Michael Thieme Of Accenture Federal Service On Five Things You Need To Create A Highly Successful Career In The AI Industry

Artificial Intelligence is now the leading edge of technology, driving unprecedented advancements across sectors. From healthcare to finance, education to environment, the AI industry is witnessing a skyrocketing demand for professionals. However, the path to creating a successful career in AI is multifaceted and constantly evolving. What does it take and what does one need in order to create a highly successful career in AI?

In this interview series, we are talking to successful AI professionals, AI founders, AI CEOs, educators in the field, AI researchers, HR managers in tech companies, and anyone who holds authority in the realm of Artificial Intelligence to inspire and guide those who are eager to embark on this exciting career path.

As part of this series, we had the pleasure of interviewing Michael Thieme.

Michael Thieme is Accenture Federal Service’s Managing Director, Generative AI Center of Excellence Lead. Thieme leads a team of data engineers, human-centered designers, and analysts to deliver innovative, high-value Generative AI solution for federal agency clients.

Thank you so much for joining us in this interview series! Before we dive in, our readers would like to learn a bit about your origin story. Can you share with us a bit about your childhood and how you grew up?

I grew up in Massachusetts, in the general vicinity of Amherst. I had the good fortune and privilege of attending excellent schools which helped to prepare me for my career.

Can you share with us the ‘backstory” of how you decided to pursue a career path in AI?

I spent the first 15 years of my professional career in identification technologies, including biometrics. At first, this was for commercial entities, including banks, and industrial manufacturers. But after 9/11, demand and priority came from the federal government. In the early days of biometrics, the technology was not reliable; it was based on rudimentary statistical techniques, and it failed frequently. Around 2016, we in the industry observed first-hand the dramatic and sweeping improvements that AI brought. It transformed biometric identification — for better or worse — and it was clear that anyone working in next-generation technology had to develop skills in this emerging technology.

Can you tell our readers about the most interesting projects you are working on now?

The most interesting project I am working on now is delivering federalized Large Language Models (LLMs) to meet our clients’ specific needs for performance, security, and transparency. FedGPT, Accenture Federal Services’ federalized capability, delivers the transformative capabilities of Generative AI while managing risk and limiting harmful or inaccurate outputs. Every day, a new model or technique emerges that we can learn from, adapt to, and enhance.

None of us are able to achieve success without some help along the way. Is there a particular person who you are grateful for who helped get you to where you are? Can you share a story about that?

Whatever professional success I have, I owe to my former boss, Tiffanny Gates, the former CEO of Novetta, which was acquired by Accenture Federal Services in 2021. Tiffanny recently moved on to join Accenture Federal Services’ Board of Managers. She helped me through a challenging period where my skills were not necessarily aligned to my work. She recognized that I would be more likely to succeed in a new role focused on innovation and emerging technology, which happened to align with the emergence of AI. Finally, she taught me that setting employees up for success in the right roles is an essential leadership capability.

As with any career path, the AI industry comes with its own set of challenges. Could you elaborate on some of the significant challenges you faced in your AI career and how you managed to overcome them?

Determining how and where to specialize in AI has been a challenge. Understanding when it’s acceptable to be a “generalist” as opposed to a “specialist” can also be difficult to navigate. The rate of change in this field requires significant time to stay aware of the “state of the art”. I tend focus on one challenge at a time that’s top of mind.

Ok, let’s now move to the main part of our interview about AI. What are the 3 things that most excite you about the AI industry now? Why?

The three most exciting aspects of the AI industry, in my opinion, are as follows:

1. It is accessible and open. As opposed to technologies with significant barriers to entry (cost or skill wise), AI is widely available and directly exposed to a wide population of both experts and laypersons. This should drive more accountability — as opposed to technology developed and progressed behind the scenes. Accessibility and openness are driving public discussions on use.
2. It is dynamic. As opposed to one monolithic leader that everyone is following, hundreds of models that provide unique, robust capabilities –from the leading tech companies, to startups, from open source to academia, are impacting the industry. This ecosystem gives our clients a wide range of options in terms of costs, architecture, and capabilities.
3. It is being met with healthy skepticism. The responsible use of A is central to every new development. This technology is expected to not carry forward biases; it is expected to be applied in a fair and equitable way, and it is expected to be applied for beneficial purposes.

What are the 3 things that concern you about the AI industry? Why? What should be done to address and alleviate those concerns?

1. The Hype. Lots of companies are jumping on the AI bandwagon. Any hyperbolic claims should be met with skepticism — as we saw in biometrics, bad actors can undermine careful and conscientious development of technology.
2. Responsible Use of the Technology. We have seen that, in the rush to release new technologies, not all companies or organizations are living up to their responsible AI commitment. With any new capability, we need to ask, ‘Where did the data come from?’ ‘How was it tested?’ ‘How does it work for every user, not just the average user?’
3. The Need for Inclusivity. AI technologies need to be conceived of, vetted, developed, and operationalized with inclusive perspectives by technical teams that represent a variety of life experiences. This requires conscientious and deliberate decision-making, even in the haste of pushing out new products and capabilities.

For a young person who would like to eventually make a career in AI, which skills and subjects do they need to learn?

My degree is in political science, not technology. I don’t think any specific subject is necessary. Someone interested in AI policy will have one track, a person who wants to develop AI algorithms will have another, and a person who wants to analyze the root causes of AI bias will have another. The idea that certain skills are a prerequisite to an AI career is perhaps a type of gatekeeping that works counter to beneficial and inclusive use.

As you know, there are not that many women in the AI industry. Can you advise what is needed to engage more women in the AI industry?

I can only share what I observe first-hand. The AI team I work with at Accenture Federal Services is comprised almost entirely of women, both in technical lead and sales & growth roles. The team is exceptional and representative of a diverse array of backgrounds and mindsets.

That said, I understand the structural impediments to inclusion and diversity in AI, not just in terms of gender, but ethnicity, cognitive aspects, etc. AI is not just about developing the most advanced models through the most arcane technical approaches. It is grounded in the human experience, and, as such, can only succeed when diverse perspectives are integrated from conception to delivery.

Ethical AI development is a pressing concern in the industry. How do you approach the ethical implications of AI, and what steps do you believe individuals and organizations should take to ensure responsible and fair AI practices?

Long before Generative AI captured the public’s imagination, Accenture Federal Services established a thriving practice in responsible AI. The foundational tenets of our approach, that work well across technologies and industries, are listed below.

• Organizational Responsibility: Create and encourage an organizational culture that empowers individuals to raise doubts or concerns with AI systems, without stifling innovation.
• Operational Responsibility. Establish transparent, cross-domain, governance structures, identifying roles, expectations, and accountability to build internal confidence and trust in AI technologies.
• Technical responsibility. Architect and deploy AI models, systems, and platforms that are trustworthy, fair, and explainable by design.
• Reputational responsibility: Articulate your responsible business mission, anchored in your principles, and informed by brand, public risk assessments, and guidance.

This framework has proven effective even with the advent of large-scale Generative AI technologies such as ChatGPT and DALL·E.

Ok, here is the main question of our interview. Can you please share the “Five Things You Need to Create a Highly Successful Career in The AI Industry”?

1. “Skeptical curiosity” of AI technology. Or maybe we can think of this as “curious skepticism”. In either case, a precondition of success in AI is being interested in the technology and its applications. This may be in the form of ideating on new problems that AI can solve or ways that AI can empower or assist people. You want to have a stake in what’s next and what’s emerging. At the same time, developments need to be taken with a grain of salt, and the default position should be to wait for proof or evidence, otherwise you are just blindly advocating for or accepting a technology that might not meet its promise. The recent emergence of Generative AI is a good example of this, where we have seen positions that focus solely on revolutionary impact, and positions that focus solely on unintended outcomes. In the long run, you need to be able to balance both viewpoints.

2. The ability to focus on your area of specialization and understand where it fits. The AI industry is broad, complex, rapidly changing, and expanding. Everyone working in the industry ends up specializing in one area with its own language or taxonomy, addressing some small part of the overall AI landscape (related to technology, market and investment, societal implications, etc.). The ability to collaborate and communicate clearly with colleagues who work in other AI domains is extremely beneficial, as it gives you a more well-rounded understanding of the overall direction of the industry. It also sets you up for success as you work in organizations where information or expertise may be stove-piped and needs to be shared in a consumable fashion. At Accenture Federal Services, we have AI experts ranging from deep technologists to long-term market visionaries. One of my roles is to coordinate and guide these groups of experts to prioritize our activities in a way that’s best for our federal agency clients.

3. The ability to see the “big picture” in context and understand other stakeholder perspectives. At Accenture Federal Services, I am active in the development of AI standards at the U.S. and international level. In this role, I interact with AI practitioners from around the world, both technical and non-technical. I continually remind myself that there are diverse perspectives about the role of technology in society, the relationship between government and industry, and the cultural norms that shape the use and adoption of new technologies such as AI. This shapes decisions on what types of AI usage is acceptable and desirable.

4. The ability to embrace uncertainty and ambiguity. Many AI disciples are characterized by uncertain and unpredictable outputs and behaviors. There are clearly situations where ambiguous or unexpected outcomes are not acceptable and must be rooted out, such as in the use of AI for autonomous driving. However, in other use cases, such as text generation for analysts and describing visual patterns in imagery, outputs are potentially uncertain or ambiguous, and require interpretation, context, and a human remaining in the loop. Being able to work with uncertainty and to properly contextualize and manage risks is essential.

5. The ability to see and empathize with the human dimension of AI. Emerging areas of AI, such as Generative AI, are differentiated by their direct, non-intermediated interaction with massive numbers of end users. While the promise and appeal of technologies like ChatGPT is immediately clear, the release of progressive more powerful GPT models has also had unintended and undesirable outcomes. The ability to understand and positively impact the human dimensions of AI — such as usability, accessibility, and inclusiveness — will be a precondition of long-term success for the industry. As opposed to being overlooked or de-prioritized, addressing the human dimension of AI will be integral to careers. This means that a wider range of skills, backgrounds, and disciplines will be required for a holistic perspective on AI technologies.

Continuous learning and upskilling are vital in a dynamic field like AI. How do you approach ongoing education and stay up to date with the latest advancements in the AI industry? What advice do you have for those looking to grow their careers in AI?

Because this technology moves very quickly, it’s best to find a few curated feeds or reader lists that provide filtered information, whether from industry or academia. This will help weed out the noise of things like press releases and marketing material. Second, it’s useful to have a peer group to talk with about the implications of various developments and new capabilities for your organization or team. This peer group can help contextualize and determine what is impactful.

More formally, the AI learning tracks are available with cloud providers like AWS, Azure, and Google are always useful, and can be calibrated from the practitioner level to data scientist or application engineer.

How can our readers further follow your work online?

The work that my team and I are doing in delivering innovative, disruptive AI capabilities for U.S. federal agencies is showcased on the Accenture Federal Services website.

This was very inspiring. Thank you so much for joining us!

About The Interviewer: David Leichner is a veteran of the Israeli high-tech industry with significant experience in the areas of cyber and security, enterprise software and communications. At Cybellum, a leading provider of Product Security Lifecycle Management, David is responsible for creating and executing the marketing strategy and managing the global marketing team that forms the foundation for Cybellum’s product and market penetration. Prior to Cybellum, David was CMO at SQream and VP Sales and Marketing at endpoint protection vendor, Cynet. David is a member of the Board of Trustees of the Jerusalem Technology College. He holds a BA in Information Systems Management and an MBA in International Business from the City University of New York.