ESG) goals when making business decisions. Digital transformations are one way to support internal sustainable development because they lead to reduced waste, optimized resource use, and improved transparency. With sustainability in mind, businesses can build their data and technology infrastructures to reduce impact. For example, companies can switch to more energy-efficient hardware or decrease electricity consumption by migrating to the cloud.
#4 Cloud Migration
More and more companies are migrating their data from on-premises to the cloud. In fact, by 2027, it is estimated that 50% of all enterprises will use cloud services1. What is the reason behind this massive transition? Cost saving is one of the biggest factors. Leveraging cloud storage platforms eliminates the need for expensive data centers and server hardware, thereby reducing major infrastructure expenditures. And while navigating a cloud migration project can seem challenging, many turn to cloud computing partners to lead the data migration and ensure a painless shift.
Future-Proof Your Business Through Digital Transformation with Kopius
Innovating technology is crucial, or your business will be left behind. Our expertise in technology and business helps our clients deliver tangible outcomes and accelerate growth. At Kopius, we’ve designed a program to JumpStart your customer, technology, and data success.
Kopius has an expert emerging tech team. We bring this expertise to your JumpStart program and help uncover innovative ideas and technologies supporting your business goals. We bring fresh perspectives while focusing on your current operations to ensure the greatest success.
Winning the interest and loyalty of customers means more than just offering a superior product or service. The secret lies in a powerful strategy called personalization – a dynamic approach that tailors the customer experience to meet individual needs and preferences. As businesses across industries strive to create lasting connections with their customers and meet their evolving expectations, the importance of personalization in the customer experience should not be overstated. Read on to explore the compelling case for customer personalization and a step-by-step guide on how your business can embark on this journey to elevate the customer experience.
Let’s face it, generic offerings are outdated. Today, customers yearn for something more; they want an experience that resonates with their unique tastes. Personalization is the magic ingredient that taps into this desire. By tailoring products, services, and interactions to individual preferences, businesses create a sense of connection that fosters lasting loyalty. And beyond that, research from McKinsey found that companies who implemented a personalization strategy generated 40% more revenue than their counterparts who placed less emphasis on this approach. All signs point to tailored customer journeys.
Data lies at the heart of personalization, offering insights into customer behaviors. More than ever, companies have access to a wealth of customer information, such as past purchases and browsing habits, that act as the building blocks to these insights. Leveraging advanced analytics and artificial intelligence, businesses can uncover valuable patterns and trends, guiding them to craft personalized experiences for their customers.
Building a successful personalization strategy requires thoughtful consideration and calculated execution. If you are just getting started, follow these steps to build an improved and tailored customer experience that will drive remarkable results for your business:
Step 1: Gather as Much Customer Data as Possible.
At the core of every successful personalization strategy lies a deep understanding of your customers. To lay this solid foundation, start by gathering valuable data from multiple touchpoints along their journey, including website interactions, purchase history, and customer feedback. Take advantage of powerful tools like customer relationship management (CRM) software, website analytics, and social media insights to gain a holistic view of your customers’ preferences, behaviors, and pain points.
Step 2: Divide Your Customers Into Audience Segments.
With an abundance of data at your fingertips, it is time to move on to segmentation. Divide your customers into distinct groups based on shared traits like demographics, purchase behavior, and interests. Audience segmentation empowers you to personalize your messaging or offerings, address individual customer needs with accuracy, and create a sense of relevance.
Step 3: Get Personal With Your Messaging.
Now that you have completed the segmentation process, it’s time to get personal! Start by creating interesting content with tailored product recommendations, and design exclusive offers that cater specifically to the unique preferences of each of your audience segments. By doing so, you will create truly personalized experiences that captivate your audience and leave an impression.
Step 4: Automate Dynamic Content Delivery.
Offer real-time digital experiences that resonate with your customers’ interests and past interactions. Embracing innovative technologies like artificial intelligence allows you to analyze customer data, predict behavior, and implement an effective personalization strategy that delivers tailored experiences on the fly. AI-powered chatbots take personalized support a step further, offering instant assistance to resolve customer concerns and boost overall customer satisfaction levels.
Step 5: Track Your Personalization Campaigns.
Monitor the impact of your personalization strategy on customer engagement, satisfaction, and business performance. Evaluate key metrics like conversion rates and customer retention to assess their effectiveness. Utilize any insights gained to identify areas for improvement and modify your approach accordingly.
The possibilities for designing a personalized digital experience are limitless. AI-powered chatbots provide real-time personalized support, making customers feel valued and cared for. Dynamic content delivery ensures website experiences are based on individual preferences. Personalization will enrich the customer journey, increasing engagement and conversion rates. If you are ready to deliver personalized experiences, Kopius is here to help. Let’s team up to create extraordinary customer experiences for your business!
JumpStart Success with Kopius
At Kopius, we’ve designed a program to JumpStart your customer, technology, and data success.
Our JumpStart program fast-tracks business results and platform solutions. Connect with us today to enhance your customer satisfaction through a data-driven approach, drive innovation through emerging technologies, and achieve competitive advantage.
Augmented Intelligence (AI) and Machine Learning (ML) were already the technologies on everyone’s radar when the year started, and the release of Foundation Models like ChatGPT only increased the excitement about the ways that data technology can change our lives and our businesses. We are excited about these five industries that are winning at artificial intelligence.
As an organization, data and AI projects are right in our sweet spot. ChatGPT is very much in the news right now (and is a super cool tool – you can check it out here if you haven’t already).
There are a few real-world examples of how five organizations are winning at AI. We have included those use cases along with examples where our clients have been leading the way on AI-related projects.
You can find more case studies about digital transformation, data, and software application development in our Case Studies section of the website.
Brands are helping customers to visualize the outcome of their products or services using computer vision and AI. Consumers can virtually try on a new pair of glasses, a new haircut, or a fresh outfit, for example. AI can also be used to visualize a remodeled bathroom or backyard.
We helped a teledentistry, web-first brand develop a solution using computer vision to show a customer how their smile would look after potential treatment. We paired the computer vision solution with a mobile web application so customers could “see their new selfie.”
Consumer questions can be resolved faster and more accurately
Customer service can make or break customer loyalty, which is why chatbots and virtual assistants are being deployed at scale to reduce average handle time average speed-of-answer, and increase first-call resolutions.
We worked with a regional healthcare system to design and develop a “digital front door” to improve patient and provider experiences. The solution includes an interactive web search and chatbot functionality. By getting answers to patients and providers more quickly, the healthcare system is able to increase satisfaction and improve patient care and outcomes.
Finance: Preventing fraud
There’s a big opportunity for financial services organizations to use AI and deep learning solutions to recognize doubtful transactions and thwart credit card fraud which help reduce cost. Also known as anomaly detection, banks generate huge volumes of data which can be used to train machine learning models to flag fraudulent transactions.
Agriculture: Supporting ESG goals by operating more sustainably
Data technologies like computer vision can help organizations see things that humans miss. This can help with the climate crisis because it can include water waste, energy waste, and misdirected landfill waste.
The agritech industry is already harnessing data and AI since our food producers and farmers are under extreme pressure to produce more crops with less water. For example, John Deere created a robot called “See and Spray” that uses computer vision technology to monitor and spray weedicide on cotton plants in precise amounts.
We worked with PrecisionHawk to use computer vision combined with drone-based photography to analyze crops and fields to give growers precise information to better manage crops. The data produced through the computer vision project helped farmers to understand their needs and define strategies faster, which is critical in agriculture. (link to case study)
Healthcare: Identify and prevent disease
AI has an important role to play in healthcare, with uses ranging from patient call support to the diagnosis and treatment of patients.
For example, healthcare companies are creating clinical decision support systems that warn a physician in advance when a patient is at risk of having a heart attack or stroke adding critical time to their response window.
AI-supported e-learning is also helping to design learning pathways, personalized tutoring sessions, content analytics, targeted marketing, automatic grading, etc. AI has a role to play in addressing the critical healthcare training need in the wake of a healthcare worker shortage.
Artificial intelligence and machine learning are emerging as the most game-changing technologies at play right now. These are a few examples that highlight the broad use and benefits of data technologies across industries. The actual list of use cases and examples is infinite and expanding.
Kopius supports businesses seeking to govern and utilize AI and ML to build for the future. We’ve designed a program to JumpStart your customer, technology, and data success.
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Tailored to your needs, our user-centric approach, tech smarts, and collaboration with your stakeholders, equip teams with the skills and mindset needed to:
Identify unmet customer, employee, or business needs
Align on priorities
Plan & define data strategy, quality, and governance for AI and ML
As AI becomes even more integrated into business, so does AI bias.
On February 2, 2023, Microsoft released a statement from Vice Chair & President Brad Smith about responsible AI. In the wake of the newfound influence of ChatGPT and Stable Diffusion, considering the history of racial bias in AI technologies is more important than ever.
The discussion around racial bias in AI has been going on for years, and with it, there have been signs of trouble. Google fired two of its researchers, Dr. Timnit Gebru and Dr. Margaret Mitchell after they published research papers outlining how Google’s language and facial recognition AI were biased against women of color. And speech recognition software from Amazon, Microsoft, Apple, Google, and IBM misidentified speech from Black people at a rate of 35%, compared to 19% of speech from White people.
In more recent news, DEI tech startup Textio analyzed ChatGPT showing how it skewed towards writing job postings for younger, male, White candidates- and the bias increased for prompts for more specific jobs.
If you are working on an AI product or project, you should take steps to address AI bias. Here are four important questions to help make your AI more inclusive:
Have we incorporated ethical AI assessments into the production workflow from the beginning of the project? Microsoft’s Responsible AI resources include a project assessment guide.
Are we ready to disclose our data source strengths and limitations? Artificial intelligence is as biased as the data sources it draws from. The project should disclose who the data is prioritizing and who it is excluding.
Is ourAI production team diverse? How have you accounted for the perspectives of people who will use your AI product that are not represented in the project team or tech industry?
Have we listened to diverse AI experts? Dr. Joy Buolamwini and Dr. Inioluwa Deborah Raji, currently at the MIT Media Lab, are two black female researchers who are pioneers in the field of racial bias in AI.
“AI research must also acknowledge that the problems we would like to solve are not purely technical, but rather interact with a complex world full of structural challenges and inequalities. It is therefore crucial that AI researchers collaborate closely with individuals who possess diverse training and domain expertise.”
Ready to JumpStart AI in Your Business?
Kopius supports businesses seeking to govern and utilize AI and ML to build for the future. We’ve designed a program to JumpStart your customer, technology, and data success.
Tailored to your needs, our user-centric approach, tech smarts, and collaboration with your stakeholders, equip teams with the skills and mindset needed to:
Identify unmet customer, employee, or business needs
Align on priorities
Plan & define data strategy, quality, and governance for AI and ML
