The Digital Advanced Technologies team is a part of the larger Advanced Technology Group at Wabtec. Think of us as the applied R&D arm of the company. Our main responsibility is to de-risk new technology introduction and deliver breakthrough solutions that can scale for our business. My team comprises several divisions focused on applying AI and advanced technologies at Wabtec. Our Artificial Intelligence group is focused on building new software and hardware products that bring scalable machine learning based solutions for our customers. More on the production engineering front, we have a cloud technologies group that builds curated cloud services for applications teams to simplify and standardize software practices across Wabtec; an IDP (internal developer platform) for our software development teams. Finally, we have a group focused on robotics and automation and how such operations can bring a new wave of capabilities to the rail industry.

What are some of the biggest challenges that your team faces when it comes to driving disruptive innovation and new digital product development in a large enterprise like Wabtec? How do you go about addressing those challenges? 

There are many challenges. These challenges also make the work exciting. In our industry, when introducing a new product into well-established operations, you need to “design the plane while landing the plane”. I know it’s a bit odd that I use an airplane analogy to describe what it’s like to drive innovation in the rail industry – but it works. The operation of railroads across the globe are vital from both a freight and passenger perspective. Any disruption to operations can cause ripple effectives on the supply chain or adversely impact a city’s ability to move its population around. When introducing a new product into well-established operations, especially in a minimum viable product fashion, we must be laser focused on the key hypotheses we’re looking to validate and what risks we are trying to evaluate. This targeted focus helps drive technology adoption. Also, just like any founder of a startup, you need to effectively communicate your innovation, business case, and potential impact to stakeholders.

How do you foster a culture of applied innovation within your team and throughout your organization, and what role do strategic product road maps and effective minimum viable products play in enabling new solutions to scale successfully? 

I strive to create an environment that gives my team the comfort to take swings on ideas.  I ask everyone on the team to ask themselves 3 questions to help foster this: 
“Are you excited about what you’re working on?” – if the team is bored, you are not getting the best out of them.  

Are you feeling a bit uncomfortable in the work you are doing?

if you’re not even a bit uncomfortable in your work, that means you’re not growing. You need to be on your toes and always learning something new. This brings intrinsic motivation and creativity to your work without you even realizing it.

Do you understand / can you see the business impact your work has to the organization?

There is no better feeling than looking at one of your projects and seeing the value you just created. You want to be able to point to your project and say, “I was a part of that, or I helped build that!”.  

Effective MVP’s and strategic roadmaps are vital to de-risking new products, regardless of the type of innovation. A good MVP helps de-risk both technical and commercial risks for a product. The goal is to come down that risk curve in a speedy and cost-efficient manner. This way if you need to pivot or park your idea, you have objective data to do so. Innovation is about iteration.   

What are some of the most exciting trends that you’ve seen emerge in AI and machine learning in recent years, particularly in relation to industrial applications and the IIoT?

Computer Vision (CV) and Digital Twin technologies have been adding a lot of value in the industrial space and particularly in rail. Advancements in edge AI and machine learning based CV tech has enabled us to perform more intelligent inspections on critical infrastructure, vehicle, rolling stock, etc. This helps get back to improving safety and reliability of operations. Digital Twins have helped in the predictive maintenance space. The ability to apply advanced concepts like physics-based neural nets into diagnosing or predicting health on an industrial asset has added a ton of value in system up time, cost profiles and performance capabilities.  

What breakthroughs in AI and machine learning are you most looking forward to from a technology perspective, and how do you think they will impact Wabtec’s business and products?

Generative AI and specifically LLMs are going to help bring in a wave of additional value. It’s going to help unlock unstructured data sets that we have in troves to help add a new “lever” to models looking to optimize asset performance or drive larger scale optimization in network operations, etc. Also progress around vehicle intelligence for high automation, autonomous vehicles and advanced robotics can allow us to explore new solutions for our industry. When it comes to automated operations, rail has a variety of advantages compared to other modes of transportation, but we have our complexities to overcome. I believe these technologies will help make our industry more competitive with truck and help promote the use of rail, which is a cheaper, safer, and more efficient mode of transportation.   

How do you stay up to date with the latest trends and developments in AI and machine learning, and what resources do you rely on?

The best way to stay up to date is by building cutting edge products and solving tough problems. Do this and you’ll be forced to seek the latest greatest tech. In most of our projects we end up creating new technology and intellectual property of our own. Networking and staying connected with various experts and strategic partners in the field is also very helpful. 

How do you work with other teams at Wabtec to ensure that your AI products and digital solutions are aligned with the overall business strategy and goals?

You know, this is a lot harder that one might think. We’re a large enterprise and have steadily grown organically as well as through numerous acquisitions. I look at alignment in two ways. The first is through common platforms and services and the second is through engagement and communication. Standardized platforms and services help drive alignment in core technology. This helps design out unnecessary cost, risks, and duplicative efforts. Change management, collaboration and patience are all extremely important to be effective across the organization. This one is tough, but you need to keep pushing communication and being creative on how to communicate these strategic directions. 

What advice would you give to someone who is interested in pursuing a career in AI and machine learning, particularly in the industrial sector?

Focus on building product that solves a big problem. The AI/ML models will come along for the ride. Understanding your product or problem well is vital. Combine that diligence in product development with a solid understanding in AI services and statistical methods you’ll be well on your way. Being well versed in application development, UX and foundational MLops are also good skills to have. 

Can you give us a sneak peek into some of the upcoming product upgrades that Wabtec customers can look forward to?

Absolutely. The Advanced Technology group at Wabtec has an exciting lineup of new product and technology innovation in the works. For our transit customers, we’re launching deep learning-based computer vision solutions that will enhance safety and efficiency in passenger bus and rail operations. Additionally, we’re developing advanced visual inspection technologies specifically designed for our freight rail customers to help improve rail safety and reliability. We’re also working on a range of human centered robotic and automation solutions. One example is teleoperation technology, which enables beyond line of site vehicle operation and low latency video streaming. We’re also developing various fixed and mobile railcar and track inspection solutions that can bring both robotic and visual inspection attention to critical systems for a railroad. So, there is a lot to look forward too. 

Bio for Milan Karunaratne, Sr Director, AI & Advanced Technologies of Wabtec Corporation

Milan has 15 years of experience in the freight transportation & logistics industry working for General Electric and Wabtec Corporation. A graduate of GE’s Edison Leadership Development Program, Milan has held various engineering roles with global exposure spanning engine technology development, control system diagnostics and systems innovation. He has extensive experience building and leading teams in advanced analytics, machine learning and automation technologies for large scale assets, advancing the Industrial Internet of Things (IIoT).  

Currently, Milan serves as the Sr Director of Digital Advanced Technologies under the office of the CTO at Wabtec. Bringing an entrepreneurial spirit to a large corporation, his dedicated team of AI data scientists, cloud engineers and architects as well as product strategists is focused around driving disruptive innovation and new digital product development across Wabtec’s $9B portfolio of businesses. Milan has over 20 patents granted throughout his tenure working in the industry and has grown that culture of applied innovation within his teams. In 2022, RailwayAge magazine named Milan among the “Top 25 under 40”, highlighting innovative contributions to the industry. 

He holds a B.E. and M.S. in Mechanical Engineering from Stony Brook University in New York and an MBA from UCLA’s Anderson School of Management, where he continues to serve as an mentor to Anderson’s Venture Accelerator and a Board Member to the Easton Technology Management Center. 

Bio for, Wabtec Corporation

Wabtec Corporation (NYSE: WAB) is focused on creating transportation solutions that move and improve the world. The company is a leading global provider of equipment, systems, digital solutions and value-added services for the freight and transit rail industries, as well as the mining, marine and industrial markets. Wabtec has been a leader in the rail industry for over 150 years and has a vision to achieve a zero-emission rail system in the U.S. and worldwide. Visit Wabtec’s website at: www.wabteccorp.com. 

The post aiTech‌ ‌Trend‌ ‌Interview‌ ‌with‌ Milan Karunaratne,‌ ‌Sr Director, AI & Advanced Technologies at Wabtec Corporation first appeared on AITechTrend.

I focus on driving digital transformation for travel and hospitality companies across the market. Within Intellias, lead the travel and hospitality vertical by designing a go-to-market strategy, developing innovative R&D projects, and delivering meaningful thought leadership content. 

In collaboration with the strategy and technology delivery teams, I also oversee business development activities, ensuring that our digital solutions meet the evolving needs of the travel and hospitality industry.

We aim to continually innovate and provide efficient solutions that drive growth and transformation in this dynamic sector. Therefore, I dedicate much of my day to monitoring industry and market trends. This continuous analysis helps drive our commercial and technology strategy and identify new business opportunities, ensuring Intellias stays ahead in the travel and hospitality technology space.

What inspired you to pursue a career in the Travel & Hospitality industry, particularly in the field of Digital Transformation and Consulting?

My roots are in a family of 3rd generation hoteliers with a history of hundreds of years of being in-keepers or in other parts of travel. I have worked in the technology field of travel in leadership positions for companies such as Expedia and HRS. I’ve always been fascinated by the potential of technology to revolutionize travel in every part of the journey, enhancing experiences for travelers and businesses alike. 

My vision has been to address and overcome the technological gaps that have historically limited the travel industry, which largely came into the world by technology being built in stacks over each other, disconnected, and never upgraded to a unified standard or for easy data exchange. I am driven by the belief that innovative technology solutions can unlock new possibilities and efficiencies in travel and hospitality by fluidly adopting the use cases and not rigid frameworks that are still the standard. 

This conviction led me to focus on making Intellias the trusted partner for building and implementing outstanding travel technology solutions, which they have proven for many other industries.

In your opinion, what are the key challenges and opportunities for digital transformation in the Travel & Hospitality sector?

The goal of digitalization in the travel industry is to future-proof the organization, which means fundamentally changing how it operates and delivers value to customers.

We face several challenging areas within the travel and hospitality industry, which build on each other like parts of a pyramid. As it is with a pyramid, they need to be in place on top of each other to make the building stand firm: 

1. Future technical debt (bottom of the pyramid)
The current technology state of travel industry companies is often a legacy IT stack where continuously new services are added on top over the years, often reaching the end of their lifecycle without being replaced, in many cases preventing further scalability and finally creating major issues around system stability. Leaders must decide today about this “future technical debt” coming tomorrow.

2. True platforming, end-to-end, online and offline (middle of the pyramid)
The next challenge is achieving seamless end-to-end service integration, both online and offline. The industry’s segmented structure, comprising suppliers, distributors, and travel technology, often leads to a fragmented view of the traveler’s journey. Each sector focuses on its domain, lacking insight into the overall experience. Particularly, integrating offline aspects remains a challenge.

While other industries have adopted IoT environments, the travel sector is still catching up in incorporating technology into the final stages of the travel experience, highlighting the need for a more unified approach across all travel segments. 

3. The human factor in travel as the unique centerpiece (top of the pyramid)
Travel often involves stress, much of which stems from the unknown. Reliance on professionals is crucial, from knowledgeable taxi drivers to skilled pilots and attentive hotel staff. This human element enriches the travel experience, ensuring safety and enjoyment, and can’t be replaced by automation or AI. 

Technology’s role is to streamline the process, removing unnecessary obstacles between travelers and industry professionals. It should work in the background to eliminate friction points, allowing for a focus on the most rewarding aspects of the journey. Human interaction, with its inherent trust and connection, remains irreplaceable, and our technological advancements should enhance, not bypass, this crucial aspect of travel. Thus, upskilling human talent with the help of AI will be one of the centerpieces for the years to come.

As an internal ambassador for travel domain knowledge, how do you foster a culture of knowledge sharing and expertise within Intellias staff in relation to travel integrations, builds, and environments?

At Intellias, we create an environment where everyone feels included. The collaboration between the development, sales, and marketing teams is crucial for sharing diverse perspectives and expertise, enriching the overall understanding of travel integrations. 

Our comprehensive internal knowledge base includes case studies, project learnings, and industry research. We also maintain channels for daily communication, sharing insights, asking questions, and discussing challenges related to travel projects. We exchange the latest updates, trends, emerging technologies, and best practices during the weekly meetings. This way, we keep our team updated and encourage continuous learning. Most importantly, we work on rapid prototyping to make the ideas come to life – and show proofs-of-concept to our clients to demonstrate our engineering prowess and keep our teams sharp.

What are some of the most significant challenges you’ve encountered in your role, and how have you overcome them?

I have centered my professional life around significant challenges as those provide the only valuable growth opportunities. Here are some key challenges I’ve faced at Intellias and the strategies I used to overcome them: 

Keeping pace with rapid technological change
The speed at which technology evolves in travel and hospitality is staggering. To keep pace internally, I’ve focused on fostering a culture of continuous learning and adaptability within our teams. Externally, I rely on my large-scale network of travel technology professionals and regularly update our skills and knowledge through training, workshops, and industry conferences.

Integrating advanced technologies into legacy systems
One of the major hurdles has been integrating new digital technologies with existing legacy systems. To address this, we’ve adopted a phased approach, starting with thorough assessments, followed by pilot projects to test compatibility and performance before full-scale integration. 

Balancing innovation with practicality
It’s easy to get caught up in the allure of cutting-edge technology. However, only some innovations are practical or necessary for some projects. We’ve overcome this by creating Design Thinking workshops with our clients to dig deep into their issues and determine which technical solutions make sense to solve them, when, and how.

How do you navigate the evolving landscape of technology and business trends in the Travel & Hospitality sector?

It’s crucial to stay updated with the latest digitalization trends and leverage critical technologies for competitive advantage. Here are the main trends in travel and tourism:

Cloud adoption
Cloud infrastructure provides the flexibility, scalability, and operational efficiency needed for growth in digital transformation. But most importantly, it gives a cutting-edge ability to handle data, which is essential for harnessing AI and other advanced technologies.

Machine learning & AI
AI is already transforming customer service in travel, with AI chatbots like Sofia from TAP Portugal Airlines and Julie from Amtrak providing efficient assistance and personalization. Hotels use AI for tailored recommendations, enhancing guest experiences based on data from loyalty programs. But even more, we will now see a shift to “upskilling existing talent” via AI support programs, which helps mitigate the current problem of continued staff shortages in the industry.

Internet of Things (IoT)
The Internet of Things connects various aspects of travel, from vehicles to hotels, allowing for real-time monitoring and asset tracking. This, for example, includes using beacon technology in airports, sensors for passenger flow management, and intelligent hotel metering for energy conservation. But how do you bring the online supply/distribution/operations technology landscape into full live context with the “offline” IoT technology? It seems that this is the “holy grail of travel” and the final last leg to connect it all – and one of the answers is likely “digital twins.”

Digital twins
Digital twins provide hyper-realistic live models and simulations of travel experiences, combining data to create immersive 3D models of destinations. Digital twinning enhances smart tourism by offering real-time updates on safety, itinerary, and local specifics for a memorable experience.

Companies can simulate different conditions for data generation with the help of AI/ML and get a perfect view of long-term business development. Think of a hotel at different occupancy levels, weather conditions, staffing levels, and F&B outlet usage. According to automated planning, simulations will create an entirely new, in-depth business picture. AI will run most of this, offsetting the results with automatic energy and revenue distribution management.

Using digital twins as the beating heart to integrate all online- and offline technology, we can offer a more connected, efficient, and personalized travel experience. It also allows Intellias to stay at the forefront of the industry’s digital transformation.

Looking ahead, what do you envision for the future of the Travel & Hospitality industry in terms of digital transformation and consulting, and how does Intellias fit into that vision?

I envision a significant shift toward what I call “deviceless travel.” This concept is rooted in the understanding that mobile technology has revolutionized travel and introduced new anxieties and dependencies. I foresee a future where travel becomes a seamless, almost device-free experience, characterized by the emergence of The Digital Purist Traveler.

This Digital Purist Traveler seeks to minimize the use of personal devices, relying instead on an integrated network of IoT devices and AI systems. The journey of such a traveler would be a masterclass in digital minimalism and efficiency: 

Seamless integration with IoT
Travelers would navigate their journeys through facial and voice recognition, interacting with IoT devices, from taxis to airport terminals. 

AI-powered personal assistance
Embedded identity systems, like the NeoKe program, would help AI models anticipate and arrange all travel necessities by connecting digital IDs of travelers through a secure network with each other – flights, accommodations, dining – based on individual preferences and real-time data.

Personalized and predictive services
AI would respond to immediate needs and predict requirements, like arranging clothes as per the destination’s weather or scheduling meals and transportation. 

Edge AI devices
For those not fully embracing purist travel, a new generation of devices with integrated Edge AI – like bright notebooks and augmented reality sunglasses – would offer more subtle and sophisticated digital assistance. 

Intellias fits into this vision by being at the forefront of integrating and refining these technologies. We focus on developing solutions that bridge the gap between ambitious concepts and practical applications. This includes working on AI integration and IoT connectivity and creating user-centric platforms that cater to the digital purist traveler and those preferring a more gradual transition to deviceless travel. 

The journey ahead is ambitious but grounded in realities and technological advancements already in motion. Our role at Intellias is to be the architect of these transformations, ensuring that the future of travel is not just a dream but an accessible, enjoyable, and seamless experience.

Finally, what advice would you give to individuals aspiring to pursue a career in leadership roles within the Travel & Hospitality and Digital Transformation sectors?

It’s mainly the “traveler-first” approach that must be at the center of any technology design. Anything that is a traveler’s full benefit, liking, and experience-lifting ability should be built. Anything else that might only have an indirect influence should always be measured: “How does this make the journey less frictionless? How can travelers be surprised and delighted at those touchpoints when technology plays a role?”.

Furthermore, it would help if you continuously learn about the latest trends and innovations in the travel and hospitality industry and digital technology. There are great newsletters, LinkedIn groups, small-scale industry events, and online get-togethers, which can inspire you daily about what comes next.

Focus on strategic vision, adaptability, and the ability to inspire and motivate teams. Be open to new ideas and approaches and be ready to adapt to the evolving industry landscape and test principles by using the Amazon methodology of “working backward” by creating “press releases” of fictional products that solve real-life issues.

But most importantly, meet with people from the industry in person. A quick story of a cup of tea can unlock unique opportunities by allowing you to connect the dots that you otherwise might oversee.

Fritz Oberhummer, Vice President, Travel & Hospitality at Intellias

Fritz leads Intellias’s Travel & Hospitality division, driving global collaborations with suppliers, distributors, and travel technology players. With over 25 years of industry expertise, Fritz brings a wealth of domain knowledge in travel and technology to Intellias partnerships, helping companies in their digital endeavors. 

Fritz has held significant roles in organizations like HRS Group and Expedia Group, developing next-generation travel technology, driving forward supplier-related product strategy, and forming lucrative 3rd party travel technology partnerships across global regions. He is also a successful former startup founder, mentor of startups in the Expedia JumpStart program, online marketing specialist, and hospitality professional. 

Recognized with numerous awards, including being a part of the esteemed PhocusWright Young Leaders Summit 2016, Fritz has established himself as a leader who intricately melds visionary leadership, technology, and strategy to help organizations succeed in Digital Transformation and achieve outstanding business results.

Intellias

Intellias is a trusted technology partner to top-tier organizations and digital natives, helping them accelerate their pace of sustainable digitalization. For over 20 years, Intellias has been building mission-critical projects and delivering measurable outcomes that meet our clients’ business needs. We are contributing to the success of the world’s leading brands, among which are Technologies, Syngenta, TomTom, HelloFresh, and Travis Perkins. Intellias empowers businesses operating in Europe, North America, and the Middle East to embrace innovation at scale. 

We help organizations from across various industries create innovative digital products and experiences using deep expertise in emerging technology, domain knowledge, and high-performance product culture. Based on the business’s strategic objectives, we enhance the product vision, technology utilization, and production capabilities.  

The post aiTech Trend Interview with Fritz Oberhummer, Vice President, Travel & Hospitality at Intellias first appeared on AITechTrend.

As VP of Industry Research, I am responsible for CompTIA’s research on various facets of the IT industry, including technology trends that are affecting digital transformation, the evolution of technical job roles, and developments within the IT channel ecosystem. Personally, my primary focus is on the shifts taking place within enterprise technology as organizations move from a tactical IT approach to a strategic technology mindset.

Considering your focus areas on cloud computing, cybersecurity, Internet of Things, and artificial intelligence, how do you approach delivering market intelligence to your audience?

We use methods and techniques that have been established as best practices within market research, utilizing three different categories of data: proprietary data that we collect, outside data available publicly or via subscription, and observational data that can come from many sources or interactions. We package our analysis in many content formats, such as longform reports, podcasts, videos, or presentations, depending on the audience we are trying to reach and the integration of our research with broader CompTIA initiatives.

What insights can you provide regarding the impact of generative artificial intelligence (AI), cybersecurity issues, and evolving hiring and retention practices on the IT industry and workforce, as highlighted in CompTIA’s “IT Industry Outlook 2024” report?

Ultimately, the IT industry (as with any industry) is driven by the people doing the day-to-day work. The goal is always to make the workforce as productive as possible, and generative AI and cybersecurity need to be managed appropriately to maximize productivity. When it comes to the IT workforce (extending to other industries beyond the IT industry), there is a pressing issue of finding enough qualified candidates, so we see hiring and retention practices evolving to achieve better balance in the supply/demand equation.

As per the report, how do you perceive the evolution of skills-based approaches beyond hiring into career development, and what implications does this have for individuals and companies in the technology arena?

Organizations have been pursuing skills-based hiring for some time now as one way of balancing the supply/demand equation. This practice is extending into career development for those firms that have done the prerequisite work of understanding individual skills within job roles. Skills-based career transparency means that companies will be tying career progression to skill development, and they will be open and consistent in sharing these policies with their employees.

Beyond the trends mentioned in the report, what other emerging technologies or industry shifts do you foresee shaping the IT landscape in the coming years?

The hybrid work environment is one that is still evolving as companies move further away from the measures forced by the pandemic and discover the best environments for both productivity and flexibility. Technology will play a key role in this evolution, including areas such as automating workflow and enhancing virtual collaboration. In order to build the workplace of the future, technology will have to work hand in hand with business operations and functional management in order to not only install the best tech products but also help the workforce adapt to new tools.

How does CompTIA define its mission in the realm of IT certification, training, and industry analysis, and what are the primary objectives behind initiatives like the “IT Industry Outlook 2024” report?

CompTIA’s goal is to unlock the potential of businesses going through digital transformation and of individuals pursuing tech careers. CompTIA research supports this mission by analyzing trends within the industry and delivering actionable insights for decision makers and job seekers. Reports like the IT Industry Outlook 2024 can be used by many different audiences to understand the impact of technology on business strategy and career development.

Can you elaborate on the 10 trends outlined in the “IT Industry Outlook 2024” report, particularly focusing on their implications for technology providers, cybersecurity, cloud architecture, and workforce practices?

Aside from the trends already mentioned, another major theme within the 10 trends is the construction of digital solutions. As solutions have become more complex in a modern environment, the underlying infrastructure must be resilient and flexible. This implies a cloud foundation, and many companies continue to grow in their maturity of multi-cloud models. Taken together, the different solutions a company may be building all support a digital transformation strategy. The next stage of digital transformation involves quantifying a high degree of complexity in a way that impacts organizational productivity.

What measures has CompTIA taken to support skills-based hiring and career development within the technology sector, and how does this align with the organization’s broader goals?

In the past five years we’ve made four significant acquisitions – two training companies, the developer of a deep learning analytics platform for online exam delivery and most recently, TestOut, a proven market leader in courseware, online labs and more for technical training and digital literacy education. We’ve also expanded our selection of certification and training products. In 2024 we intend to release up to 10 new certification and learning products and complete major refreshes of five existing certifications. These products cover the full range of technology disciplines – cybersecurity, data, infrastructure, coding, AI and more. We’re also developing resources to help people become more knowledgeable and confident in their business and soft skills.  We believe digital fluency is a universal need in almost every profession. Some level of understanding is required to be conversant, especially with technology at the core of most business operations.

Could you share insights into the level of optimism among IT professionals and technology companies regarding their career paths and industry prospects for 2024, as mentioned in the report?

The optimism among IT pros and tech companies ties back to the issue of supply and demand. There is incredible appetite for technology skills and services. Technology pros from generalists to specialists can find career opportunities and develop skills along their areas of interests. Technology firms have ample opportunity to deliver technology solutions for clients and tailor these solutions to specific needs. There will always be challenges, such as economic shifts or budgetary constraints, but by and large these challenges are outweighed by positive possibilities.

In what ways does CompTIA engage with technology professionals, career changers, and aspiring individuals to provide training, education, and professional certifications, and how does this contribute to unlocking the potential of individuals in the tech industry?

Millions of current and aspiring technology workers around the world rely on CompTIA for the training, education and professional certifications that give them the confidence and skills to work in tech. We do this directly through our own training programs and via nearly 4,000 academic, corporate training and content delivery partners worldwide. Businesses in all industries and all sizes – from Fortune 500 multi-national corporations to mid-size companies to small businesses – employ CompTIA-certified IT professionals. Some 3.5 million CompTIA certifications have been earned by IT professionals around the world.

The post Charting the Course of IT: Seth Robinson, VP of Industry Research at CompTIA, Explores Trends, Training, and Professional Certification first appeared on AITechTrend.

In my role, I focus on applications that are utilized by enterprise workers, including CRM, ERP, CDP, field service, contact center, retail, manufacturing, and logistics software, and other line-of-business applications.

What are the key components within the enterprise software market that fall under your research focus?

I look at the underlying technologies that are enabling these applications, from artificial intelligence, natural language processing, and RPA, to iPaaS platforms, API managers, and other utility software, including digital adoption platforms.

How does The Futurum Group contribute to the understanding of technology trends, including RPA, automation, and artificial intelligence, within various industries?

We have close relationships with nearly all major vendors in the enterprise application space, (see some of our clients noted here:  https://futurumgroup.com/about-us/customer-testimonials/), and regularly speak with other active vendors within in the market, as well as enterprise end users. Most of our research is available on our site, on major social platforms, and on podcasting platforms, without requiring a subscription or a fee.

According to The Futurum Group, what are the observations regarding the market for SaaS/Embedded AI applications, and how do you characterize its competitiveness?

The market is headed for consolidation, largely due to enterprises realizing they need to consolidate their technology stacks to better manage costs, reduce security risks, and prepare for the ubiquity of artificial intelligence across a greater range of workflows and processes. Large vendors, of course, have the initial advantage, given their ability to acquire, build out, and integrate technology for their customers more quickly. The segment will remain highly competitive in the years to come.

Which vendors stand out as key players, and what factors contribute to their significance in the SaaS/Embedded AI market?

Microsoft has established itself as a leading vendor, in terms of quickly integrating AI – particularly generative AI – into its enterprise and consumer products. They have taken a responsible approach to ensuring that the algorithms are grounded in vetted data sources, as well as establishing guardrails to help encourage responsible use. Other large SaaS vendors, including Salesforce, ServiceNow, Adobe, OpenText, SAP, Google, and Amazon, are also taking similar steps.

What are the projected trends for AI investments in 2024?

I see an increased focus on smaller language models that are tuned for specific verticals or functional areas, as the cost of generative AI compute can be expensive, particularly for high-volume applications (such as contact case summarizations, content marketing, and chatbots.) Large language models will not go away, but increasingly will be augmented in the market by purpose-built models that are embedded into SaaS applications where they are most appropriate.

The post Diving Deep into Enterprise Software: A Dialogue with Keith Kirkpatrick, Research Director at The Futurum Group first appeared on AITechTrend.

The journey of ChillBaby into the realm of SMART technology for juvenile care was deeply influenced by the personal experiences and insights of its leadership team, who are all parents themselves. Initially, the team’s attention was drawn to the issue of child abandonment in vehicles, particularly the alarming frequency of incidents involving children left in hot cars. Recognizing this as a critical problem where technology could make a significant difference, ChillBaby was inspired to act.

From this starting point, the team broadened their scope to tackle more complex challenges, with a particular focus on mealtime solutions. This led to the creation of Cammy, a product designed to aid parents and caregivers. Cammy provides allergen detection and alerts during meals, real-time logging of food consumption into a digital diary, and advanced features for choking hazard detection. Additionally, its facial recognition technology not only enhances safety but also captures joyful mealtime moments for sharing with loved ones.

At the core of Cammy’s functionality are proprietary AI algorithms, which leverage visual recognition technologies for real-time food analysis. This showcases ChillBaby’s commitment to harnessing cutting-edge technology for practical and impactful applications in childcare.

Looking forward, ChillBaby is dedicated to continuous innovation and development, with a focus on AI and machine learning. ChillBaby is focused on pioneering the next generation of smart technologies in juvenile products. By doing so, we aim to unlock enhanced safety outcomes and foster a safer, more nurturing environment for children and families across the globe.

How does ChillBaby stay ahead in tech evolution, and how does research shape its product roadmap?

Staying ahead in technology involves a strong commitment to research and development. Chillbaby Technologies invests heavily in understanding emerging tech trends, consumer needs, and industry developments. Our product roadmap is shaped by this ongoing research, ensuring that new products or updates to existing products like Cammy are in line with the latest technological advancements and user requirements. We partner closely with manufacturers so that we are able to understand the emerging needs of their customer base. Which allows us to validate our research and refine it as we meet the needs of our manufacturing partners.

Share instances where consumer feedback improved ChillBaby’s products, especially Cammy’s design.

For all tech companies, consumer feedback is invaluable. Although Chillbaby Technologies is not a B2C company, we do partner closely with our manufacturing partners to ensure that we are meeting demand for products like Cammy.

Any key collaborations shaping ChillBaby’s tech capabilities?

ChillBaby’s collaboration with Microsoft for Startups has been instrumental in enhancing its tech capabilities, providing access to advanced Microsoft technologies and AI tools, alongside crucial technical support, and expertise. This partnership offers business and strategy guidance, enabling ChillBaby to expand its market reach and scale up operations effectively. Additionally, it ensures that ChillBaby’s products, like Cammy, meet high standards of data security and compliance, vital in the sensitive area of children’s health data.

Additionally, ChillBaby’s partnership with pNeo, a business accelerator, has significantly advanced our marketing capabilities, providing valuable insights and strategies to enhance our market presence and effectively promote our products.

How does Cammy specifically ease modern parenting challenges through tech?

The primary audience for Cammy is broadly any parent or caregiver of a young child. We like to think of Cammy as giving families “eyes in the back of their heads.” Cammy provides an extra level of safety to support the often-hectic environment of today’s family homes.

More specifically, this product directly supports parents that care for children with food born allergies.

For first-time parents, Cammy offers peace of mind and a sense of security. It helps them monitor their child’s eating habits and potential allergic reactions with greater precision and ease. This is particularly important as new parents may not yet be fully aware of the signs of food allergies or how to manage them. When a child presents symptoms like a rash, which could suggest an allergic reaction, the ability to provide a detailed, real-time dietary log to medical professionals is invaluable. This precise tracking enables healthcare providers to narrow down the potential allergens more effectively, making allergy testing and diagnosis more efficient and accurate. For medical professionals, having access to such detailed information can be a crucial factor in developing an effective treatment plan.

A significant challenge during Cammy’s development and the successful resolution?

One of the significant challenges faced during the development of Cammy, was deeply personal and rooted in the experiences of Matt, the CTO of ChillBaby Technologies. Matt, who had struggled with allergies since childhood, was motivated by his concern for his children potentially inheriting similar issues. This led him to develop an early model of an allergen-detecting device, using a simple web camera set up in his kitchen, paired with a machine learning model, to identify the cause of his youngest child’s allergies. This initial development phase involved intense programming and months of trial and refinement, eventually yielding a robust dataset that proved crucial for medical consultations. Matt’s journey, blending technological expertise with a father’s determination, laid the foundation for Cammy.

Lasting impact vision for ChillBaby in child safety, health, and parenting experiences?

Chillbaby Technologies’ vision for lasting impact in child safety, health, and parenting experiences is centered around leveraging technology to create safer, more informed, and stress-free environments for families.

Ethical use of consumer data – how does ChillBaby ensure it, and benefits received?

For a device handling sensitive health-related data, robust security measures are crucial. Our approach includes end-to-end encryption of data, secure cloud storage, user authentication systems, and compliance with health data protection regulations like HIPAA (in the United States) or GDPR (in Europe).

Community engagement initiatives beyond products at ChillBaby?

ChillBaby originated from a deep-seated dedication to enhancing community safety, particularly sparked by the founders’ recognition of a grave social issue: child abandonment. Driven by the conviction that technology could offer a meaningful solution to this problem, ChillBaby was established. The company remains focused on this commitment, continually broadening the scope of its solutions to address a range of critical safety concerns.

How does ChillBaby foster a culture of continuous learning to keep up with tech evolution?

ChillBaby employs a continuous feedback loop from our systems, allowing us to constantly learn about how our products are used and perform in real-world scenarios. This approach enables us to gain insights into the benefits consumers are experiencing and identify areas where they could benefit even more from our solutions, ensuring ongoing improvement and relevance of our products.

Bio for, Chill Baby

ChillBaby^TM Technologies develops and manufactures innovative SMART hardware and AI solutions for the Juvenile products industry. In partnership with manufacturers, we incorporate SMART systems into existing and new product designs across strollers, car seats, and child nurseries, enhancing the overall safety and comfort of children. What we do:

• Hardware Development
• Brand App Development
• Manufacturing
• SMART Platforms

ChillBaby Website: https://www.chillbabytechnologies.com/

The post ChillBaby’s SMART Tech Revolution: Jason Lowe’s Story of Innovation in Childcare first appeared on AITechTrend.

I joined Hyperscience in 2021 as the company’s first Chief Technology Officer, responsible for leading the product, machine learning and engineering teams. I’m responsible for product development and delivery activities to ensure our enterprise artificial intelligence solutions support the needs of our customers. This includes exploring new and emerging technologies, managing the security of our platform, and ensuring seamless implementations.

Hyperscience is described as a “Machine Learning first platform.” Can you explain what this means and how it differentiates your company from others?

Machine learning is in our company’s DNA. In 2014, Hyperscience was founded by three ML engineers who wanted to apply ML and AI to the enterprise to help solve problems like clerical errors and time savings rooted in manual work. Throughout the last nine years, our systems have not strayed away from being an ML-first platform—we’ve continued to design with ML and deep learning models inherently baked into the process, leaning on our team’s extensive experience building applications in the enterprise AI environment. We’re very knowledgeable about building systems that work, which involves human supervision, model quality assurance and frequent testing to work out any issues along the way, separating ourselves from others in the industry just entering the ML space.

During your tenure as CTO, could you share some of your key achievements or projects related to technology strategy and vision?

I’ve always been proud of Hyperscience’s flexibility in helping customers work how they want to, from on-premises to cloud and SaaS deployment options. We continue to develop our platform through incremental change to stay up-to-date with the rapidly changing tech landscape.

We’re also used by companies doing great work in the community, which is incredibly humbling. Last year, Hyperscience announced a partnership with the International Rescue Committee (IRC) to apply ML to the IRC’s data collection in health clinics treating malnourished children. Malnourishment affects roughly 50 million children worldwide at any time, and it’s an honor to be helping support the IRC’s work to improve patient outcomes.

Hyperscience has begun working with several new large government and financial companies during my tenure. It is rewarding to help these massive companies and agencies secure their data and reduce infrastructure footprints, especially since they can pass these benefits to consumers. One example that comes to mind is a financial institution that implemented our platform to reduce the time to process funeral claims by 80 percent. Their company mission is to serve customers in times of distress, and I’m grateful we were able to play a small role in that mission.

On the sciences side, our team has created bespoke models that constantly generate award recognition for handwriting analysis. As the AI/ML landscape changes in 2024, we’ll help our customers find even more insights and savings through data analysis, which keeps me motivated daily.

Hyperscience supports various deployment options, including on-premise, private, and public clouds. How do you ensure that your infrastructure design caters to each deployment type’s specific needs and security concerns?

Hyperscience’s platform works with many integrations, including OpenAI’s ChatGPT and Salesforce, allowing us to cater the technology to each deployment.

From a security standpoint, we rigorously test our platform to ensure it meets the most stringent requirements. For customers that leverage our platform on-premises, the technology is folded into their internal security policies, so we do not manage those security protocols. However, Hyperscience manages and operates the system for customers using a SaaS model. We use a partition deployment model to ensure individual customer data is separated from others, and we are constantly reviewing our security protocols to ensure they’re up-to-date, as evidenced by our recent SOC 2 certification.

Hyperscience is known for its ability to automate many document processes. Can you provide specific examples of industries or use cases where the platform has demonstrated flexibility and effectiveness in transforming unstructured data into actionable insights?

Many industries benefit from turning unstructured data into actionable insights through automation, but two that come to mind are the public sector and claims insurance.

Government spending is often scrutinized, and many agencies have historically wasted countless hours on manual processes. The volume of citizen and bureaucratic requests is incredibly high, and relying on pen and paper for tasks like processing tax returns and passport renewals makes it nearly impossible to keep up.

For example, one agency faced a backlog of hundreds of thousands of claims in various handwritten formats and needed to shorten processing time to serve its citizens. In the first three months after using Hyperscience’s platform to automate claims processing, they processed 115,000 claims and are now saving $45 million annually. These are real taxpayer dollars, which underscores why automation in the public sector is so important.

Meanwhile, the insurance sector handles a high volume of data related to consumer financial and medical information, which is especially sensitive and requires extra care. Inaccurate data extraction from these forms is a huge concern in the industry, requiring automation that can guarantee a high degree of accuracy. Our platform has proven high accuracy even when automating complex data sources, allowing insurance companies to speed up processes and improve customer turnaround times.

How does your team ensure the platform remains adaptable to evolving document processing needs and stays ahead in accommodating new data sources and document types?

AI and ML constantly change as open-source models become more prevalent in business. At Hyperscience, we strongly believe in understanding the ethical ramifications of these evolving technologies before deployment to ensure our customers receive a solution that works best for them.

A big priority for my team is remaining up-to-date on how to leverage new data sources. We’re determined to share this knowledge with our partners. From technical blogs to demos on ChatGPT integration, we’re constantly exploring new ideas and creating a community of well-informed technologists. In the year ahead, as we look to expand across all human-friendly document types, I’ll continue working closely with my team to understand our customers’ changing needs, doing a lot more work with unstructured documents (contracts, deeds, email, etc.), and what capabilities they’ll require.

However, potential security and bias concerns inherent to AI/ML are top of mind for us, so we’re moving slowly and carefully to ensure customer data is protected, first and foremost.

Machine learning models are a crucial part of Hyperscience’s offerings. How do you effectively manage and maintain these models to ensure consistent performance and accuracy?

Certain models are more static than others and realistically do not need to be improved beyond a certain point, such as handwriting analysis. However, digital form submissions can be unique across different industries and even within a single industry and thus require more attention.

In these instances, we re-train ML models to ensure they work at peak efficiency, including anomaly and bias detection. Organizations leveraging AI and ML models must understand the concept of model drift over time and are actively working to keep their technology up-to-date.

What role does data management play in Hyperscience’s machine learning-first approach, and how do you address data privacy and security concerns?

Data management is critical to our approach, especially since many different types of data exist. Perhaps none are more critical than training data for AI systems, especially as it can introduce bias into large language models (LLM), impacting what information comes out. In our last few releases, Hyperscience has applied ML to training data management to address this issue.

For customers that store data on-premise, they manage control over their data. We ensure data is partitioned in different areas for SaaS customers and constantly update the platform with the latest security patches. Our priority is giving customers an offering catered to their specific needs, and we’ll support whichever implementation they prefer.

Hyperscience has achieved SOC 2 certification. Can you elaborate on the significance of this certification for your company and how it impacts your approach to security in AI systems?

SOC 2 certification is significant because it highlights that Hyperscience is operating at a certain level of security compliance, which is audited and updated annually. It’s a standard the industry understands that shows we’re on top of the latest security threats.

Regardless of this seal of approval, it’s important that we mature as a company and are secure with customer data. Our responsibility to provide a secure environment is one that we take very seriously, so we take every step to ensure our customers are protected.

With the increasing importance of security in AI systems, how do you ensure that Hyperscience remains proactive in addressing potential vulnerabilities and evolving security threats in AI and machine learning?

Bad actors leveraging AI for prompt engineering are inherently attempting to get access to underlying servers, but Hyperscience’s software isn’t open to the public, so the threat of intrusion is minimal. We’re constantly reviewing and updating our security procedures to ensure potential threats from new and emerging technologies are adequately dealt with. The recent SOC 2 compliance is just one way we show how seriously Hyperscience takes cybersecurity and that we’re on top of the latest potential vulnerabilities.

The post aiTech‌Trend‌ ‌Interview‌ ‌with‌ Tony Lee,‌ Chief Technology Officer at Hyperscience first appeared on AITechTrend.

I graduated from Bournemouth University, a relatively small institution in Southern England. While attending university, I met one of my co-founders, Alistair Russell (our CTO), and then met our other co-founder Dominic Lewis (our CRO) shortly thereafter and the three of us decided to try starting a company together.

Initially, we developed a product that built email-type workflows. When we moved to San Francisco in 2012, we were outsiders and had no obvious way to tap into the VC community, but we were able to leverage the technology used in our email workflows and other APIs to create a custom solution that would find VCs for us, send emails to these VCs and help us book meetings—all using automation. That’s when the proverbial lightbulb came on and we realized how valuable it was to use APIs and automation together. From there, the concept of Tray.io was born with the mission of creating a world where anyone in any department can solve business problems without the constraints of poorly adapted technology.

Can you explain how Tray Merlin AI’s natural language automation capability can unlock the full potential of automation? How does it differ from other solutions in the market?

Tray Merlin AI automatically translates natural language inputs—prompts or requests written in plain English—into sophisticated workflows, meaning anyone—from developers and business technologists to executives and front-line employees—can use it to develop fully baked workflows.

Previously, developing automated workflows required a certain level of technical aptitude or coding expertise, but Merlin completely removes that learning curve. By empowering everyone in your organization to create sophisticated workflows, you can unleash a formerly untapped level of innovation and productivity.

One of the things that makes Merlin unique is that it works across the entirety of the customer’s software stack, which is very different from the other GPT-related chatbot announcements. Those products will only be able to take pre-defined actions within their own application (i.e. a chatbot for a marketing automation platform will only be able to build workflows within the product while most marketing processes transcend many different applications inside and outside of the marketing organization).

Additionally, unlike other applications that interface with LLMs, the operational capabilities of Merlin AI and the underlying Tray platform are self-contained. Merlin only needs to fetch small pieces of information from the LLM on an as-needed basis during the integration-building process, meaning customer data is never exposed or sent to the LLM.

How does Tray.io’s vision to lower the barriers that prohibit enterprise-wide automation relate to the potential of generative AI?

These two concepts go hand-in-hand.

Typically, requests for information or business process improvements require complex integrations that span multiple applications. What seems simple to the requester, such as adding a new step in a company’s order-to-cash process, requires someone else, likely a developer who has a completely different set of priorities, to develop the complex business logic and then build, test and maintain the integration required to deliver that “simple” business process change.

To address this, we first made automation more accessible by offering a low-code platform that both technical and non-technical employees could use to integrate the disparate apps across their tech stack and develop automated workflows. With the release of Tray Merlin AI, we’re taking this accessibility a huge step further. Now, users don’t need to have experience with coding or business logic to create automated workflows. For example, if a marketing leader wants to create a new process to follow up on high-priority leads from a recent trade show, they can simply ask Merlin to create this workflow and, using generative AI, Merlin will then select the proper connectors from the Tray connector library, prompt for the required authentications and develop the multi-step workflow—all on the user’s behalf.

In your opinion, what are the unintended consequences of digital transformation that Tray.io’s AI can address?

When businesses ramped up digital transformation efforts to accommodate remote work environments during the pandemic, they inadvertently created a slew of other issues, such as technical debt, complex tech stacks and business process inefficiencies. Now more than ever, delivering fast results that meet customer demands—while keeping the business profitable—is a top priority for business leaders, and streamlining business processes to create efficiency and open room for innovation can help organizations achieve this.

This is where business technologists—employees who have technical skills but are not using them in their primary job function—come into play. By placing automation in the hands of each department rather than a limited few, Tray.io is empowering people to transform their fragmented processes into powerful business outcomes. Tray Merlin AI equips employees with self-service, AI-augmented automation, enabling companies to tap into a vast set of underutilized talent while taking the development burden off of their engineers. With Merlin, for the first time, these issues can be solved faster, more accurately and by a wider variety of people within the business.

Can you talk about how Tray.io’s platform brings together the power of flexible, scalable automation and support for advanced business logic?

Building and delivering an iPaaS that serves a spectrum of users from executives and front-line employees to technical developers is challenging. If you orient too much toward the non-technical user, you risk ending up with a rigid platform that is very simple to use but doesn’t do much more than connect applications. Orient the other way, and only a very small number of people in your organization can operate the platform, which will quickly lead to an amassed backlog of integration projects while the users of these integrations wait for something to be developed that often won’t entirely meet the need anyway—if it is even delivered at all.

With Tray, and now made even better with the introduction of Merlin AI, we can support advanced business logic–complex integrations involving multiple applications–with low code. This means that while developers can use the platform too, many others in the organization with the technical aptitude, or just the need to make business queries, can use Tray to build integrations and automations. That is the ultimate flexibility. However, offering such a broad set of users the power to build in the way that best suits them on a single platform falls apart quickly if careful attention isn’t paid to scalability. This combination of flexibility and scalability is the strength of Tray because our users can count on the power of our platform for the performance, security and governance they expect from a strategic platform that can be used across their entire software stack.

How does Tray.io plan to stay ahead of the curve in the fast-moving AI industry?

Generative AI and the pace of innovation it enables will spell the end of the iPaaS architectures that were built for a different time. AI is revolutionizing automation, and Tray.io is committed to staying at the forefront of this movement. We’re proud to be one of the first iPaaS solutions to offer native generative AI capabilities, and Tray Merlin AI is just the beginning. We’ll continue to improve and advance our platform to meet the growing needs, expectations and demands of our customers.

Because Tray is built entirely in the cloud and for the cloud, it made it far easier for us to add the Merlin AI intelligence layer to our platform. Unlike other products that must bolt-on AI features, Merlin can natively access a vast array of integration and automation capabilities including our extensive library of connectors. We’ve also already built the governance, security and scalability functionality into the platform that enterprise organizations will require as they determine how to best and most safely introduce AI capabilities to their employees and products.

What are some of the biggest challenges you foresee in the adoption of Tray Merlin AI, and how do you plan to overcome them?

The most immediate challenge is to punch through the noise in the market. OpenAI was the first company to make AI accessible to the masses, but there are many competing large language models that customers will ultimately have to choose from. Businesses must also sort through a myriad of vendors that are scrambling to bring products to market. For example, there have been so many ChatGPT connector announcements that the market at large has become numb to them—largely because the proposed functionality is only applicable to the product of the announcing vendor.

The opportunity for Tray to overcome these challenges is the simple fact that our solution transcends the entire software stack of an organization. Not limited to a single application, Tray Merlin AI can seamlessly build workflows that require access to and data from multiple applications. For example, an order-to-cash workflow can be built with Merlin that requires access to NetSuite, Salesforce, Google Sheets and email in order to end manual work and automate their way to cash faster.

In your view, how can Tray.io continue to lead in the low-code automation and integration space?

It’s all about continuous innovation and not resting on your laurels. We’re committed to constantly improving the Tray platform and delivering new and innovative features and functionality to the market—all with the aim of bridging the gap between line-of-business workers and the complexities of code to foster increased enterprise velocity and competitiveness in today’s demanding environment.

We do this by intently listening to our customers, understanding the implications of digital transformation and the speed and accuracy at which their businesses must operate. They are consistently seeking an integration and automation experience that has a shorter learning curve; more power and flexibility to build what they need when they need it; and that they know “has their back” when it comes to the governance, security and scale that is now mandatory. Especially with Merlin, Tray is perfectly positioned to continue to lead in the integration and automation space—period. Low-code describes one of the interfaces by which our users interact with the platform, but it no longer fully defines what we  offer and that is going to be a game-changer for Tray and for Tray customers.

The post aiTech Trend Interview with Rich Waldron, CEO and co-founder at Tray.io first appeared on AITechTrend.

Constant Contact was founded with the goal of giving small businesses the tools they needed to keep up with their larger competitors, and we’ve never wavered that mission – even after 25+ years. Our customers are retailers, restaurants, child care providers, plumbers, universities and all sorts of other businesses and not for profits that power our daily lives. We exist to help them attract the right customers, engage those customers and grow. To achieve that goal, we’ve created a comprehensive marketing platform that provides small businesses with the tools they need to become better marketers. 

We started as an email marketing pioneer, but we’re so much more than that now. Small businesses can use Constant Contact to send text messages, dial up their social media strategy, sell products, host events, raise money, manage their contacts and even run ads. Those core features are powered by innovative technologies like artificial intelligence and automation, which enable them to work more efficiently and drive more results.

Constant Contact has been a leader in the email marketing industry for many years. How do you stay innovative and adapt to the evolving needs of businesses and consumers?

We are laser focused on our customers 100 percent of the time, and we ask them for feedback on what their biggest challenges are. I know that sounds pretty basic, but you’d be surprised how often companies stray from that approach. Understanding their pain points and goals helps us shape our product roadmap and ensure we are building solutions that will help them save time and be successful. 

We also invest in research and development to ensure our platform incorporates cutting-edge technology, but we always want to do that in a practical and consumable way for our customers. For example, our customers told us that it takes them hours to build email campaigns – so, we built a new content generator that leverages AI and GPT technology to handle the writing for them. Now, they can move faster while still delivering high-quality content to their customers. We also launched new automation campaign flows that enable them to customize the types of messages they send, based on customer preferences and recent behavior.

With the rise of social media and other digital marketing channels, how does Constant Contact integrate and complement these platforms in its marketing strategies?

There’s a good chance that someone’s first experience with a brand will come via social media, so it’s important for small businesses to be active on the appropriate social networks. It can be an effective channel for customer acquisition and brand awareness. However, we often see small businesses rely too heavily on social media to be the primary driver of business results. It works best when it’s incorporated into a bigger marketing strategy, and that’s where a platform like Constant Contact can help.

Our platform allows our customers to deliver their message via email, a social post, sms or a paid digital ad.  We believe that helping our small businesses get the right message to the right customer at the right time is paramount of what we deliver..

In your opinion, what are the key elements of an effective email marketing campaign? How does Constant Contact help businesses achieve these elements?

The best email marketing campaigns have defined audiences, engaging content and clear calls to action. Whether you’re creating your first email newsletter, or managing dozens of automated drip campaigns, Constant Contact simplifies the process and makes it easy to build emails that get results.

Small businesses who struggle with writing can take advantage of our new AI Content Generator to craft high-quality marketing messages with clear CTAs in seconds. That allows them to spend less time on marketing while making it easy for their customers to convert. We also offer pre-built audience segments to help ensure those messages are getting to the right people. We also have pre-built automated campaigns and the ability for customer campaigns. These capabilities along with our personalization tools help our customers deliver effective messages in the channels their customers prefer.

Constant Contact has a large customer base. How do you ensure that each customer receives personalized support and attention in their email marketing journey?

A big differentiator for us is our award-winning customer support team. In this age of automated phone trees and AI chatbots, it’s refreshing to be able to talk to a real human. Our customers really appreciate that they can get in touch with us through phone or chat support whenever they have a question. Whether it’s troubleshooting a quirk or getting advice about a specific industry, we have experts ready to assist – and unlike many other companies in our space, our support team is available to every customer free of charge.

Data privacy and security are major concerns for businesses and consumers. How does Constant Contact prioritize and address these concerns to build trust with its users?

We are firm believers that everyone has a right to privacy, and we do everything we can to ensure that our customers’ data remains secure from bad actors. Our platform complies with all major privacy laws, including CCPA and GDPR, and we commission annual SOC2 audits by a third party to regularly evaluate the overall integrity of our security infrastructure. We have 24/7 threat monitoring and take several proactive steps to prevent would-be attacks from outside parties – including advanced endpoint protection, regular penetration testing, and a bug bounty program.

Constant Contact offers various features and tools to help businesses automate their email marketing. Can you discuss the benefits and best practices for using automation in email campaigns?

Automation is a tricky word because it can mean a lot of different things. For example, welcome emails and reminders are automated campaigns that are sent automatically whenever someone completes an action – like signing up for a newsletter. That type of automation eliminates manual work and helps make your brand sticky, and our customers have had access to it for years. But, the real power of automation comes when you combine it with AI to tailor your messages based on the preferences and behavior of each customer. 

If I’ve learned over the years that a customer only opens emails every few days – but they’re active on text – I can create an automated campaign that sends themn a text about a new product I just got in stock at my store. Then, if they don’t engage with it after 24 hours, the campaign will automatically follow up with an email offering a discount to encourage them to engage. 

That level of automation puts so much power into the hands of small businesses because it allows them to focus on their business with confidence while knowing their emails and texts will be delivered to the right person at the right time, automatically. We launched an automated journey builder earlier this year, and we’re really excited about what our customers are doing with it. It’s game-changing technology that small businesses haven’t had access to before.

What emerging trends or technologies do you see shaping the future of email marketing, and how is Constant Contact preparing to stay ahead in this evolving landscape?

Our goal is to make marketing easier, faster and more intuitive for small businesses. We see a significant opportunity to further integrate technologies like AI and marketing automation into our platform. As a customer-centric organization, our roadmap will always be driven by what our customers need to be successful. So, we will continue to invest in key areas where they are encountering obstacles.

Lastly, what advice would you give to businesses looking to improve their email marketing strategies and maximize their ROI?

A few things:

• Grow, grow, grow your list. Encourage website visitors, social media followers, and customers to subscribe to your emails and texts, then nurture those lists with engaging and helpful content.
• Make it personal. Consumers want to feel like their favorite brands know them and understand what they’re looking for. Tailor your marketing content based on their preferences and behaviors, and segment your audiences to deliver more relevant and targeted messages.
• Embrace technology. We’re living in the golden age of marketing technology, but many small businesses are slow to embrace things like automation and AI. Leverage all the tools at your disposal to save time and improve relationships with your customers so you can spend less time marketing and more time doing what you’re passionate about – running your business.

The post aiTech‌ ‌Trend‌ ‌Interview‌ ‌with‌ Laura Goldberg, CMO of Constant Contact first appeared on AITechTrend.

Welcome, ladies and gentlemen, to an exciting session with Xander Song, a Developer Advocate and Machine Learning Engineer at Arize AI, a leading company in the world of technology and AI. 

Song has been instrumental in shaping Arize AI’s success in ML observability and pushing the boundaries of AI monitoring, model performance, and transparency in the evolving generative AI space. 

Most recently, Song – along with a team consisting of Mikyo King, Francisco Castillo Carrasco, and Roger Yang – helped develop Phoenix, an open-source library offering ML observability in a notebook to better monitor and fine-tune generative LLM, computer vision, and tabular models. 

We caught up with Song on the thinking behind Phoenix and Arize’s strategy more generally. 

Research breakthroughs are always fascinating! Can you provide insights into any recent advancements or methodologies developed at Arize AI that have been instrumental in advancing the field of model monitoring and debugging?

One big breakthrough that I’ve been focused on for the past six months is Arize Phoenix. 

Phoenix is open source software that enables evaluation and risk management for LLMs, computer vision and tabular models. Phoenix’s main users are the people building applications on top of LLMs.

For example, a data scientist might be building an application using an LLM like OpenAI’s ChatGPT to generate legal advice in a virtual lawyer product, or a startup might be working with medical providers trying to accurately summarize doctor-patient meetings for an electronic medical record. 

As the industry re-tools around LLMs and data scientists apply large foundational models to new use cases like these – supplanting traditional approaches – they lack ways to reliably evaluate whether LLM applications they build are ready for production. And when they are in production, data scientists also have no idea when models fail, when they make wrong decisions, or give poor responses (LLM) or incorrectly generalize. That’s dangerous in a world where we have known issues around bias and hallucinations for major models like GPT-4. 

The risk of deploying LLMs in high risk environments (i.e. working with medical or legal data) is immense, and running blind without tools such as Phoenix should give pause to businesses that depend on LLM technology. Phoenix can help teams visualize complex LLM decision-making, monitor LLMs when they produce false or misleading results, and narrow in on fixes to improve outcomes. Phoenix also supports computer vision and other language model use cases, and traditional ML use cases. 

Can you walk me through a typical scenario? 

Phoenix finds where LLMs go wrong. Let me give you an example. Say you’re building a health insurance customer care chatbot. Users can ask this chatbot about their coverage plans from the health insurance provider. This is an application that demands a high degree of trust in the output of the LLM since users depend on the answers to decide what specialists to see or procedures to take. We want to find where the chatbot gives inaccurate/hallucinatory responses. 

Phoenix runs in a notebook locally, and the library leverages clustering of embeddings for debugging. 

Embeddings are vector representations of data. They are everywhere in modern deep learning, such as transformers, recommendation engines, layers of deep neural networks, encoders, and decoders. They preserve relationships within your data.

In order to use Phoenix, users:

• Load their data (Example: Chatbot conversations which include prompts & responses). This leverages embeddings and LLM-assisted evaluation to generate scores for responses
• Start Phoenix 
• Investigate groups of responses that are problematic (Example: questions from Spanish-speaking patients where the LLM responded incorrectly) 
• Download bad responses to use for LLM fine-tuning & improvement

Step 1: Users upload their data and embeddings into Phoenix. They can see groups where the LLM gave good responses, and areas where LLMs gave bad responses. 

Step 2: Users can grab groups of responses (clusters) that represent a problem

Step 3: Troubleshoot and grab prompt & response pairs 

In short, Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality.

What other solutions exist around LLM observability? 

We haven’t seen many. LLMOps is a rapidly-emerging discipline with new players emerging seemingly daily, so it’s an exciting space to contribute to and watch!

Modern models are built on latent structure and embeddings as the foundation of how they work. Embeddings are the core building blocks of transformers. Phoenix maps out how the embeddings connect, how they are related to each other and how they progress as sentences are generated by LLMs. 

Embeddings can either be extracted from the LLM itself as it’s generating text, generated using services such as OpenAI’s embedding generator service, or generated locally on data by another LLM. Once extracted the latent structure gives an idea behind what the model has learned, what it’s thinking and how that thinking progresses. 

Phoenix is the first observability solution we’ve seen built with embeddings as the core foundation but we are certain it won’t be the last. 

Has anyone tested Phoenix? 

Anyone can try out Arize Phoenix now, and we’ve been fortunate to get feedback from over 100 users and researchers at different companies and organizations who were generous with their time in advising us on the development of Phoenix and related technology using embeddings.

Phoenix is still relatively new, but reception has been positive. Here are a few quotes from folks on the technology: 

• “A huge barrier in getting LLMs and Generative Agents to be deployed into production is because of the lack of observability into these systems. With Phoenix, Arize is offering an open source way to visualize complex LLM decision-making.” –  Harrison Chase, Co-Founder of LangChain
• “This is something that I was wanting to build at some point in the future, so I’m really happy to not have to build it. This is amazing.” – Tom Matthews, Machine Learning Engineer at Unitary.ai

How does this fit into Arize AI overall? 

Phoenix is designed to be a standalone offering delivering ML observability in a data science notebook environment where data scientists build models. 

The team designed Phoenix so that data scientists can quickly evaluate their model decisions, augment data, iterate on it, and identify patterns or clusters to perform production workflows such as prompt iteration and model analysis without the need to rely on engineering functions for implementation. This aspect is the key to empowering enterprise data science teams and anyone building on top of foundational models, giving them the right tools to improve performance and model outcomes.

It is the Arize AI team’s vision that these ML notebook-based observability tools (personal tools for the data scientist) have connections to larger platforms such as Arize. The ability to download datasets, iterate locally and upload clusters of data or discoveries into large platforms will become the normal operational workflows for fixing and improving AI systems.

Why are tools like this important? 

There is probably nothing more important in the tech world right now than tools that help teams understand what AI is doing, where it is going wrong and why.

According to a University of Pennsylvania study 80% of the U.S. workforce and over 300M people globally will have their jobs impacted by GPTs. Generative AI is already reshaping industries in ways we’re barely starting to understand. As new applications get built, Phoenix is here to provide the right guardrails to experiment and innovate with this new technology safely. By remaining open source, Phoenix provides the implementers of AI the ability to evaluate LLMs and generative models in an unbiased environment. 

How does Arize AI foster a culture of innovation and collaboration to encourage research-driven advancements in the field of AI monitoring and explainability?

There is probably nothing more important in the tech world right now than tools that help teams understand what AI is doing, where it is going wrong and why. Arize combines diversity with unique passion and expertise to continue to encourage research-driven advancement, and I’m proud to be a part of such a talented group. Perhaps nowhere is that culture of innovation more prominent than Phoenix, which realizes a vision for contributing to the open source community.

Conclusion

We would like to express our heartfelt gratitude to Xanader Song for sharing his experience, knowledge, and perspective. 

As we conclude this interview, let us remember that the future of AI lies in the hands of visionaries like those at Arize AI. Together, we can continue to unlock the untapped potential of AI, shaping a future where technology serves as a catalyst for positive change in all aspects of our lives.

Thank you for joining us on this enlightening journey, and we look forward to witnessing the continued success and groundbreaking advancements from Arize AI and its exceptional team.

The post aiTech‌ ‌Trend‌ ‌Interview‌ ‌with‌ Xander Song, a Developer Advocate and Machine Learning Engineer at Arize AI first appeared on AITechTrend.

We recently announced that we will be leveraging Generative AI, and specifically Large Language Models (LLMs), with our existing platform, which indicates the direction of our strategy in the long term and is a differentiator for us against other industry players. Moreover, we are wrapping LLMs into our platform, but we are ensuring that it is in a way that doesn’t compromise the reliability of our virtual agents. While the industry is keen to bring the latest developments in the AI space directly to the enterprise, we need to ensure that it is in a responsible way that maintains the high level of accuracy that businesses and customers alike demand. Finally, we have a reputation at boost.ai for our ability to scale up customer service and internal support seamlessly, and LLMs will allow us to supercharge our ability to do this. 

As a thought leader in the conversational AI space, how does Boost.ai actively contribute to the industry’s discourse and shape the future direction of AI-powered customer experiences?

Speaking directly with customers and businesses is the best way to hear and understand their challenges. That’s why we attend numerous conferences and summits, such as the recent REWORK Conversational AI Summit and the Digital Transformation in Insurance Conference. We always make sure to keep abreast of the latest developments and conversations in AI and the broader tech industry and produce content like videos, e-books and newsletters to keep our customers informed. We want to showcase the potential of AI to improve both the customer experience and wider business objectives, and we think the way to do that is not just through the demonstrable capabilities of our platform but through transparency and sharing our expertise and knowledge. 

With emerging technologies like natural language processing, machine learning, and neural networks, what novel approaches is Boost.ai exploring to advance the capabilities and performance of its conversational AI solutions?

We have been working on significant enhancements to our platform, particularly with the integration of Large Language Model (LLM) technology. This involves creating what we’re calling a Hybrid NLU system, which combines the predictive abilities of LLMs with the enterprise-grade control of our conversational AI platform. This hybrid system offers unmatched accuracy, flexibility, and cost-effectiveness.

Our latest update focuses on key customer experiences improvements such as content suggestion, content rewriting, and accelerated generation of training data. We’re leveraging Generative AI to propose messaging content to AI trainers within our platform, which generates suggested responses and drastically reduces the implementation time for new intents.

Moreover, our Hybrid NLU approach allows enterprises to benefit from the combination of our market-leading intent management, context handling, and dialogue management solutions with powerful LLM-enriched tools. Our existing intent engine is highly trained with guardrails in place to guide the LLM, thereby improving overall accuracy and minimising false positives.

The end result of our efforts is virtual agents that can confidently provide precise answers to inquiries and a more streamlined development path that significantly enhances how our customers can build scalable customer experiences for both chat and voice.

Boost.ai’s success is built on delivering exceptional customer experiences. How does Boost.ai foster a culture of customer-centric innovation within the organization to continuously enhance its conversational AI offerings?

We put the customer at the heart of everything we do. By remaining customer-centric in our way of thinking, we are better placed to identify areas of improvement for our solution, which we then work hard to address. 

Our solution has a number of in-built tools that allow both our customers and their end-users to provide feedback in order to continually enhance the virtual agent experience.

Our team actively engages with our customers to understand their needs, challenges, and goals. These insights guide our product development process, ensuring that our conversational AI solutions not only meet but exceed our expectations.

We continuously invest in research and development to stay ahead of the curve and are always looking for ways to leverage emerging technologies – whether that’s Generative AI, Voice tech and more – to enhance the capabilities and performance of our conversational AI solutions.

As ethical considerations surrounding AI become increasingly important, what principles and practices does Boost.ai adhere to ensure responsible and ethical use of conversational AI technology?

When it comes to using AI in the enterprise, there is no room for error. We have seen criticism towards the raft of generative AI offerings rolled out in recent months, particularly for ‘hallucinations’, i.e. providing inaccurate responses. We have a responsibility to our clients, and they have a responsibility to their own customers, too, to prioritise accuracy above all else. This is our primary consideration when making changes to our platform. We believe that virtual and human agents can and should work together, and keeping a human-in-the-loop helps to accentuate each other’s best qualities, alleviating stress on human workers and streamlining processes. Ethical use of AI means not biting off more than you can chew and taking considered steps in adopting new AI technologies. 

Collaboration with industry partners and stakeholders often leads to breakthrough innovations. How does Boost.ai actively seek partnerships and collaborations to build a robust ecosystem and drive collective progress in the conversational AI domain?

Conversational AI doesn’t exist in a vacuum; to be effective, an internal AI culture needs to be built up so that employees can see virtual agents as the allies they are. Our boost.ai AI trainers programme ensures stakeholder engagement within the firms deploying our platform and encourages a deeper understanding of how our virtual agents work. Furthermore, we have partnered with firms like Clarasys to bring our platform to as many customers as possible. Conversational AI can seamlessly slide into existing processes and improve them greatly. Collective progress in the conversational AI industry will come from more businesses realising the technology’s potential and seeking to invest in the industry. 

Boost.ai has achieved considerable success in the conversational AI market. How does the company approach international expansion and adapt its solutions to cater to diverse global markets and cultural nuances?

We have a blueprint for success from our beginnings in Norway. We work with 9 of the 10 biggest banks in the Nordic region and have worked with local governments and leading telecos and retailers. One thing that we have learned is that there’s no such thing as one size fits all. Each virtual agent will be undertaking different tasks within different contexts, and that’s why working with our clients to develop customised virtual agents, tailored to their needs is so important. One of the great things about conversational AI is that it is adaptable and universal. Customer service exists in similar channels worldwide; everyone can benefit from automated customer service. 

Looking ahead, what emerging trends or technologies do you believe will have a significant impact on the conversational AI industry, and how is Boost.ai positioning itself to capitalize on those trends?

As mentioned, Large Language Models are a technology that is set to revolutionise how enterprises can scale customer service and support going forward. The critical challenge that needs to be addressed is using LLMs to answer end-users directly without needing a human in the loop to approve responses. We believe that the key to achieving this in the future will be to connect the LLM answer with a trustworthy source and figure out a way to verify it with an acceptable level of accuracy. This is the most crucial step in utilising LLMs in customer-facing applications. Once this part of the equation is figured out, I can see a future where our conversational AI platform fully integrates free-talking language models with its other components to ensure they remain structured and verifiable.

Given the highly specialized nature of conversational AI, how does Boost.ai attract top talent and foster a culture of continuous learning and skill development within the organization?

We believe in investing in our team’s growth, as we understand they are our greatest asset. With the recent explosion in the AI space, we attract top talent by positioning ourselves at the forefront of this AI revolution, offering unique opportunities to work on groundbreaking projects like Hybrid NLU, Voice and other core technologies.

Norway’s work culture, known for its emphasis on work-life balance, cooperation, and flat organisational structures, is a significant part of our ethos at boost.ai. We believe that these elements contribute to a healthy, productive, and innovative work environment, and we take these values forward to our global offices in other parts of Europe and the U.S.

In the context of Boost.ai’s thought leadership and market impact, how does the company measure success beyond traditional metrics like revenue or market share?

We are extremely proud of our low customer churn rate. 40% of our clients have come to boost.ai from a competing solution, and less than 1% have left us. This is a strong indication that the more than 500 organisations that currently use our conversational AI platform have found the solution that meets their needs. This is similarly reflected in the incredible results that our clients are seeing, which include consistent conversation resolution rates of over 90% and some of the leading brands in the Nordics and beyond automating more than 20% of total customer service traffic through their virtual agents.

The post aiTech‌ ‌Trend‌ ‌Interview‌ ‌with‌ Henry Vaage Iversen, Co-founder & CCO, boost.ai first appeared on AITechTrend.