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.
Every technology project starts with an outcome—a business goal that needs to be achieved. But to achieve that goal, you need to define a set of deliverables, establish a timeline, and determine a budget. Rarely do the timeline and budget line up with the work that needs to be done. There are seldom enough dollars to put the number of people on the project necessary to bring the deliverables to life within the timeline. This is often because of how challenging it is to fully scope a project up front. No matter how thorough you are, new requirements come to light, resulting in scope creep.
Many companies will lean into project management to make everything come together. Smart—a solid PMO practice is the foundation on which all successful technology projects are built.
But you can’t always project manage your way out of this type of problem. That said, there are some things you can do.
Two Key Approaches to Use When Time and Budget are Out of Sync with Project Scope
One of the most complex technology consulting programs I’ve worked on was for a new company in the healthcare space. The budget was a swag from an investor’s presentation deck and was completely out of alignment with the six-month timeline for standing up ERP manufacturing system, provider and patient registration and management portals, and an ecommerce app. Rescoping the project wasn’t an option—if every compliance parameter wasn’t met within the given timeframe, the client would have to wait an entire year to reapply with the organization that had program oversight. In the end, we met the timeline, stayed on budget, and our client was awarded the contract they were after.
We used two key approaches to make it happen. First, we brought all the right stakeholders to the table early to develop standard operating procedures (SOPs). And second, we used those SOPs, as well as compliance guidelines, to build and validate wireframes before standing up MVPs. Then we validated those before coding the actual apps.
Engage the Hive Mind
At the beginning of the initiative, we brought all the key stakeholders together for a series of workshops—one for each app we had to deliver. Every department that had a say was represented—product, engineering, sales, marketing, manufacturing, and legal. Not only did we have a binder on hand detailing hundreds of pages of compliance regulations, but we also had someone on hand who knew them inside and out. Collectively, we walked through every aspect of each app, developing standard operating procedures, strawmen, and requirements.
This hive mind approach meant we could problem solve, make decisions and come to agreements at speed and minimized our chances of going down the wrong path.
My Take
When timeline and budget aren’t in line with the work that needs to be done, you can’t afford to make mistakes. Get the people who hold the answers to your questions in a room and map out your requirements. At Kopius, we call these JumpStarts, and they may take a few days or a few weeks. Then, continue to check in with the same stakeholders at every critical juncture to validate your work.
“Measure once. Cut twice.”
For me, the project management equivalent of “measure once, cut twice,” is wireframes first, MVP second, coding third. And at each of these stages, you need to bring your stakeholders together to validate your work. For the healthcare project, once we had a thorough list of requirements, our UI/UX developed wireframes that we validated with the same group of stakeholders we initially brought to the table. This allowed us to identify and work through any potential issues up front. Then, once the wireframes were validated, we stood up MVPs for each app so stakeholders could walk through the basics of each process and validate it. Only then did we dive deep into coding all the features and functionality for the first release.
My Take
When timeline and budget aren’t in line with the work that needs to be done, the inclination can be to jump right into coding. A better approach is to double down on validating your path forward through JumpStart workshops and wireframing. This will minimize errors—and added time and costs—in the long run.
Expect the Unexpected
No matter how thorough you are in developing your requirements, there are going to be some “ahas” along the way. You have to expect the unexpected and remain flexible. But being flexible doesn’t mean saying yes to everything. Scope creep can derail a project from both a timeline and budget standpoint. For this project, we managed that by getting everyone to agree to a light phase one for each application, then planned to iterate, releasing new features every two weeks after launch.
Looking Ahead: The GenAI Approach
Like many technology companies, Kopius is actively integrating generative AI (GenAI) into our processes, and I’m working on a set of custom GPTs that I believe can make a difference when time and budget are out of sync with requested deliverables. By entering business and technical requirements into it—maybe even a transcript from a discovery session—and asking it to generate common use cases that serve as starting points for designing application features, we can streamline the work involved in building new apps. The prompt engineering requires a lot of up-front effort, but once that initial lift is done, we’ll be able to use it again and again.
Undoubtedly GenAI will deliver thousands of small efficiencies like this, but it’s only part of the equation. The time / budget / scope challenge is an inherent part of software development, and solving it is always going to take a multi-faceted approach.
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 AI (GenAI) adoption is surging. Sixty five percent of respondents to the McKinsey Global Survey on the State of AI in Early 2024 indicate their businesses are using generative AI in at least one functional area. Yet, more than half of individual GenAI adopters use unapproved tools at work, according to a Salesforce survey. Clearly, businesses want and need to implement the technology to meet their business goals, but in the absence of a clear path forward, employees are finding ways to adopt it anyway, perhaps putting sensitive data at risk. Organizations need to move fast, put a strategy in place, and implement pilot projects with impact.
But what’s the best way to get started?
We get this question often at Kopius. Maybe you have a problem you need to solve in mind or a general use case, or maybe that’s not yet clear. You might understand the possibilities but haven’t narrowed down an opportunity or area of impact. Regardless of which camp you’re in, when we peel back the onion, we find that most companies need to step back and address fundamental issues with their data foundation before they can begin to tackle GenAI.
At Kopius, we have a detailed framework for walking you through the things you need to take into consideration to identify a GenAI pilot project and build a data foundation to support. But asking—and answering questions like the ones below—is at the root of it.
What problem are you trying to solve?
In a survey of Chief Data Officers (CDOs) by Harvard Business Review, 80% of respondents believed GenAI would eventually transform their organization’s business environment, and 62% said their organizations intended to increase spending on it. But no company can afford to make investments that don’t deliver on outcomes.While there is value in just getting started, it’s both worthwhile and necessary to define an initial use case. Not only do you want your program to have impact, but the GenAI ecosystem is so broad that without some sort of use case, you will be unable to define what type of outputs need to be generated.
Some companies will have a clear use case, while others will have a more general sense of where they’re headed. Still others are working with an “AI us” request from senior leadership to explore the landscape.Wherever you are in this process, our framework is designed to help you identify a meaningful pilot project.
What are your data sources? What do you need to capture? Next, you’ll need to take stock of your data sources, so you have a solid understanding of the full set of data you’re working with. What inputs do you have coming in and what inputs do you need to get to your end goal? Often, there is a project behind a project here. If you don’t have the data you need to solve the business challenge, then you’ll have to develop and implement a plan to get it. For instance, say you want to measure the impact of weather conditions on fleet performance, and you’re planning on using IoT data from your vehicles. You’ll also need to determine what weather data you need and put a solution in place to get it.
What is the state of your data? Is it relevant, quality, and properly housed and structured?
With GenAI, your ability to get quality outputs that deliver on business outcomes depends on the quality of your inputs. That means data must be current, accurate, and appropriately stored and structured for your use case. For instance, if you’re developing a GenAI enabled chatbot that employees can query to get information about policies, procedures, and benefits, you’ll need to make sure that information is current and accurate.
At this point, you’ll also need to consider where the data is being stored and what format it’s in. For instance, JSON documents sitting in non-relational database or tables sitting in a SQL database are not necessarily a model for GenAI success. You may have to put your raw data in a data lake, or if you already have a data lake, you may need to warehouse and structure your data so that it’s in the right format to efficiently deliver the output you want.
What governance and security measures do you need to take? Data governance is about putting the policies and procedures in place for collecting, handling, structuring, maintaining, and auditing your data so that it is accurate and reliable. All these things impact data quality, and without quality data, any outputs your GenAI solution delivers are meaningless. Another important aspect of data governance is ensuring you are compliant with HIPPA or any other regulatory mandates that are relevant to your organization.
Data security, in this context, is a subset of data governance. It is about protecting your data from external threats and internal mishandling, including what user groups and/or individuals within your organization can access what. Do you have PPI in your system? Salary data? If so, who can modify it and who can read it? Your answers to these questions may inform what data platform is best for you and how your solution needs to be structured.
What is your endgame? What types of outputs are you looking for?
The problem you’re trying to solve is closely tied to the types of outputs you are looking for. It’s likely that exploration of the former will inform conversation of the latter. Are you building a chatbot that customers can interact with? Are you looking for predictive insights about maintaining a fleet or preventing accidents? Are you looking for dashboards and reporting? All this is relevant.This also gets into questions about your user profile—who will be using the solution, when and where will they be using it, what matters most to them, and what should the experience be like?
A Rapidly Evolving Data Platform Landscape Drives Complexity
Getting started with GenAI is further complicated by how complex the third-party GenAI, cloud, and data platform landscapes are and how quickly they are evolving. There are so many data warehouse and data lake solutions on the market—and GenAI foundational models—and they are advancing so rapidly that it would be difficult for any enterprise to sort through the options to determine what is best. Companies that already have data platforms must solve their business challenges using the tools they have, and it’s not always straightforward. Wherever you land on the data maturity spectrum, Kopius’ framework is designed to help you find an effective path forward, one that will deliver critical business outcomes.
Do You Have the Right Data Foundation in Place for GenAI?
In the previously mentioned survey by Harvard Business Review, only 37% of respondents agreed that their organizations have the right data foundation for GenAI—and only 11% agreed strongly. But narrowing in on a business problem and the outcomes you want and defining a use case can be useful in guiding what steps you’ll need to take to put a solid data foundation in place.
One last thought—there are so many GenAI solutions and data platforms on the market. Don’t worry too much about what’s under the hood. There are plenty of ways to get there. By focusing on the business problem and outcomes you want, the answers will become clear.
JumpStart Your GenAI Initiative by Putting a Solid Data Foundation in Place
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.
Artificial intelligence (AI) is revolutionizing the retail sector, giving businesses greater access to valuable customer data and market insights than ever before. Implementing AI strategies can be highly advantageous for brands looking to meet changing consumer demands and protect their bottom line.
About 40% of retail executives say their companies already use automated artificial intelligence technologies, and 80% say they plan to implement these strategies by 2025. Understanding how your business can utilize AI in retail is key to capitalizing on these solutions for long-term stability, enhanced operations, and profound growth.
Check out the primary use cases and advantages of artificial intelligence for the retail industry below.
8 Use Cases for Artificial Intelligence in Retail
As AI’s popularity and capabilities grow, so do the number of ways your brand can use these tools. Top AI use cases in retail include:
1. Inventory Management
Artificial intelligence can streamline inventory management operations by analyzing historical sales data and customer behavior to accurately predict demand. It can help your retail business maintain optimized stock levels and anticipate trends.
AI-powered inventory management solutions can automate stock replenishment processes, preventing stockouts that frustrate consumers. They also help you avoid overstock situations, identify slow-moving items, and detect anomalies like sudden sales spikes. These insights enable your business to adapt quickly and ensure customers can get the products they want when they want them.
2. Personalized Marketing
AI is an incredible tool for supercharging retail marketing initiatives. These tools and strategies can help businesses deliver personalized campaigns to consumers. Targeted campaigns increase the likelihood of engagement and conversion.
They also enable predictive analytics based on customer data, preferences, and behavior to forecast trends and optimize marketing messages for the greatest return on investment. AI is particularly helpful for identifying the most relevant messages to share at the best time for particular marketing channels.
3. Product Recommendations
By analyzing customer browsing behavior and purchase histories, artificial intelligence can deliver personalized product recommendations that resonate with consumers and increase sales.
AI-powered strategies can automate upselling and cross-selling initiatives for retailers by suggesting complementary products at checkout. Paired with machine learning (ML), AI helps businesses continuously adapt and improve recommendations. They can use consumer feedback to refine the algorithm and increase the likelihood of conversion.
Retailers can also use AI to support personalized shopping experiences and assist consumers while they browse.
4. Automated Customer Service
Automated customer service is among the top examples of AI in retail. Generative artificial intelligence can revolutionize how your business interacts with customers, answers questions, and resolves purchase issues. You can use automated chatbots empowered by natural language processing and ML algorithms that understand and can reply to customer inquiries.
Automated, AI-powered chatbots on your website and social media can help consumers around the clock, reduce administrative burdens on your retail staff, and ultimately improve customer experiences.
5. Predictive Analytics
Retailers can also use artificial intelligence for predictive analytics to accurately forecast future trends, make data-driven decisions, and anticipate customer behavior.
You can train AI models using historical data to predict everything from demand planning to risk management. The AI algorithm can identify patterns and correlations within massive datasets, helping retailers uncover valuable insights to stay ahead of market changes and maintain a competitive edge.
Using predictive analytics insights enables retailers to enhance customer shopping experiences while protecting their bottom line.
6. Pricing Optimization
Artificial intelligence enables retail businesses to develop dynamic pricing models by analyzing various historical and real-time data regarding the following:
Competitor prices
Purchase histories
Market dynamics
Current demand
Inventory levels
Seasonality
Retailers can use AI to maximize profitability and revenue, ensuring you price your products competitively in today’s fast-paced market. Dynamic pricing algorithms utilize machine learning and AI to automate price changes for your business, saving your team time and energy.
7. Supply Chain Optimization
Retailers can also use artificial intelligence to improve supply chain management. After years of supply chain complications and delays, retailers began using AI for real-time monitoring and predictive analytics to support resilience.
AI-powered tools can enhance visibility into logistics operations. This helps businesses choose optimized delivery routes, streamline warehouse processes, and improve supplier relationships. Automated AI solutions and machine learning also allow retailers to achieve:
Increased efficiency.
Lower operating costs.
Minimized human errors.
8. Foot Traffic Analysis
Foot traffic analysis is an advantageous way to use AI in retail stores. Businesses can use AI and machine learning algorithms to track customer movement through a retail space and use that data to optimize store layouts and enhance operational efficiency.
AI solutions can analyze traffic patterns, popular areas, and dwell times to determine what products attract customer attention. Foot traffic analysis data can inform staffing levels and product placements to support better overall shopping experiences.
6 Key Benefits of Using Artificial Intelligence in the Retail Industry
AI can streamline and enhance many elements of running a successful store online and offline. The following are six key benefits of leveraging AI for shopping and retail:
1. Operational Efficiency
Artificial intelligence strategies can significantly improve your retail business’s operational efficiency by automating various time-consuming processes, from inventory management to customer service to supply chain administration. Your business can use different tools and solutions to streamline daily tasks and create smoother processes, making planning and scaling your operation easier.
AI reduces the strain on your team members, giving them more time and energy to enhance your business and help your customers.
2. Reduced Labor Requirements
A huge advantage of introducing artificial intelligence into your retail business is reducing the labor requirements to keep your store running effectively. You can save significant costs and lessen the administrative strain on your team. AI decreases human error, saving your employees and managers time and stress caused by missteps like ordering too much inventory of a particular product.
AI-powered customer service is particularly helpful for your retail team, helping them reduce the time spent answering repetitive inquiries and dealing with basic customer requests. You can rely on chatbots to ease administrative burdens and reduce the number of customer service individuals you need on the job.
3. Enhanced Customer Experiences
AI strategies make it easier for your business to deliver better customer experiences. Automated customer service is particularly helpful for catering to consumer needs and offering them greater convenience. With AI-powered chatbots, your customers can get answers to their questions around the clock. Whether they need to ask about a product or service at midnight or noon, retailers can use artificial intelligence to offer assistance and resolve their inquiries as fast as possible.
4. Increased Profitability
AI and machine learning in retail can help your business become more profitable. In fact, 72% of retailers credit AI for a decrease in operating costs, and 69% attribute an increase in annual revenue to these tools.
Through boosted efficiency and automation, AI enables businesses to boost operational efficiency, productivity, and turnaround times. Retailers can use these solutions to build positive customer relationships, driving increased loyalty, retention, and sales.
5. Personalization
Today’s consumers want personalization and the increased convenience of tailored shopping experiences. Artificial intelligence is invaluable for delivering personalized communications and product recommendations to drive customer engagement and make your shoppers feel special.
AI-powered personalized experiences enable your retail business to cultivate more meaningful and profitable customer relationships. They can drive customer retention, increase conversions, and make shopping at your stores more enjoyable.
6. Competitive Advantage
Artificial intelligence gives retailers a significant advantage over businesses that do not use these solutions. Automated AI enables faster and more accurate processing, analysis, and decision-making. Your business can use AI to quickly identify risks, resolve customer issues, and supercharge inventory management for better operations overall.
Navigating the Challenges of AI in the Retail Industry
While artificial intelligence offers many advantages to retailers, a few hurdles can complicate the integration process:
Privacy and Security
Retailers must prioritize robust data security measures when implementing artificial intelligence solutions. Today’s consumers want to know how you use their personal information and that it is secure. By following strict data protection rules, your business can mitigate the risk of data leaks and breaches.
Different privacy approaches, such as strong access controls and encryption, help retailers overcome these challenges. Through transparency about your AI-related data processes, you can also instill greater trust in your customers.
Data Quality and Integration
Another common pitfall of introducing artificial intelligence into your retail operations is ensuring data quality through smooth integration with other data-collecting systems.
AI models need accurate, consistent data to deliver the most reliable insights to your business. Your AI system likely collects data from multiple sources, and improper standardization practices can lead to disparities, inconsistencies, and errors. Data quality is essential for gaining the most useful and correct insights for important decision-making.
AI Knowledge Gaps
Artificial intelligence technology is new for many individuals. Your team members may not fully understand AI’s key abilities and uses, and some people have misconceptions about these solutions. The knowledge gap can hold your business back.
To capitalize on the many benefits of AI, your retail employees may need training and educational opportunities to help them learn how to responsibly utilize these tools and how they can benefit their daily processes.
Ethical Considerations
AI strategies are not perfect, and you must be conscious of the potential biases in your machine learning algorithm. If bias exists in the training dataset, your outcomes may be unethical and negatively impact certain communities. Ongoing monitoring, refining, and evaluation of your artificial intelligence systems is essential for mitigating bias and ensuring these tools’ most fair and ethical uses for decision-making.
Customer Acceptance
Some consumers are apprehensive about the use of artificial intelligence. Sharing how your business leverages AI and taking full responsibility for its outcomes is essential for instilling greater confidence among your shoppers.
The more your business shares regarding its AI practices, the more consumers will accept it and trust that you are using their data responsibly.
Best Practices for Implementing AI for Retailers
Successful AI implementation requires careful planning. The following are best practices to consider for seamless retail execution:
Define Your AI Objectives
Begin by outlining your business’s goals relating to AI. You may want these solutions to optimize operations, increase sales, or improve customer experiences. Whatever your top objectives are, identify key performance indicators to help measure and track your success. Consider your overall business goals and how AI can support your overarching purpose. These goals should be realistic and achievable, especially at the beginning.
Start Small
The best approach to introducing artificial intelligence into your retail business is to start on a small scale, focusing on specific use cases. Your team can use this opportunity to learn from initial implementations and gather feedback to inform decision-making. It is best to gradually expand your AI capabilities rather than do a massive technological overhaul, which can frustrate your team and customers.
Prioritize Data Quality
Your data needs to be accurate and relevant to support the success of your artificial intelligence initiatives. Prioritize high-quality data by investing in data cleaning, standardization, and enrichment processes. Proper data governance practices will also help your retail business maintain data quality over time, ensuring your insights are valid and useful for informing important decision-making.
Educate Your Team
Providing your team with adequate training, resources, and educational opportunities regarding AI technologies and concepts is key to the most successful implementation possible for retail businesses. Your employees must understand how AI will impact their roles and responsibilities and how to implement your solutions to deliver the best customer experiences. Share your business’s AI objectives, and be transparent about AI’s uses in your business.
Select AI Solutions Carefully
Choose AI solutions for retail with integration, compatibility, scalability, and integration in mind. The systems you work with need to align with your objectives and address specific opportunities for your business. Not all artificial intelligence tools are created equally, so selecting those with robust security and retail-specific capabilities will contribute to smoother, more streamlined operations.
Partner With an AI Expert
Retailers can enjoy a significantly easier AI integration process by working with AI experts like the team at Kopius.
We’ll help you JumpStart your success by delivering cutting-edge AI, ML, and retail technology. We understand the best practices for these solutions to enhance your business’s growth and meet your top goals. We’ll assist you in fostering tech-enabled innovation to boost digital and in-store experiences for your customers. With our help, you’ll be lightyears ahead of the competition.
JumpStart Your Retail AI Journey With Kopius
Kopius helps retail businesses use artificial intelligence and machine learning to supercharge their future. We created a program to JumpStart your data, technology, and customer success. We’ll help you take your customer shopping experiences and personalized marketing to the next level while supporting advanced data security.
With our JumpStart Retail partnership program, Kopius fast-tracks business results and enhances platform solutions. Our approach is user-centric and tailored to your unique business requirements. We will help identify your operational, customer, and team needs, ensuring your technologies align with your top priorities.
Today’s world is more interconnected than ever. Trying to succeed in a globally connected space means every little edge makes a difference. Data runs at the heart of innovation — how can you tweak things to make efficiency that much higher and your team even slightly more productive? With data giving you accurate, fast information, your company can truly thrive.
Building a data-driven culture can transform your organization, allowing you to make better decisions, boost efficiency, and support innovation. If you’re interested in fostering a data-driven culture, you need to understand data and how to use it.
What Is a Data-Driven Culture?
The meaning of a “data-driven culture” is a business that backs its decisions with data. Data analysis and interpretation are the foundation for decision-making, whether through daily tasks or long-term strategies. While intuition can be helpful sometimes, data-driven cultures use concrete stats to give them a solid understanding of how their company is doing and where it needs to go.
Today, data-driven cultures are critical for modern business success. Using all available resources maximizes your knowledge and gives you more resources to advance. With data informing your decisions, your team can make more informed moves, identify trends, predict customer behavior, and streamline processes. A data-driven culture is important for creating efficient, targeted cultures, letting you improve productivity and see results.
Characteristics of a Data-Driven Culture
Understanding what makes up a data-driven culture can help you craft your approach and ensure success. A data-driven culture should have:
Data accessibility: To make data useful, it needs to be accessible. Your team needs to eliminate data silos and ensure each department gets the relevant information it needs for decision-making.
Data accuracy: Your decisions are only as good as your data. A company’s data needs to be of good quality, up to date, and accurate. Regular checks protect data quality to ensure you make decisions based on good data.
Data transparency: Decisions based on data should have a clear logic. If you hide the data you use internally, how can your company trust the decisions different teams make? Transparency around the data you use for your decisions encourages trust and collaboration.
Data development: Finally, since data is always changing, your team should constantly be growing in their approach to data. Encourage your team to become more data-literate and keep your organization on top of new data practices. You’ll boost data security and your team’s ability to adapt to changing trends.
Some examples of data-driven culture are Google and Amazon. Google is constantly collecting data on search engine users to tailor results to each user’s search goals, creating a more accurate, seamless experience. Amazon takes data from customers’ buying history and browsing habits to personalize their shopping recommendations, driving more sales.
The Benefits of a Data-Driven Culture
Building a data-driven culture can transform your company’s success. Is a department falling behind? Are customers responding to a product or service well? Where are the gaps in your company’s approach? Data can help you answer these questions.
Once you have the information at your fingertips, you can use it to transform your success. Data-driven culture benefits include:
Enhanced decision-making: Take the guesswork out of your decision-making with accurate data. Data gives you clear insights into customer behavior, market trends, and internal operations. This information lets you make more informed decisions about your next moves.
Improved operational efficiency: A data-driven approach can also improve your operational efficiency. Analyzing data shows you bottlenecks, process inefficiencies, and poor resource allocation. You can streamline these areas to save time and money, creating more value.
Competitive advantage: Finally, data-driven approaches give you a competitive advantage. Data gives you accurate information faster. This means you can anticipate customer needs, innovate, and respond to market shifts more quickly than the competition.
How to Build a Data-Driven Culture
With new technology and strategies, you can maximize your data use to create an effective data-driven company culture. Use these steps to help you build a framework that works for your team.
1. Leadership Buy-In
Creating a new culture starts with the leadership. Leadership buy-in is critical because it sets the tone for the rest of your journey. Executive management can lead the charge with decisions made with the support of transparent data. When you start using your data at the top, it sends a clear message to the company that this is a valuable asset for everyone.
Encourage leadership buy-in by clearly laying out the benefits of a data-driven organization. Showing the real results of data can convince leadership to come on board fully — use case studies from successful companies to demonstrate its value and the competitive advantage it delivers. Get leadership to set clear goals that have measurable objectives. With progress tracking for leadership goals, leaders show data’s value and encourage everyone else to follow.
2. Data-Driven Culture Framework
Once you have the leadership behind you, you need to set up a framework. Having a clear path to success is motivating and keeps everyone on track. Here’s a rough framework you can use as a jumping-off point:
Assessment: First, assess your company’s current data usage. What are your existing data systems? Where are your data gaps? How do you currently use data to make your decisions? Once you know what you’re working with, you can find improvement areas and set steps to address them.
Data policies: Set policies for collecting, storing, managing, and sharing data. Make sure you’re following relevant data protection regulations. Protecting data quality and security ensures you use accurate data while maintaining trust.
Training: All employees should receive training on working with data. Offer training programs, ongoing learning, and workshops to improve data literacy. The more your team knows about using data, the more effectively they can wield it to meet your goals.
Collaboration: Make sure your departments can always access the data they need. Avoid data silos and encourage cross-functional teams to get better insights into data. Regular communication keeps everyone on the same page, ensuring successes and challenges are addressed effectively.
3. Tools for Data-Driven Culture
You need more than good planning for a successful culture transformation. The right tools enable you to collect and analyze data, creating an effective strategy efficiently. Software and platforms that can help you out include data analytics tools, business intelligence (BI) platforms, data visualization software, machine learning (ML), and artificial intelligence.
Analytics tools allow teams to analyze large datasets quickly, getting actionable insights. BI platforms let you easily visualize data so it’s more understandable for your team.
4. Performance Metrics
Key performance indicators (KPIs) tell you whether your data-driven approach is effective. KPIs provide you with concrete measurements, helping you meet your goals.
Identify your KPIs — include metrics like data-driven decisions, customer satisfaction scores, revenue growth, or operational efficiency. You want to choose KPIs that align with your business objectives while clearly showing how your organization uses data.
Once you have your KPIs, you need to keep monitoring them. Set up dashboards with real-time data updates and schedule reviews. Regular monitoring allows you to stay on top of your progress and adjust as needed to keep on track with your goals. You’ll make timely adjustments to your strategies and avoid being surprised by the data when it’s too late.
5. Data-Driven Decision-Making
All of this data cultivation has been in service of data-driven decision-making. Your culture should revolve around how data influences your decisions. Start this step by incorporating data into all aspects of your business. Encourage teams to explore data before they enter a new market, launch a product, or optimize internal processes. Push everyone to consult data where necessary for better decision-making.
Use statistical analysis, predictive modeling, and ML to gain deeper data insights. These tools break your data down into patterns, showing you the gaps in your strategy. With clear and actionable insights from your data, you can really start implementing effective decision-making.
6. User Adoption
Employees have to engage with data to make successful, data-driven decisions. The easier it is for your team to use and access data, the more they’ll use it. Make your data tools easily accessible and user-friendly.
Training and development are essential for user adoption. Invest in regular training sessions to ensure everyone is on the same page about your tools. These sessions will help your team understand how to use the tools and make data-driven decisions. Encourage everyone to keep learning. Support ongoing development — the more up to date your team is, the more effective and competitive their work will be.
7. Fostering Continued Improvement
Instilling a data-driven culture is all about using data to continually improve your company. Extend this philosophy to your entire team. Set up feedback measures so you can regularly assess and adjust your approach, and ask employees about the tools and processes they use. Collecting feedback lets you know what’s working and where you need improvement.
Open communication is essential for this step. Encourage employees to share their experiences with data tools and strategies. Let them know you value their input and want to hear more. Focus groups, regular surveys, and informal discussions can help you get to the heart of your approach. Additionally, review, test, and improve your data strategies for better results. Use an ongoing dialogue to keep your company responsive and on the cutting edge.
8. Partnering With Experts
Building a data-driven culture within your company is challenging. Accessing improved decision-making and gaining a competitive edge are excellent benefits. However, achieving this shift might be outside your organization’s current capabilities. This is where partnering with experts is invaluable.
Working with experts allows your company to tap into specialized knowledge and experience. If you want a smooth transition into a data-driven culture, you should lean on trusted support resources. Experts can look at your company and point you toward the right tools for the job. Additionally, they can create tailored digital strategies and best practices that align with your goals. Leveraging their industry knowledge takes you over common challenges straight to success without the learning curve.
Why Choose Kopius?
Kopius is your solution to navigating data-driven transformations. With a combination of digital strategy, design, and engineering expertise, we unlock growth across the customer experience. Our end-to-end capabilities support every aspect of your shift to a data-driven culture. Use our ideation workshops and digital product development to take data and innovation to the next level.
With team members in the United States and Latin America operating in your time zone, Kopius simplifies getting the right solutions. Our approach combines high-quality, reliable solutions with efficiency and speed. These qualities keep your company on pace with changing technological advancements.
Whether you need project-based teams, embedded delivery, or managed services, Kopius can help. With a 92% client retention rate and a team of over 600 specialists, Kopius is dedicated to helping your business thrive.
JumpStart Your Data-Driven Culture With Kopius
If you want to stay competitive in today’s business landscape, you need to invest in a data-driven culture. Getting started on your own might delay your productivity, setting you back before you start seeing success. With Kopius, you can skip the challenge of shifting cultures and get expert support for your new strategy. As experts in digital strategy, design, and engineering, we provide the guidance you need to unlock your data’s full potential.
We understand that every business is unique. That’s why we offer tailored solutions to align with your specific goals. Whether you need help with data planning, architecture, engineering, or advanced analytics, our team delivers results. By partnering with us, you gain access to cutting-edge tools and strategies to turn your data into a powerful asset.
Our track record and nearshore delivery model ensure your transition is smooth. Let our expert team members give you the support you need to sustain a data-driven culture. With our help, you can experience enhanced operational efficiency, better decision-making, and a competitive edge.
Don’t let the complexities of data management hold you back. Let Kopius’ data strategy services set you on the path to long-term success. Contact us to start your digital transformation today! Together, we can build a future where data empowers every part of your organization.
The data lake market will generate revenues of more than $86 billion by 2032, driven in part by IoT-dependent verticals like manufacturing, healthcare, and retail, according to Polaris Market Research. A data lake is a centralized repository for storing all your raw data, regardless of source, so you can combine it, visualize it, and even query it. It is essential for any organization wanting to take advantage of generative AI, now or in the future. But if you’re planning to implement one—or just want to get your data out of silos and into the cloud—it’s important to get it right.
At Kopius, we have helped a fair number of companies whose first try at setting up a data lake didn’t yield the kind of results they were looking for to get projects back on track. Given that risk, it’s no surprise that we also talk with companies that are wondering if it’s worth it.
The answer is a resounding yes.
When properly implemented, putting your data in a data lake or similar environment will enable you to better meet customer needs, solve business problems, get products to market faster, more closely manage your supply chain, and even unearth insights about your business that a human might not even be able to see.
And setting one up doesn’t have to be a long, arduous journey—think of it as more of a quick trip.
Data Lakes Deliver Strategic Advantages: Insights at Speed
Whether you’re moving your data from an internal SQL or other server to Cloud or already have your data in one or more disparate cloud applications, putting all that raw data into a data lake has strategic advantages. Chief among them, and the one that is top of mind for many organizations, is that it is an essential first step in preparing yourself to take advantage of generative AI, (GenAI), which requires raw data in a modern environment.
Another big advantage of data lakes is simply speed. Once all your data is in a data lake, you can set up pipelines to ingest and structure it. You don’t have to go through the tedious process of standardizing or normalizing it to build dashboards or reports or do whatever you need to do. It’s a much faster process than your old SQL server or whatever solution you’re using now. For example, a global healthcare consulting and services company we worked with spent months coding a pipeline to ingest data they needed for a process that took three to four hours to run. Once we implemented their data lake, we set up a couple of pipelines in just two weeks to support the activities they were already doing, with processing time of just minutes.
As fast as businesses move today, all that speed gives you a competitive advantage.
A Data Lake Isn’t the Endgame
Implementing a data lake isn’t the end game—it’s a starting point. It’s just one of several critical components in an overarching, long-term data strategy. Data must be structured—formatted so you can visualize it, query it, or do whatever it is you need to do. So even though it’s somewhat straightforward to stand up a proof of concept, it’s important to know what your endgame is. You’ll need to have some big picture idea of how you want to use your data, because that informs what solution set is best for you. And the market for solutions is both somewhat nascent and already very complex.Fortunately, at Kopius, we have a process for walking you through all these important considerations to find a point of departure or move you further along the data maturity path, including helping you narrow down what your endgame is. It’s designed to get you on the right path up front, so you get the outcomes you are looking for.
Jumpstart Your Data Lake Initiative with a Proof of Concept
At Kopius, we harness the power of people, data, and emerging technologies to build innovative data lake 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.
One of the earliest known instances of IoT being used for remote monitoring took place at Carnegie Mellon University in 1982. A Coke® vending machine was connected to ARPANET, a forerunner of the Internet, to keep track of how cold the sodas were and whether the machine was full. This notion of collecting data from an end point and sending it to a network to monitor a device is as valid a use case now as it was then. In fact, it’s at the heart of an initiative we work on with the Gates Ventures—monitoring fixed and mobile refrigeration units in remote locations to ensure the vaccines stored in them remain safe to use.
Today, however, IoT is much more than this.
IoT has grown into a massive industry that spans virtually every vertical. In fact, McKinsey estimates that by 2030, the economic potential of IoT could rise as high as $12.5 trillion globally. It plays a critical role in industries as broad ranging as energy management, healthcare, agriculture, and retail. But IoT is only one piece of a much larger data strategy. It’s no longer just about monitoring endpoints and measuring activities. It’s about gaining insight, solving business problems, and driving better outcomes.
To move from monitoring endpoints to driving outcomes, you must establish a solid, overarching data strategy around all your relevant data sources, including IoT devices. You will need to explore how to combine data from disparate sources, determine where to store it, for instance in a data lake, decide how best to structure it to make it queryable, and address how to visualize it. You will also need to put appropriate data governance and security policies in place.
These decisions will serve as the bedrock on which your ability to use and manage your data in the future will be built. To ensure that it is a strategy that supports long-term business agility, you would be well served by collaborating with an external partner with deep domain expertise in structuring and visualizing data who can help you explore challenges your organization is likely to encounter in the future and how will need to access and use its data to address them.
Developing a data strategy and structuring your data, is not a one-and-done project. It is a journey—you will need to put processes in place to manage your assets over time. Where you are on that journey, essentially your level of data maturity, determines your point of departure. For instance, organizations taking early steps may need a business intelligence solution like PowerBI, while others that are farther along may be ready for a data lake or solution like Snowflake that leverages a type of SQL database better suited to support AI applications.
The Impact of AI on IoT
Looking down the innovation curve, AI in general, and generative AI (GenAI) in particular, will have a significant impact on the IoT ecosystem. AI will enable edge computing, which means data can be stored and used much closer to the actual IoT device, whether it is a component of a smart grid or a robot, so it can autonomously respond in real time to changing environmental conditions. Further, by layering AI and GenAI applications on top of your structured data, you will be able to use natural language to query it to find insights that humans would not ordinarily be able to see.
Increasingly today, and well into the future, IoT, Data, and AI will serve as three-legged stool that organizations can build on to gain deep insight into their business, make data-informed decisions, and ultimately drive better outcomes.
And virtually every industry can benefit from it.
What’s the Next Step on Your IoT, Data, and AI Journey?
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.
Manufacturing has long been an industry of innovation. Smart factories are the industry’s next leap, with the market expected to reach $321.98 billion by 2032. By applying artificial intelligence (AI), intelligent automation, and machine learning, smart factories can take your business to new heights, increase productivity, reduce costs, and improve overall efficiency. Learn more about smart factories and the technologies they use to optimize manufacturing processes.
What Is a Smart Factory?
Smart factories are the modern interpretation of the factory environment. They improve manufacturing processes through the use of interconnected networks of machines, communication mechanisms, and computing power. Key features include:
Interconnectivity: Machines, devices, and systems share data and communicate with each other.
Automation: Robotics, AI, and Internet of Things (IoT) technologies work together to automate processes and reduce manual intervention.
Data analytics: Real-time monitoring and data analytics predict equipment failures and allow better decision-making.
Flexibility: Technologies offer quick adaptability to changes in demand or production requirements.
Quality control: Advanced sensors and monitoring systems ensure consistent product quality.
Smart factories analyze data, drive intelligent automation, and learn as they go, allowing for greater efficiency and quality control in manufacturing plants.
How Do Smart Factories Work?
While automation and robotics have been used in manufacturing for decades, the smart factory was introduced to integrate these machines, people, and data into one interconnected system. Ultimately, a smart factory teaches itself and humans to be more adaptable, efficient, and safe through the use of technologies like:
Artificial intelligence: Smart factories integrated with AI have more power, speed, and flexibility to gather and analyze disparate sets, and offer real-time insights and recommendations. AI essentially powers automation and intelligence within smart factories, helping them continually optimize manufacturing processes.
Machine learning: Machine learning offers predictive maintenance capabilities in smart factories. The system monitors and analyzes processes, sending alerts before system failures occur. This way, you can make necessary repairs to prevent costly downtime, or the system will automate maintenance, depending on the situation.
Internet of Things: IoT connects the various devices and machines in a manufacturing plant, where they exchange data to automate actions and workflows. The interconnectedness can promote better resiliency and safety in your processes.
Benefits of Smart Factories
Smart factories can transform your manufacturing processes, unlocking numerous opportunities for automation, efficiency, cost savings, and safety:
Improve Efficiency
Smart factories use robotics and automated systems to boost productivity. By monitoring processes and identifying bottlenecks in real-time, these technologies can point out ways to reduce inefficiencies and streamline workflows. The system’s use of sensors and AI can also predict maintenance needs, reduce human error, and monitor product quality to ensure consistent output.
Reduce Operational Costs
Smart factories help reduce operational costs in many ways. Predictive maintenance allows you to make timely repairs, which can prevent costly downtime and extend the life span of machinery. Additionally, real-time data analytics can track your inventory levels to minimize excess stock and storage costs.
Greater efficiency, consistent quality, and responsiveness can ultimately lead to customer loyalty and increased market share.
Enhance Workplace Safety
Smart factories identify ways to keep your workplace safe. IoT sensors continuously monitor equipment, worker activities, and external conditions, detecting potential safety hazards promptly. They may also trigger automatic alerts in case of emergencies like gas leaks or fires, giving you time to act and prevent accidents or injuries.
Manufacturing With Smart Factory Solutions
By applying various forms of digital technology like AI and intelligent automation, smart factories can highlight inefficiencies and make manufacturing processes much smoother. You might apply the following smart manufacturing solutions across the different stages of your operations:
1. Intelligent Automation
Intelligent automation refers to the use of AI and machine learning solutions to automate tasks. When AI becomes part of smart factories, machines can learn, adapt, and make decisions without human intervention. For example, you can use intelligent automation on assembly lines to schedule maintenance and prevent downtime. Software bots can pinpoint the source of issues and notify engineers to fix them quickly to get operations up and running.
Intelligent automation solutions can also optimize demand forecasting, inventory management, and logistics to reduce costs and streamline operations.
2. IoT
IoT devices, like sensors, actuators, and radio frequency identification (RFID) tags, can be used in manufacturing plants to collect real-time data on equipment performance, environmental conditions, supply chain logistics, and product quality. These devices then transmit data to a central system, where AI and machine learning analyze it.
For example, you might integrate IoT automation through a smart inventory management system. In this scenario, you place IoT sensors on inventory shelves and storage areas to monitor stock levels in real-time. The sensors collect data on quantities, movement patterns, and expiration dates. When inventory reaches a certain threshold, they automatically reorder supplies to improve efficiency.
3. Machine Learning
Machine learning technology can optimize manufacturing processes in various ways. For instance, it can forecast the energy usage of equipment, allowing you to meet resource demands or limit energy consumption. Machine learning can also improve health and safety in smart factories. For example, you might use IoT sensors to measure air quality and noise levels.
Machine learning algorithms can use information from IoT sensors to identify when workers are exposed to high levels of pollutants or excessive noise. Once detected, the sensors can send out alerts and recommendations to help workers avoid safety risks.
4. Cobots
In smart factories, AI can be used to power collaborative robots, or cobots, to work alongside humans. This recent innovation can promote safety in smart factories, as they work through features like sensors and computer vision to halt operation at the detection of danger.
Cobots also allow for human-machine interaction without barriers, supplementing physical work with machine efficiencies. While the development of cobots is still ongoing, they are already being used in manufacturing factories, including Amazon, which has used cobots since 2012 to help with stock picking in their warehouses.
JumpStart Your Smart Factory Journey
Smart factories present numerous opportunities for growth. By using smart manufacturing technology, such as IoT and automation, you can promote greater efficiency, productivity, and safety in your operations. However, a lack of expertise and internal resources are among the numerous obstacles that can prevent businesses from successfully implementing these technologies. Kopius’s JumpStart program can help you unlock the full potential of smart factories and drive success.
Our experts offer end-to-end solutions, assisting in every stage of the implementation process, from planning to delivery. We can also help you manage the daily operations and infrastructure of your software, keeping things running smoothly. Digital Possibilities Delivered. Contact us today to leverage manufacturing solutions for your business.
A large language model (LLM) is a deep learning algorithm pre-trained on massive amounts of data. LLMs use transformer models — a set of neural networks that includes an encoder and decoder with self-attention capabilities. Essentially, the encoder and decoder identify meanings from text and understand the relationships between the words and phrases in it.
This article provides an overview of LLMs, including how they work, their applications, and future innovations. It also highlights the advantages of implementing LLMs for your business and how to use them for success.
Large Language Models Explained
Large language models are foundational models that use natural language processing and machine learning models to generate text. Natural language processing is a branch of artificial intelligence (AI) concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.
By combining computational linguistics with statistical machine learning and deep learning models, LLMs can process human language in the form of voice data or text to understand its whole meaning, including user intent and sentiment.
There are different types of large language models, such as:
Generic or raw language models: Trained to predict the next word based on the language in the training data, typically used to perform information retrieval tasks.
Instruction-tuned language models: Trained to predict responses to the instructions given in the input, allowing them to perform sentiment analysis or generate code or text.
Dialog-tuned language models: Trained to have a dialogue by predicting future responses. Examples include chatbots and virtual assistants.
The goal of LLMs is to predict the text likely to come next. LLMs are pre-trained on vast amounts of data to understand the complexities and linkages of language. The sophistication and performance of an LLM can be judged by the number of parameters it has — or the factors it considers when generating output.
Generative AI vs. Large Language Models
Generative AI is an umbrella term that refers to AI models capable of generating content. LLMs are a specific category of generative AI models with a specialized focus on text-based data. Essentially, all large language models are generative AI. The main differences between generative AI vs. LLMs include:
Training: Generative AI undergoes extensive training on large datasets to connect patterns and relationships present within that data. Once trained, they can generate new content that aligns with the characteristics of the training data. In contrast, LLMs are trained on vast volumes of text data, from books and articles to code. After training, LLMs can complete text-related tasks.
Scope: While generative AI uses many models to create new content beyond textual data, LLMs excel at understanding language patterns to predict and generate text accurately.
Type of content: As mentioned, generative AI creates images, music code, and other content beyond text. They are a good fit for creative fields like music, art, and content creation. LLMs are best suited for text-based tasks and applications like chatbots, language translation, and content summarization.
When used together, generative AI and LLMs can enhance various applications like content personalization, storytelling, and content generation. For example, a generative AI model trained on artwork datasets could be improved by LLMs trained on art history by generating descriptions and analyses of artwork. A business could use that combination to create marketing images and phrasing that improves user intent, ultimately helping boost sales.
How Do Large Language Models Work?
A transformer model is the most common basis for a large language model, consisting of an encoder and a decoder. The transformer model processes data by tokenizing the input and conducting mathematical equations to discover the relationships between the tokens, or words. This process allows the computer to see patterns a human would if given the same query.
Before working from a transformer model, LLMs must undergo training to ensure they can fulfill general functions, and fine-tune their skills to perform specific tasks. Large language models are often trained on massive textual datasets like Wikipedia, containing trillions of words.
During training, the LLM engages in unsupervised learning, which is processing datasets given to it without specific instructions. This stage allows the LLM’s AI algorithm to decipher the meaning of words and the relationships between words. It also learns to distinguish words based on context. For example, it would learn whether “right” means “correct” or the opposite of “left.”
Key Components of LLMs
Large language models consist of several neural network layers — recurrent, embedding, attention, and feedforward layers — that work together to process input text and generate output content. Here’s how these components work:
Embedding layer: The embedding layer consists of vectors representing words in a way the machine learning model can quickly process. This part of the large language model is working on dissecting the meaning and context of the input.
Feedforward layer: The feedforward layer consists of various connected layers that transform the input embeddings. This allows the model to glean higher-level abstractions or understand the user’s intent with the text input.
Recurrent layer: The recurrent layer analyzes each word in a sequence provided in the input, capturing the relationship between words in a sentence.
Attention mechanism: The attention mechanism enables the language model to focus on single parts of the input text that are relevant to the task at hand. This layer allows the model to generate the most accurate outputs.
Business Applications of Large Language Models
Large language models have numerous applications in business environments. Key examples include:
1. Content Creation
LLMs can help generate valuable content spanning many formats, from articles and blog posts to product descriptions and social media posts — saving your company plenty of time and resources. As writing assistants, large language models can also provide real-time grammar, spelling, and phrasing suggestions.
Further, language models can help your company generate fresh outlines by analyzing existing content and trending topics, helping you develop relevant content that resonates with your target group.
Suggesting relevant keywords to enhance visibility in search results
Identifying common search queries to tailor your content to match user intent
Helping structure content to improve ranking in search results
Conducting SEO audits to analyze your website’s speed and areas for improvements
Using LLMs’ recommendations about SEO strategy can help improve user engagement and improve your site’s visibility.
3. Customer Service
Large language models can help improve the customer service experience by automating various interactions. For instance, chatbots can respond to customer inquiries, help with troubleshooting, and provide relevant information 24/7. Additionally, virtual sales assistants can engage with customers, answer product questions, and guide them through the sales process.
4. Virtual Collaboration
You can also use LLMs to enhance staff productivity and effectiveness. The AI tool can help facilitate collaboration and streamline routine tasks. Examples of functions LLMs can perform include:
Generate meeting summaries and transcriptions
Provide real-time translations for multilingual teams
Facilitate knowledge sharing
Document company and project-related processes
Assist team members with disabilities, such as vision or hearing impairment
5. Sales
Large language models can also support sales professionals with various processes, including:
Lead identification: LLMs can identify potential leads by analyzing massive amounts of data to understand customer preferences. This can help your sales teams target high-quality leads with a higher likelihood of conversion.
AI-powered chatbots: AI chatbots can engage with website visitors, collect information, and provide teams with customer insights to generate more leads.
Personalized sales outreach: Using customer information and data, LLMs can help craft personalized sales outreach messages, such as customized emails and product recommendations.
Customer feedback analysis: AI strategies can also analyze customer feedback and pain points to help sales teams personalize their approach and build stronger relationships.
6. Fraud Detection
Large language models also offer fraud detection capabilities. They can analyze textual data, identify patterns, and detect issues to help your company fight against fraud. These AI strategies provide real-time monitoring, such as financial transactions or customer interaction. They can quickly identify suspicious patterns and generate real-time alerts to jumpstart an investigation.
Applications and Use Cases in Other Industries
With various applications, you can find uses for LLMs in several fields, such as:
Marketing and advertising: LLMs excel in generating high-quality content, making them a good fit for personalized marketing, chatbots, content creation, ad targeting, and measuring the effectiveness of marketing campaigns.
Retail and e-commerce: Large language models can analyze customer data to generate personalized recommendations for products and services. They can also help answer customer inquiries, assist in purchases, and detect fraud.
Health care: Large language models are being used in health care to improve medical diagnoses, patient monitoring, drug discovery, and virtual reality training. LLMs are revolutionizing the health care industry to improve patient satisfaction and health outcomes.
Science: LLMs can understand proteins, molecules, and DNA. They can potentially be used in the development of vaccines, finding cures, and improving preventative care medicines.
Tech: Large language models are widely used in the tech industry, from allowing search engines to respond to queries to assisting developers with writing code.
Finance: LLMs are used in finance to improve the efficiency, accuracy, and transparency of financial markets. They can complete risk assessment tasks, assist in trading and fraud detection, and help financial institutions comply with regulations.
Legal: These AI strategies have helped lawyers, paralegals, and legal staff search massive textual datasets and generate legal phrasing. LLMs can streamline tasks like research and document drafting to save time.
Benefits of Large Language Models
The benefits of rolling out large language models for your business include:
Deeper Levels of Comprehension
Unlike earlier chatbots and automated systems that relied on keyword matching and rigid scripts, LLMs can better understand the context, sentiment, and intent behind queries. This allows better customer-support chatbots, virtual assistants, and search engines. For example, in e-commerce, when an online shopper has a question for the online assistant, AI can dissect the question and reveal its context to provide a relevant and accurate response.
Saved Time
Large language models can produce almost anything text-related, from quick suggestions to lengthy essays. As a result, marketers, journalists, and even employees who aren’t tasked with writing are using LLMs to streamline their work and create professional content. This saved time and effort can be channeled into personalizing the content.
Enhanced Efficiency and Accuracy
Traditional methods of text processing and analysis methods can be daunting and prone to errors, especially when working with vast datasets. By contrast, with their deep-learning algorithms, LLMs can analyze data at unparalleled speeds, reducing or eliminating manual work altogether. For example, businesses can use LLMs to scour customer reviews, identify common issues and areas they’re doing well, and respond to customers quickly — saving a lot of time in the process.
Personalized Experiences
By collecting data and analyzing customer behavior and preferences, LLMs offer personally tailored recommendations and experiences. For instance, LLMs can work as product recommendation engines that suggest items to shoppers based on browsing and purchasing history. This increases the likelihood of conversions and a better customer experience.
Considerations When Implementing LLMs
While LLMs provide many advantages across business applications, they also come with a few considerations to note. This technology is still growing and changing, meaning companies will need to be aware of risks like:
Hallucinations or falsehoods generated as a result of poorly trained LLMs
Biases when the datasets aren’t diverse enough
Security issues, such as cybercriminals using the LLM for phishing and spamming
Challenges in scaling and maintaining LLMs
Using LLMs for Business Success
LLMs will continue developing and learning, offering various innovations for businesses. With improvements like better accuracy, audiovisual training, and enhanced performance of automated virtual assistants, you’ll want to get ahead of the competition and use AI to transform your workplace. While it can be challenging to implement LLMs without technical expertise, the right consultants can guide your LLM strategy, ensuring it drives success for your company.
By considering your unique objectives and resources, the experts at Kopius can help you implement AI and machine learning (ML) solutions to empower your team and strengthen your company for the long term.
Our focus areas include:
Customer service automation
Data analytics and business intelligence
Process automation and optimization
AI and ML strategy development
Bias mitigation and fairness
Personalization and marketing automation
Churn prevention and customer retention
Supply chain optimization
Talent acquisition
At Kopius, our AI and ML solutions can transform inefficiencies in your company and improve your decision-making. When working with us, our experts will highlight the areas of your business that could grow the most with artificial intelligence and machine learning.
Explore LLM Opportunities With Kopius
LLMs can unlock exciting possibilities for your business, including streamlining tedious administrative tasks, generating fresh content, enhancing your marketing efforts, and personalizing the customer experience. When you’re ready to implement AI and machine learning for your business, Kopius is here to help with digital technology consulting services.
While it can be challenging to implement these strategies on your own, our consultants have the knowledge and expertise to help you get the most out of technology and drive real results. We consider your unique needs and goals to develop a plan that works best for you. To get started, contact us today.
Although in-person shopping has recovered well from the pandemic, many customers feel frustrated with the experience of visiting brick-and-mortar stores.
Unified retail analytics solutions, which combine in-store technology with integrated software and advanced algorithms, can help your store learn more about your customers’ common pain points and desires so you can better cater to them.
When your customers are freer than ever to abandon your business for a competitor, you need a solution that can help you make your store the best place to shop. Using data analytics for retail operations is the key to gaining an edge in this hyper-competitive sector.
What Is Retail Analytics?
Retail analytics is the science of collecting and analyzing business data to improve your business outcomes on an ongoing basis. Depending on which sales and marketing channels your company uses, your data sources might include:
In-store devices and sensors.
Online storefronts.
Catalog orders.
Social media platforms.
Once you’ve collected all the data you need, you’ll run analytics to create reports that reveal valuable insights into your store’s business. Several types of retail business analytics exist with varying levels of complexity:
Descriptive: Descriptive analytics provides the most basic level of data insights for your business, such as which locations carry certain products, how much of each item is on the floor, and how often store employees restock shelves. This data informs more sophisticated analytics programs.
Diagnostic: Running diagnostic analytics on your descriptive data helps you identify and understand the issues you’re facing in real time so you can find actionable solutions.
Predictive: Predictive analytics algorithms use AI to analyze your historical data and predict likely future events based on different variables, including evolving economic circumstances and supply chain disruptions.
Prescriptive: Similar to predictive analytics, prescriptive analytics uses AI algorithms to recommend solutions to the forecasts from the predictive analytics system.
The Role of Data Analytics in Retail
Historically, the retail industry has relied on intuition to make decisions about inventory, store layout, customer communications, and more.
Even the slightest adjustment can cause major consequences for inventory movement and profitability. For example, choosing to offer a 15% discount on a product over a 10% discount can cost you thousands in lost revenue — especially if you apply it to the wrong product.
Retail tech solutions eliminate the guesswork by using data and advanced digital technologies to enable more informed decision-making. Data-driven decisions can help you commit to continuous improvement and sustainable business growth.
That’s what we do at Kopius. Our team takes a collaborative approach to every project, working closely with your company’s best and brightest to design custom retail analytics solutions.
With the most accurate data at your fingertips, you can empower your team to streamline retail store operations, whether you’re focusing on an online storefront or a brick-and-mortar shop.
Key Benefits of In-Store Analytics
A well-designed retail store analytics solution helps boost business for retail companies of all sizes. Whether you’re a small local business or a multinational corporation, retail analytics can help you gain a competitive edge in the market by:
Enhancing operational efficiency: Retail store analytics makes a proactive approach to inventory management and customer service possible by alerting you to potential issues and recommending appropriate solutions before anything happens.
Boosting sales: Trends in the data you collect on customer preferences and in-store shopping behaviors help you identify ways to improve marketing, store layout, upselling, and cross-selling to bring in more sales.
Enhancing organizational visibility: Retail location analytics enables you to generate insights from all your physical stores and warehouses in real time, so you always have the most up-to-date information at your fingertips for data-backed decisions.
Improving customer experiences: When you start paying attention to customer preferences, it becomes easier to anticipate their needs and expectations. This enables you to deliver convenient, engaging shopping experiences that delight consumers.
Strengthening your supply chain: Retail analytics solutions are cloud-based, which enables them to scale as needed. This flexibility means you can monitor your entire supply chain for even more accurate decisions.
Remember that these solutions work best when they’re purpose-built for your company. A solution that claims to be “one-size-fits-all” usually leaves some gaps in data collection and analysis, which can cause you to miss important insights.
Emerging Applications for Data Analytics in the Retail Industry
Retail store analytics solutions have enormous potential for enhancing performance across all types of stores, including in-person and online. Here are some of the most promising ways businesses have leveraged retail analytics solutions to get ahead in the industry.
Enhancing Omnichannel and Multichannel Retail and Shopping Experiences
Omnichannel and multichannel strategies rely on accurate, up-to-date data to generate the results you’re looking for.
Although many sources treat omnichannel and multichannel experiences the same, they’re actually different strategies:
Multichannel: A multichannel strategy uses multiple channels, including websites, social media platforms, brick-and-mortar stores, and mobile apps, to facilitate customer interactions. Because each channel acts as a separate entity with its own goals, the customer experience can vary. This can help you appeal to a different segment of your customer base through each channel.
Omnichannel: An omnichannel strategy integrates all your sales and marketing channels to provide a seamless, consistent experience in every interaction. This approach allows you to combine the strengths of each channel for greater customer satisfaction.
For both types of experiences to work, you need a reliable source of customer data, which is where your in-store retail analytics technology comes in.
Integrated IoT sensors collect data on stock movement and customer shopping behaviors, and AI-powered analytics assess that data to identify trends that could help you improve your experiences.
For example, a customer might use your mobile app to put an item they like in their shopping cart, but they abandon their cart before checking out. If they visit your store, your analytics solution could serve them an ad featuring the product.
Leveraging Customer Behavior Insights for Business Growth
Understanding typical customer behaviors can reveal the rationale behind their purchasing decisions, which is important for effective marketing and sales.
Retail analytics solutions can help you uncover valuable insights into how your customers think, which can help you grow your business by:
Maximizing customer lifetime value: The more a customer purchases from your company, the higher their lifetime value and the more revenue you gain. Detailed customer insights can help you understand where to apply your focus to keep your most valuable customers satisfied.
Minimizing customer churn: Analyzing trends in customer behavior can predict when customers are most likely to abandon your brand, which reveals opportunities to improve the customer experience.
Improving your marketing results: Effective omnichannel and multichannel marketing strategies rely on customer data to keep audiences engaged with your brand. Collecting customer behavior data in physical stores and online channels provides a more comprehensive understanding of your customers that can boost results.
Identifying opportunities for expansion: If you’re considering expanding into other regions, accurate customer data can help you gauge which markets your business will most benefit from.
Personalization: The Key to Immersive Shopping Experiences
Personalized customer experiences are no longer just “nice to have” — they’re now a necessity for keeping your customers engaged in your business.
One study found that 81% of customers prefer to buy from companies that deliver personalized experiences. That goes for both e-commerce platforms and in-person shops.
Delivering relevant discounts and offers to individual customers’ phones based on their history with the brand.
Identifying previous interactions to identify and guide customers to the specific items they’re looking for.
Sending customized marketing messages via the customer’s preferred communication channels.
Recommending relevant products and deals to specific customers for upselling and cross-selling.
Using virtual reality (VR) and augmented reality (AR) technologies to allow customers to preview products in upcoming releases.
With all the choices customers now have for both in-person and online shopping experiences, it’s important that your store meets customer expectations and differentiates itself from all the other retail businesses in your sector.
Inventory Management and Sales Forecasting
Accurate sales forecasting is critical for ensuring you have the right amount of stock on hand. Too much and you end up with dead stock and lost revenue. Too little and you run into stockouts and frustrated customers.
A comprehensive retail analytics solution that integrates with an inventory management platform improves sales forecasting through predictive analytics. The platform can analyze historical trends to generate insights into what to expect over the next period, such as:
Identifying characteristics of products that sell well.
Predicting when you should expect higher demand than usual.
Making stocking recommendations for specific products.
Determining which sales channels to divert more inventory to.
You can even use this technology to ensure proper stocking on your sales floor. For example, you can predict which products will sell faster, which can help you determine exactly how many units to put on the shelves and the best intervals to restock.
Digital Technologies Driving Retail Analytics
In-store retail analytics solutions are complex, with both online and offline components:
The Internet of Things (IoT): The IoT is a network of internet-connected objects, such as sensors and mobile devices, that communicate wirelessly with each other and the cloud. This technology allows streamlined data collection and communication, which is essential for a unified retail analytics system.
Artificial intelligence (AI): AI is a branch of computer science that allows machines to “think” like humans. In terms of data analytics, an AI-equipped software program can identify patterns and draw conclusions much like a human data analyst.
Machine learning (ML): ML is an AI technique that analyzes previous outcomes and “learns” from them, incorporating feedback on an iterative basis. Essentially, ML enables your AI-powered software to continuously improve the predictions it makes so you can easily meet rising customer expectations.
Business intelligence (BI) solutions: BI platforms leverage AI and ML capabilities to analyze business data and generate actionable insights for effective, data-driven decision-making.
Point-of-sale (POS) systems: Your POS system is the point at which your business makes sales. This system can be a physical device you use to check customers out in-store or virtually, such as a payment form in your e-commerce channels.
Inventory management platforms: These systems allow your warehousing and logistics teams to track products as they move through the supply chain, which helps you fulfill online orders more efficiently while keeping your brick-and-mortar locations stocked appropriately.
Customer relationship management (CRM) systems: A CRM platform consolidates all your customer data into one centralized hub, making it easier for customer service reps and marketing teams to deliver personalized experiences and build brand loyalty.
Deep integrations between each piece of your retail analytics solution create a seamless data flow across every channel and tool in your system. This process ensures easy access and analysis. That’s why it’s so important to design a comprehensive system where each component is compatible — as long as they can work together, your solution should provide smooth, streamlined results.
The Future of Retail Analytics and Store Operations
Analytics has become essential for more efficient in-store operations in retail businesses across all sectors, and its importance will continue growing well into the future.
The evolution of retail analytics will follow advances in the technologies behind it, especially in AI, ML, and IoT. AI and ML have only become mainstream in the past couple of years, and IoT is still developing in many ways.
As more businesses begin implementing automated processes, these digital technologies will become more necessary in their day-to-day operations. Getting ahead of the curve and investing now can help your company become more competitive in the long run.
Getting Started With Retail Tech for In-Store Operations
Diving in headfirst without taking the time to plan your transformation is one of the biggest mistakes in starting out with retail tech.
Instead, it’s best to take a phased approach that accelerates results while empowering your front-line employees to focus on customer services. Start by identifying your most important goals and focus on achieving these first.
Consulting with a team of retail tech experts is one of the best ways to begin outlining your implementation strategy. The team at Kopius is uniquely qualified to help.
How Kopius Can Help
A team of experienced retail tech consultants can help you identify the right solutions for your business, designing a custom in-store retail analytics solution tailored to your unique requirements. That’s where the JumpStart program from Kopius, Inc. comes in.
The JumpStart program is our data-driven approach to the common pain points retail companies face daily. Our people meet with yours to discuss your needs and expectations, and we work closely with you to determine the right configuration of digital technologies for your goals.
Read our case studies to see how we’ve helped businesses like yours implement robust retail tech solutions and achieve excellent returns.
JumpStart Your Retail Operations With Help From Kopius
If your company is considering investing in retail analytics solutions, you can count on Kopius to help you create a comprehensive system that meets your store’s unique needs.
We designed our JumpStart program to spark innovation and drive customer, digital technology, and data success. By combining tech smarts and collaboration with a user-centric approach, we can help you fast-track business results and platform solutions.
Add our brainpower to your team and see where we can take you. Contact our specialists today to JumpStart your future success.
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