Across industries, artificial intelligence and machine learning are revolutionizing how work gets done by streamlining processes, enhancing productivity and driving innovation.
One of the most notable breakthroughs is the rise of generative AI, powered by large language models like GPT and multimodal systems.
These technologies can create human-like text, generate realistic images and even craft complex code, significantly reducing the time and effort needed for creative and technical workflows. Together with advanced ML technologies, GenAI has found applications across diverse fields, from automating customer service and personalizing marketing strategies to accelerating drug discovery and enhancing data analysis.
Companies like DRW, Strata Decision Technology, Medtelligent and EDGE are integrating these technologies into their workflows and product offerings, highlighting the utility for these technologies regardless of industry to enhance how work gets done and to pass those advancements on to customers through the product.
At DRW, AI and ML are transforming how teams collaborate and innovate. AI-powered chatbots equipped with internal knowledge bases streamline communication, enabling immediate access to information and improving cross-team collaboration. In addition to the chatbots, AI-driven code completion tools accelerate development cycles by automating repetitive coding tasks, allowing teams to focus on complex problem-solving and innovation.
Meanwhile, Strata Decision Technology uses GenAI during product development to accelerate processes like synthetic data creation, allowing teams to focus on developing new product solutions. With a robust AI governance framework and active exploration of emerging technologies, Strata ensures its AI strategies stay ahead of industry trends while maintaining data security and compliance.
Medtelligent focuses on improving senior living operations through AI and ML innovations. The company’s AI-powered early alert system analyzes data sets like clinical notes and medication records to proactively identify at-risk residents, enabling timely interventions. With weekly AI ideation sessions and a production pipeline designed for continuous model refinement, Medtelligent embodies adaptability and commitment to leveraging AI for better resident outcomes.
EDGE integrates AI and ML across its fintech analytics platform, using traditional ML for accurate, explainable insights and GenAI for enhanced data mining. By analyzing text and time-series data at scale, EDGE accelerates the discovery of actionable insights, allowing rapid product updates tailored to customer needs. EDGE’s focus on compliance and explainability ensures its innovations meet regulatory requirements, strengthening client trust.
Across these companies, AI and ML are not just tools but strategic advantages. Built In Chicago learned more about how each company has embedded advanced technologies into their operation to enhance efficiency, accelerate product development and drive meaningful results.
DRW uses advanced technology to identify and capture trading and investment opportunities globally.
How is your team integrating AI and ML into the product development process, and what specific improvements have you seen as a result?
Our team is proactively integrating AI and machine learning into various aspects of our product development process to enhance efficiency and collaboration. One significant integration is the deployment of AI-powered chatbots equipped with internal knowledge bases. These chatbots streamline internal communication by automating routine inquiries and providing team members with immediate access to information. This enhancement has improved knowledge sharing and collaboration across teams, allowing us to focus more on complex problem-solving and innovative initiatives.
We’ve also implemented AI-driven code completion assistants that leverage large language models. These tools assist our developers and data scientists by offering intelligent code suggestions and automating repetitive coding tasks. This has accelerated our development cycles, enabled faster iteration and reduced the time spent on manual coding efforts.
Overall, the integration of AI and ML into our workflows has led to significant improvements in productivity, collaboration and the speed at which we develop and refine our projects.
“The integration of AI and ML into our workflows has led to significant improvements in productivity, collaboration and the speed at which we develop and refine our projects.”
What strategies are you employing to ensure that your systems and processes keep up with the rapid advancements in AI and ML?
To keep pace with the rapid advancements in AI and machine learning, we employ a comprehensive strategy focused on continuous learning and adaptability. Central to our approach is fostering a collaborative culture that encourages innovation and the sharing of ideas. This open environment is crucial for staying current with the latest developments in the field.
We actively engage with the broader AI community through partnerships with research labs and academic institutions. These collaborations provide valuable insights and opportunities to work on cutting-edge projects. Our team members also regularly attend industry conferences and seminars to stay informed about new trends, tools and best practices in AI and ML.
Internally, we’ve established platforms for disseminating the latest machine learning research and technological advancements. Regular knowledge-sharing sessions and team discussions ensure that everyone is up-to-date and can effectively incorporate new methodologies into our workflows.
Moreover, we maintain a flexible approach to code design and data architecture. By building scalable and adaptable systems, we’re able to integrate emerging AI technologies and processes without requiring extensive overhauls. This flexibility allows us to remain agile and responsive in a rapidly evolving landscape.
Can you share some examples of how AI/ML has directly contributed to enhancing your product line or accelerating time-to-market?
The integration of AI and machine learning has directly enhanced our product development and accelerated our time-to-market in several meaningful ways. By incorporating AI-enabled tools into our development processes — as mentioned earlier with AI-powered chatbots and code completion assistants — we’ve streamlined workflows and reduced development cycles. These tools have allowed us to iterate swiftly, reduce manual efforts and focus on refining complex aspects of our projects.
Utilizing AI to interpret application logs and error messages has also contributed to faster issue resolution. This proactive approach to diagnostics means we can address and fix problems more efficiently, resulting in more stable and reliable outcomes.
Our emphasis on diligence and thorough evaluation in implementing AI technologies ensures that we align with industry best practices and regulatory standards. Robust checks and approval processes are in place to maintain the integrity of our work and uphold our commitment to responsible innovation.
By leveraging AI and ML in these ways, we’ve been able to enhance the quality and effectiveness of our projects while bringing them to completion more efficiently. This has positioned us to respond swiftly to opportunities and maintain a competitive edge in our field.
Strata Decision Technology provides financial software, data and insights to drive decisions and performance.
How is your team integrating AI and ML into the product development process, and what specific improvements have you seen as a result?
We have been using AI and ML models for years at Strata. I have also been lucky to be part of the pilot team at Strata to figure out how to use Generative AI in our day-to-day work.
In ideation, ChatGPT assists in brainstorming and refining ideas, streamlining the creative process. For user research, we leverage Dovetail AI to quickly transcribe, summarize and analyze insights, allowing us to make faster, data-driven decisions. GitHub Copilot accelerates development by providing real-time code suggestions, cutting down repetitive tasks and expediting the build phase.
One of our data products, the Comparative Analytics tool, uses advanced ML techniques that normalize and integrate customer internal data with external data, making comparisons of financial data easy and fast. By standardizing data automatically, it removes the manual data submission, so customers can pull up metrics and compare with any peer group they choose. This is now even more important as we are integrating multiple data products that Strata has acquired. We are in the process of coming up with new standards for our next generation of products.
What strategies are you employing to ensure that your systems and processes keep up with the rapid advancements in AI and ML?
We are actively piloting proofs of concept for GenAI tools and LLMs to explore emerging technologies, supported by an AI advisory group that monitors and evaluates new developments. We also work with our governance, regulatory and compliance team to ensure our data is safe throughout the lifecycle of each AI tool and ML model from POC to implementation. We have a very exciting dataset and we take data security and privacy very seriously.
We also seek partnerships for new technologies and LLMOps platforms, expanding our capabilities as AI evolves. To maintain ML model relevance, we continuously update and monitor our ML models for performance impacts at the product level.
Additionally, we’ve centralized AI knowledge, best practices and standards across the organization, created extensive documentation and training resources, equipping new users to adopt AI tools effectively. Finally, a cross-functional group ensures AI tools are implemented and utilized properly across departments, maximizing consistency and impact across the organization. This multi-layered approach keeps us agile, secure and aligned with the latest AI advancements.
Can you share some examples of how AI/ML has directly contributed to enhancing your product line or accelerating time-to-market?
One example is how we use ML models to normalize our data. To create a true “apples to apples” comparison between our customer and their peers, our data products utilize discriminative ML models during both implementation and production stages. These models standardize key categories, such as job titles, department names, account details and payor information to ensure consistent data across varied datasets. Healthcare data can be incredibly complex. For example, we found over 700 variations just for the title “Nurse” in our dataset. Using ML to standardize job titles allowed us to accurately calculate the nursing turnover rate and track how this rate shifted during the pandemic.
During our GenAI tool proof of concept, we closely tracked the time saved using AI tools. A prime example is synthetic data creation for testing, a traditionally labor-intensive process involving manual data adjustments or custom tooling for specific configurations. ChatGPT reduced time spent on this process by 92 percent, generating synthetic data files or skeleton scripts as needed. Engineers highlighted that AI tool automation relieved them from tedious tasks, enabling them to focus on more complex work.
“Engineers highlighted that AI tool automation relieved them from tedious tasks, enabling them to focus on more complex work.”
Medtelligent is a software company that develops comprehensive management solutions for assisted living facilities.
How is your team integrating AI and ML into the product development process, and what specific improvements have you seen as a result?
Over the past five years at Medtelligent, we have developed and refined a pipeline of taking ML/AI solutions to production which starts with identifying pain points that may be amenable to data-driven solutions. In doing so, we prioritize rapid iteration over prolonged analysis. This helps us avoid investing significant time and resources in solutions that may not eventually be successful in production.
Once the model is finally in production, we don’t simply set it and forget it. As part of our productionization process, we have established a feedback loop that captures and analyzes model errors. These errors are then used to refine the model and improve its performance over time. As the model matures, we offer users flexibility in how they interact with it. Users can choose from a range of options, from using the model as an assistant to help with specific tasks to fully automating certain processes.
What strategies are you employing to ensure that your systems and processes keep up with the rapid advancements in AI and ML?
The landscape of AI is constantly evolving, with new techniques, tools and applications emerging every day. To keep up with this rapid pace of change, any organization that looks to integrate this technology in their products or services must inevitably embrace a culture of continuous learning and adaptability. This sentiment is reflected in our company’s tagline — welcome change — which was intentionally chosen to embody this core value. What also helps is the autonomy the data science team has to set the AI/ML direction for the company, which makes it more agile in responding to change. On a tactical level, we hold weekly ideation sessions where we discuss the latest AI trends and tools and how we can use them to “make life better” for assisted living and memory care companies, communities, staff, residents and families.
“To keep up with this rapid pace of change, any organization that looks to integrate [AI/ML] in their products must embrace a culture of continuous learning and adaptability.”
Can you share some examples of how AI/ML has directly contributed to enhancing your product line or accelerating time-to-market?
There are too many applications to mention, but I will share a few examples. We’ve built an AI-powered early alert system that proactively identifies at-risk residents. By analyzing a diverse set of data points, ranging from clinical notes to medications, the system detects recent changes in a resident’s health status and uses this information to flag residents who may be at risk of departure from their communities. These early alerts allow staff to intervene and implement strategies to avert resident move-outs.
We also leverage LLMs to process and extract insights from unstructured data. This includes free-form text data from observations, progress notes, medication records and incident reports. Besides text, images constitute another major type of unstructured data within our electronic health records. Today, the healthcare industry still heavily relies on paper-based documentation and record-keeping. Similar to using a mobile check deposit app, where a photo of a check is processed to identify the payee and amount, we utilize computer vision models to analyze scanned images of various forms and documents generated in the context of senior living.
EDGE is a data and analytics platform that harnesses bank transaction data to measure consumer credit risk
How is your team integrating AI and ML into the product development process, and what specific improvements have you seen as a result?
Our team actively integrates AI and ML into various facets of our product development process. We employ traditional machine learning techniques within our analytics products to ensure compliance and maintain explainability. This focus on transparency allows us to provide interpretable insights to our clients, which is crucial in the fintech industry.
One avenue for more advanced techniques and use of LLMs and GenAI comes during data mining. EDGE works primarily with bank transaction data, which involves extracting insights from text and timeseries data at scale. ML and AI techniques allow us to accelerate that exploration, leading to more rapid product enhancements that better meet user needs.
Additionally, we are integrating GenAI into our software engineering process — allowing developers to leverage AI-based code completion and other tools in their development workflows.
As a result of these integrations, we’ve seen improvements such as more accurate analytics, faster time-to-market for new features and products that are more closely aligned with customer expectations.
What strategies are you employing to ensure that your systems and processes keep up with the rapid advancements in AI and ML?
To keep pace with rapid advancements in AI and ML, we employ several key strategies.
First, continuous learning. We invest in ongoing education for all team members, not just those in technical or data science roles. We believe that having a firm understanding of machine learning techniques benefits everyone in the company, enabling cross-functional collaboration and innovation.
Second, a compliance and explainability focus. We prioritize technologies and methodologies that align with our commitment to regulatory compliance and model explainability. This ensures that, as we adopt new AI/ML advancements, we continue to meet industry regulations and maintain client trust.
Third, staying informed. We actively monitor industry trends and participate in professional networks and conferences. This helps us stay abreast of emerging technologies and best practices that can enhance our products and processes.
Finally, collaborative innovation. We foster a culture of collaboration where ideas and insights are shared across departments. This approach allows us to integrate AI and ML advancements more effectively and ensures that our systems evolve alongside technological developments.
Can you share some examples of how AI/ML has directly contributed to enhancing your product line or accelerating time-to-market?
Enhanced analytics products: By utilizing traditional ML techniques in our core analytics offerings, we’ve improved the accuracy and reliability of the insights we provide. This has not only enhanced the value of our products but also ensured they remain compliant and easily explainable to our clients.
Accelerated software development: Incorporating GenAI tools into our development process has streamlined coding tasks and reduced the time required for debugging. This has accelerated our release cycles, allowing us to bring new features and updates to market more quickly.
Data-driven product enhancements: Through advanced data mining and ML, we’ve gained deeper insights into customer behaviors and preferences. This has enabled us to make targeted product enhancements that better serve our users’ needs and stay ahead of market trends.
In all these instances, our emphasis on compliance and explainability has been paramount. By ensuring that our AI/ML applications are transparent and meet regulatory standards, we’ve been able to innovate confidently while maintaining the trust of our clients and stakeholders.
