Tracy Pizzo Frey is the Founding Partner, Uncommon Impact Ventures. Founder, Restorative AI. She was a one time dancer, teacher, forest explorer, Googler and is currently a mom. Uncommon Impact Ventures invests in technology solutions led by founders who share our values to create products, equity, and profits with integrity. To support them, it created a proprietary model of startup development that combines the best of VCs, incubators, and accelerators. Its holistic playbook de-risks a new venture to create a higher win ratio by providing companies with capital, infrastructure, and connectivity.
A study by Lopez Research found that 80% of organizations are looking to embrace and expand automation efforts in 2022. Today, market leaders are also using artificial intelligence to automate processes, find patterns in data and model outcomes of various actions.
There are many definitions of automation; not all are considered artificial intelligence. Automation describes a wide range of technologies that reduce human intervention in processes. These solutions range from more basic automation, such as robotic process automation, that uses scripts to emulate human processes, such as extracting data, filling in forms, and moving files. Many companies started with robotic process automation, but the automation field is much more comprehensive than this. At the higher end of the practice, automation extends to using artificial intelligence to understand and react to conditions with minimal human intervention.
I recently had several conversations with NTT DATA about intelligent automation, including an interview for the AI with Maribel Lopez (AI with ML) podcast that you can listen to here. A key takeaway from my conversations with the company is that there’s no one-size-fits-all approach to adopting automation. Organizations can use automation to help alleviate the impact of labor shortages by reducing human intervention for repetitive tasks. Still, in other cases, you may want more advanced AI-enhanced automation that streamlines alerts and suggests subsequent actions. For example, AI-enhanced network management may assess a variety of alerts, define which alerts are the most serious, and suggest network configuration changes to remediate an issue. A company can even set these solutions to make changes, if desired, automatically. My other takeaways are as follows:
1. Focus on the problem, not the technologies. NTT DATA shared that a company needs to define the problem it’s trying to solve before the firm can successfully design a technology plan. It said, “We don’t define automation as technology. Automation is a set of tools and techniques to solve a business problem. We’re not looking for Robotic Process Automation (RPA), chatbot, machine learning, or IoT opportunities. (Instead), let’s first try and understand what we’re trying to solve and figure out if automation is the right solution.”
2. Map automation to business key performance indicators. There are many areas where automation can assist the business. How do you decide where to start? NTT DATA spoke of mapping projects back to one of the three big levers of creating business value within a company. Does it help you grow revenue, optimize costs, or reduce organizational stress? These are the three major drivers of enterprise transformation for every type of company.
As I work with IT leaders on defining areas where automation can significantly move the needle on business value, I’ve discovered these projects often require a more significant process transformation. Most companies struggle with the process reinvention aspect of automation but automating an inefficient process won’t yield the best results. A company should streamline the workflow first, then automate necessary but routine functions. For example, credit checks within a mortgage application process are easy to automate, but if there are five unnecessary steps before the credit check, automating a single aspect of the process will have minimal impact. The company can still achieve the benefits of automation by focusing on several “quick wins” where they can rapidly demonstrate the value of automation
3. What gets measured gets improved. Like any other project, it’s important to define metrics and procedures to measure success at the outset. In my experience, most organizations fail to specify if they are measuring hard or soft goals. NTT DATA calls this measuring return on value versus return on investment because some returns are qualitative versus quantitative. At some point, management will ask you to quantify the value automation has created for the organization. At times, a business can quantify the value of automation in dollars. At other times, the value gets measured as process acceleration or what tasks no longer need to be performed.
For example, employee experience and retention may improve due to automation minimizing manual labor. However, these types of metrics are difficult to equate to one item. NTT DATA shared that companies need to monitor automation outcomes to ensure everything works as planned and continues to perform well over time. Lopez Research reports that creating a lifecycle management approach to validating and refining automation is particularly important in AI-based automation, where machine learning can modify processes in unintended ways. The takeaway? Don’t forget data and automation governance, monitoring, and management.
Automation in the real world All of this sounds great but is it practical? Over the course of several meetings, the company shared several examples of automation, including a case study they had published on the company’s work with Integra Lifesciences. This case study highlights three different ways automation was used within an organization to achieve both quantifiable benefits as well as employee experience improvements. For example, Integra LifeSciences used NTT Data’s Nucleus AI platform to speed up Oracle ERP testing by 98%. It also achieved a 50% faster increase in processing Oracle ERP user access requests through digitizing paper-based forms.
As part of process transformation efforts, Integra also worked with NTT DATA’s Digital Experience designers to implement social listening technologies to actively track, gather and analyze data from the social media and online platforms favored by doctors and other users. Meanwhile, round-the-clock access to an Intelligent Assistant on Microsoft Teams, powered by Nucleus, allows employees to resolve issues at a time that works for them.
Things to remember As your business upgrades its technology portfolio, a certain amount of AI and automation will be built into your business’s software and cloud computing solutions. There are several questions you should ask as you progress in your journey. What does your technology team need to create versus what comes inherently in the product? Do you have the internal skill set to develop automation, or do you need to select a partner? If you choose a development partner, does the vendor support a wide range of solutions so you can choose what’s right for you? These are just a few questions you’ll have to answer to make the most of your automation strategy.
In the post-COVID era, organizations have experienced at least the first wave of digital transformation. Now companies are spending more time creating technology strategies that enable digital acceleration. Automation is a fundamental component of this strategy. Effectively, automation will help you evolve from accelerating simple repetitive tasks to creating intelligent systems.
Other areas for you to consider Machine learning and automation are also heavily used in security. If you’d like to hear more about ML and security, please check out this podcast with Lacework. As you look to pursue automation and insight through AI, you must build ethical AI. You can find an article I wrote on ethical AI here.
I look forward to sharing more with you on automation in the future.
Alex Hagerup, the CEO of Vic.ai, shares the difference between using AI for automation versus autonomy in accounting.
About Alex:
Alexander is a serial tech entrepreneur with a strong passion for artificial intelligence. Prior to launching Vic.ai, he founded two other technology companies; his last one was funded by Northzone Ventures and later acquired by NASDAQ-listed J2 Global Inc., in 2014. He has a finance and accounting background and is a former board member of 24SevenOffice.com, the largest cloud accounting & ERP system in the Nordic region.
There are numerous challenges to working with distributed data. How do we secure, analyze and govern data in a hybrid cloud world? In this podcast, Michael Factor from IBM Research describes what a hybrid data fabric is and how it helps companies gain value from data in distributed locations.
Michael Factor's bio.
Dr Factor is an IBM Fellow with a focus on cloud data, storage and systems. He has a B.Sc., Valedictorian (1984) in Computer Science from Union College, Schenectady, NY. M.Sc. (1988), M.Phil. (1989) and Ph.D. (1990) in Computer Science from Yale University. Since graduating, Dr. Factor has worked at the IBM Research — Haifa.
His current main focus area is hybyrd cloud data. Among his responsiblities is as a global lead for all work on Hybrid Data form IBM Research. In this role, he and the global team are defining future directions to ensure 1) it is easy to get the right data for a task, 2) that data is always used in a secure and governed fashion and 3) that IBM has high-performance, secure, highly-functional and cost efficient data stores and processing engines. In addition, he serves as the main focal point in moving IBM Research innovations from the Lab into the IBM public cloud where his team has contributed to services such as IBM Cloud Object Storage, IBM SQL Query Service, and various Spark related services. Beyond his Research efforts, he also works closely with both the IBM Public Cloud and the IBM Data and AI team to provide guidance and expertise on directions such as serverless computation, data lakes and future enhancements to object storage. You can follow Michael's research here.
You can follow me on Twitter @MaribelLopez and on LinkedIn here.
Machine Learning Operations (MLOps or ML Ops) is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently, as defined in various publications. In this podcast we take on the topic of MLOPs. What is it and is it like DevOps for AI? Turns out it’s broader than you might think including everything monitoring to governance and explainability. Adewumni shares why it's both necessary and exciting.
For her 30 second recommendation, Ade shared the Cloudera Fast Forward Labs blog which can be found here. She also mentioned a report by the Algorithmic Justice League on bug bounties for algorithmic harms which can be found here.
You can follow Ade on Twitter @Adewunmi and @FastForwardLabs . You can also find her on Medium medium.com/@adeadewunmi and LinkedIn here.
You can follow me on Twitter @MaribelLopez and on LinkedIn here.
In this podcast, Merve Unuvar, the Director of AI Platforms and Automation team in IBM Research AI, talks about automation trends including AI-enabled low code automation, API management, and how to deal with unstructured data.
About Merve Merve Unuvar is leading the global research strategy for Business and IT Automation, partnered with the IBM Hybrid Cloud Automation business with a focus on bringing AI infusion across IBM's Cloud Paks for automation, and integration. Her team consists of research and data scientists, engineers and designers building platforms, tools and programming models that enable data scientists and developers to create and operate AI models and applications faster and better. Merve's team is developing cutting edge technology in the intersection of core AI, distributed systems, cloud computing, human computer interaction and visualization.
Machine learning and cybersecurity are tied at the hip. In this podcast, Chris Pedigo, the Go-to-market CTO for Lacework, discusses key trends, common misconceptions and advice for navigating a rapidly evolving security market.
You can subscribe to the podcast on your favorite channel and the newsletter by visiting https://aiwithml.com
About Lacework Lacework is a security company for the cloud. The Lacework Polygraph® Data Platform automates cloud security at scale. It collects, analyzes, and correlates data across an organization’s AWS, Microsoft Azure, Google Cloud, and Kubernetes environments, and narrow it down to the handful of security events that matter. It was founded in 2015 and is headquartered in San Jose, California. Learn more at http://www.lacework.com.
Natural Language Processing in AI isn't a new field, but it's advanced rapidly since 2019. Are we at the human-level of understanding with NLP and what can be done today? Models trained on generic data sets often fail to retrieve the right information for businesses. Today, technology companies are developing solutions that allow enterprises to extract meaningful insights from textual data. In this podcast Shila Ofek-Kiofman, the Director of Language Technologies for IBM Research AI, shares what's happening in NLP research and how it will help companies create better models using business-specific terms. You can follow Shila at https://www.linkedin.com/in/shila-ofek-koifman-1660701/
How can computer vision and machine learning change retail? In this podcast, I interview Richard Schwartz of Pensa Systems. The company offers an automated retail shelf intelligence solution that uses patented computer vision and artificial intelligence to scan all products and categories within a store. In this podcast, we talked about how AI can help retailers with instantaneous access to actual shelf inventory conditions, enabling them to improve sales, optimize labor and deliver better shopping experiences.
In this podcast, I speak with Afsana Akhter about how Amelia Virtual care uses newer technology such as augmented reality, virtual reality and artificial intelligence to help individuals overcome fears from their homes and at professional facilities.
Her bio Afsana Akhter, CEO of Amelia Virtual Care
With 20+ years of experience across Tech and Digital Health, Afsana Akhter is driving the expansion and adoption of Amelia Virtual Care’s VR platform for mental healthcare. Afsana has held commercial leadership roles at Livongo, Prealize Health, and Medullan. Afsana holds BS and MEng degrees in E.E.C.S. from MIT.
AI Automation is a hot topic in enterprise IT circles but getting it right requires more than a set of Robotic Process Automation tools. In this podcast, I speak with Anisha Biggers from NTT DATA Services on the how and why of automation. We discuss topics such as return on value versus return on investment. Where to find us: You can follow me on Twitter at http://twitter.com/MaribelLopez and LinkedIN at https://www.linkedin.com/in/maribellopez/
Whether it's the virtual assistant on your phone, a chatbot in your mobile banking app or on a website, we've all experienced the good and the bad of virtual agents. Everytime there's a negative experience we blame the technology. Yet conversational AI interfaces have reinvented the way we interact with the world. In this podcast, Don White, the CEO of Satisfi Labs, shares his takes on this topic. We discuss where AI fits and how to build success AI assistants by focusing on specific tasks. To mix things up, I also asked Don for his opinions on the meta verse. You can follow
Don White on Twitter at @TheDonnyWhite and @satisfi On LinkedIN at
In this episode, Dr Flores shares the opportunities for AI and federated learning . She discusses examples in healthcare including, Gatortron, the largest clinical language model.
About Dr Flores. Mona G. Flores, M.D. – Global Head of Medical AI at NVIDIA
Dr. Mona G. Flores is the global head of medical AI at NVIDIA, where she oversees AI initiatives
in medicine and healthcare to bridge the chasm between those industries and technology.
Dr. Flores first joined NVIDIA in 2018 with a focus on healthcare ecosystem development.
Before joining NVIDIA, she served as the chief medical officer of digital health company Human-
Resolution Technologies, following over 25 years working in medicine and cardiothoracic
surgery.
Dr. Flores received her medical degree from Oregon Health and Science University. She
completed a general surgery residency at the University of California, San Diego, a postdoctoral
fellowship at Stanford, and a cardiothoracic surgery residency and fellowship at Columbia
University.
Dr. Flores also has a master’s degree in biology from San Jose State University, and holds an
MBA from the University at Albany School of Business. She initially worked in investment
banking for a few years before pursuing her passion for medicine and technology. Where to follow us: Maribel Lopez on Twitter at @MaribelLopez and LinkedIN https://www.linkedin.com/in/maribellopez/
Heidi Williams of Grammarly shares thoughts on the differences between building a Grammarly for consumers versus businesses. She also shares how the company approached creating inclusivity in AI. Heidi’s Past podcast interviews:
Heidi Williams is Head of Engineering for Grammarly Business, our newest product offering for professional teams and organizations.
At Grammarly, Heidi is inspired by the potential impact the product can have as a platform, with the opportunity to help reduce conflicts and misunderstandings in communication and educate people on how to be more inclusive and equitable.
Before coming to Grammarly, Heidi served as VP of Platform Engineering at Box, founded WEST Diversity and Inclusion, and was co-founder and CTO of tEQuitable, a confidential platform addressing issues of bias, discrimination, and harassment in the workplace. Heidi was at Adobe for 17 years and most notably was a founding engineer on Dreamweaver, which democratized web development in the late 1990s. Heidi volunteers as a technical advisor for PaymentWorks, Raise For Good, and CaregivingHQ and is a mentor for FastForward.org’s tech nonprofit accelerator program. Her expertise and perspective have been featured in Built In SF and the podcasts Stayin’ alive in Technology, Dev Interrupted, and CTO Connection.
As a lifelong soccer player, Heidi’s often on the pitch; she’s also an avid hiker, bicyclist, and kayaker. She once hiked with her husband across England, 192 miles coast to coast (with B&Bs and pub stops along the way).
Heidi studied at Brown University, where she earned a BS in computer science. She also attended Stanford University’s Executive Institute.
Alice Xiang is the Head of AI Ethics Office the AI Ethics Office in Sony Group Corporation, and also leads the AI Ethics Research Flagship in Sony AI. She joined Sony AI after working as the Head of Fairness, Transparency, and Accountability Research at the Partnership on AI. A lawyer and statistician by trade, Alice’s work sits at the intersection of social justice and AI. Alice is recognized as one of the 100 Brilliant Women in AI Ethics. In this podcast we discuss the discuss the growing focus on AI Ethics among technology companies.
Hillary Ashton is the Chief Product Officer for Teradata. She leads the global products organization, a diverse team responsible for innovation, product management, engineering and quality. Recently she's focused on Teradata's the hybrid multi-cloud platform. In 2019 she was named to the National Diversity Council’s (NDC) annual list of the Top 50 Most Powerful Women in Technology. In this episode we discuss data gravity, how to think about balancing cost versus analytical performance and focusing on outcomes versus the technology. Ashton also provides advice and tips for individuals seeking a career in data.
Dr. Carlotta A. Berry is a Professor in the Department of Electrical and Computer Engineering at Rose-Hulman Institute of Technology. She is also the 2021-2024 Dr. Lawrence J. Giacoletto Endowed Chair for Electrical and Computer Engineering.
Her research interests are in robotics education, interface design, human-robot interaction, and increasing underrepresented populations in STEM fields. She has a special passion for diversifying the engineering profession by encouraging more women and underrepresented minorities to pursue undergraduate and graduate degrees. She feels that the profession should reflect the world that we live in in order to solve the unique problems that we face.
In this podcast, Dr Berry shares her journey in robotics and how she approaches robotics education. She also shares her insights and strategies on eliminating artificial intelligence bias in robotics and creating a more diverse AI field.
