According to a World Economic Forum study, it would take 152 years to close the gender gap if we did nothing more than what we do today. 

This was just one insight during my discussion with Debbie Botha, Global Chief Partnership Officer, who further describes the Women in AI organization as a 'Do-Tank', not just another 'Think-Tank. 

"Even with the hardest efforts, we will still be biased human beings ....it is important teams build the future of our world with AI applications to embrace internal diversity as much as possible to create an inclusive AI and represent a right image of a multicultural society. That’s why we need... more women in AI, but (also) more diversity in general. This can extend to skin color, religion, education, country, family, age, etc.," Founders of Women in AI 

Started from a grassroots organization, Women in AI has grown into a global organization in 150 countries with boots on the ground in over 40 countries.  

Our conversations dive deeper into the following:  

 - making a visible impact on minimizing bias and closing the gender gap  

- five (5) ways to increase (or speed up) the process 

 - how does culture impact, hurts, or help the progress  

- example such as job description, where a small change can make a big difference

Is customer service really servicing the customer if the priority is capturing data rather than understanding the customer?  

Think about the last time you called customer service and spoke with someone who spent more time on data entry than really helping you, or what about a non-profit organization whose support center is manned by volunteers or interns?  Perhaps the customer service agent does not want to ask the customer to repeat themselves and thereby miss out on documenting key information, or perhaps the agent is so busy hearing the conversation for data entry and not listening to the customer's needs.   

Wyser is a 2-year-old start-up using AI technologies to document, in real-time, the voice of the customer. This reduces data entry errors while increasing data accuracy. More importantly, it allows the customer service agent to provide service to the customer rather than risk escalating customer frustration even further.   

Donna, "we are processing data in the most intelligent way to fully use it without someone having to manually look at it.... able to identify patterns and keywords to establish trends and then adapt accordingly."   

Additional topics discussed are:  

 - Shifting into an early-stage start-up from a long career in the industry 

 - Is this value of what you are doing immediately understood 

 - Types of bias in the traditional customer service approach 

 - Cultural challenges in off-shore call centers 

 - Loss of valuable information due to individuals who are slow at data entry

TBDWith the increase of AI technologies and, at the same time, a decrease in the cost of using those technologies, we are seeing a rise in startups and small businesses that can focus attention on solving specific business challenges.   

In this episode, Erin McFarlane, VP of Product Innovation at the Boston, MA startup Fairmarkit is doing just that with a new round of funding in 2022, bringing 'fairness to the market is possible.  Three (3) years ago, Erin took a risk to shift from working on the buy-side, most of her professional career, to the supply side and work with a very young start-up company.   

Erin, "Policies often state using procurement at a threshold of $X. P-card is reserved for smaller but more frequent spending. The middle ground is where Fairmarkit excels, supporting both direct & indirect spending."  

Additional topics covered: - What lured you away from industry to a start-up? - Fairmarket overview - What use cases is Fairmarkit solving for? - Does it really cost more for the organization to use diverse suppliers? - What and where are the AI technologies used? - Lessons Learned in working with Startups from Erin

As older generations (last of the baby boomers) are expected to be fully out of the workforce in the next 5-8 years, the organization will be left with a void of resources with deep expertise expanding over a career of 25 years.   

Their replacements? A new wave of resources leveraging data and AI technologies. Many of us already in the workforce have learned on the job or been taught by co-workers.  What about the generation in college/university today? Can we continue to expect the current formal education will be enough?  

Kerry Calnan, VP of Innovation & External Affairs at Nicols College in MA, USA, leads by experience, fostering capabilities such as the Center for Intelligent Process Automation (currently going into its 3rd year) and in Fall 2022, will launch a new 4-year degree 'AI Automation.'  

Intending to link real-world experiences at the undergraduate level,  Nicols College is continuing its efforts to remain at the forefront of education and emerging technologies combined with the 21st-century skill needed in our constantly changing global landscape. 

 Want to know more:  https://www.linkedin.com/in/kerry-calnan-75693a12/ https://www.nichols.edu/offices/center-for-intelligent-process-automation/

Our guest today is Mara Pometti. She outlines her professional journey from Journalist to AI Strategist and covers topics:  - Predictive maintenance solutions in the energy sector. 

Specifically, failures in high voltage cables routing energy from the wind farm to the mainland - Translating data through technical KPIS using visual data story - Perspectives on how the EMEA is embracing AI technologies - Human-centered AI discipline - Multi-disciplinary team throughout the AI lifecycle  - Lessons learned along the way.  

About Mara:

Mara is IBM’s first-ever AI Strategist. She defines herself as a data-savvy humanist who sits at the intersection of AI, data journalism, and design. Mara’s method uses human needs as a lens to uncover overlooked opportunities hidden in companies’ data and transform that data into stories that illuminate the vision of a new scenario that aligns AI projects with human values and business intents. ​ As a polymath whose experience spans from the humanities to AI, Mara acts as a translator who bridges these worlds through design, strategy, and data storytelling. 

Mara’s work focuses on humanizing AI, that is, responsibly weaving AI into the dynamics that we, as human beings, live in every day.   

Mara’s book: Communicating with data: information between data journalism e data visualization https://amzn.eu/d/9TjDgQO

User experience (UX) design defines a user's experience when interacting with a digital product, application, or website. It is the primary layer between the user and the technology.

Design decisions in UX design are often driven by research, data analysis, and test results rather than aesthetic preferences and opinions.  Software applications focus on UX design to bridge the gap between the results of the application and how the end user interacts with those results. 

There hasn't been a lot of focus on (UX) User-Centric Design Experience with AI-driven results.  

Laura's past 6 years as a UX Designer - and recently focused on AI applications in the past 3 years -has uncovered additional value to the stakeholder by including UX Design as part of the AI Lifecycle.  

Find Laura on LinkedIn: https://www.linkedin.com/in/laura-dohle-559aba121

I love food & I have embraced AI Technologies. Therefore, I was excited to discover Andrew Yip, Head of Future Foods at Monde Nissan Singapore.  In this conversation with Andrew, we take a discovery perspective of breaking down food to its chemical components, understanding taste and texture, where AI technologies are getting deployed today, and where we might see AI Technologies in the future.   Think kitchen in a restaurant or research. With new roles such as Food Technologist, technology and food are fusing more and more.   

More about Andrew Yip - https://www.linkedin.com/in/andrew-yip/

What if using AI technologies could remove the unconscious human bias when evaluating & segmenting unstructured data? What if we are no longer required to store petabytes (1 petabyte = 1 million gigabytes) of historical, industry-specific data to reference and compare?

That is the focus of the discussion with Adriano Garibotto, Co-founder - Chief Sales & Marketing Officer at Creactives SpA.

Humans look for patterns in data to cluster like data together, then use queries or a grammar-based machine learning approach that trains models based on existing data sets.  When using deep learning combined with natural language processing, those older approaches are no longer necessary to create a more realistic neural network of connections.

Creactives provides AI-powered solutions to provide real-time procurement insights and analytics, optimize inventories and procurement processes, and to render business data usable by overcoming geographical, linguistic, and ERP/PLM/S2P system barriers.

Discussion today focuses on how AI is slowly started to making its way into the legal system in India, as well as, trends Divya is observing as a practising advocate (lawyer / attorney).  A constant learner, she has recently has focused on AI policy and auditing for AI processes.    Divya also talks about global resources and connections she has used which can assist others in starting or continuing your education journey in AI privacy, policy, etc.  

Divya (दिव्या द्विवेदी) Dwivedi is a practising advocate at the Supreme Court of India. An Engineer turned Lawyer with experience in ensuring the legality of commercial transactions of Corporate world involving fields like AI, IoT, Data Privacy, IPR (Patent Drafting, Patents, Trademark Filing, Copyright Maintenance, IP Portfolio Management), Company Law, Technology Laws, Cyber Laws, Cyber Forensics, Environmental Compliances, Gender Justice, Legal due diligence.

This episode explores from point in time when an executive leader say 'This is our goal for this year' to understanding how to identify & 'right size' the enterprise data to support those strategic goals.  

I 1st heard the term 'right-size' when I worked in waste management. The goal of 'right-sizing' is to ensure you have the right solution to meet the customer's needs at a cost that is acceptable. I feel this term is appropriately used here as well. How much data and at what cost (time, bias) is the right fit to support value to the organization. 

Additional topics discussed with Samta Kapoor, from Accenture include:  - Avoiding data paralysis  - Linking data directly to corporate goals focusing on valuable outcomes - leading with value first  - Recognizing enterprise data may be in mulitple data sources and may have overlapping data across those sources - Governance and specifically identifying an individual who owns the governance is critical to sucess - Data is and should be treated like an asset - Start small with proof of concept for buying with business stakeholders and leadership - Not everyone will understand data. Taking time to fold in a data driven culture - What does it really mean when we say 'value to the organization' - Lessons learned