As a political savant and leader in policy and legislation at the helm of Government Curated, Donald Gainsborough possesses a unique perspective on the forces shaping Pennsylvania’s future. With a massive $90 billion in AI-related investment poised to enter the commonwealth, Gainsborough offers a clear-eyed analysis of this transformative moment. Our conversation explores Pennsylvania’s strategic advantages in the AI race, the practical steps businesses are taking to integrate this technology, and the critical balance between innovation and security. We delve into real-world applications, from revolutionizing healthcare documentation to creating massive efficiencies in the legal field, and consider what this technological shift means for professionals at every stage of their careers.
The article highlights a coming $90 billion AI investment and points to Pennsylvania’s unique energy and academic resources. Beyond these advantages, what specific initiatives are underway to secure this investment, and what practical, first steps should a small business owner take now to prepare for this shift?
The excitement is palpable, and for good reason. The promise for Pennsylvania is greater than for any other state, and we’re not just waiting for it to happen. A key initiative is fostering robust public-private partnerships. For example, the Pennsylvania Chamber of Business and Industry has already teamed up with Google to hold seminars across the state. This isn’t high-level theory; it’s hands-on, practical training designed specifically for small businesses to understand and apply AI. For a small business owner wondering where to begin, my advice is simple: seek out these resources. The first step is not to become an AI expert overnight, but to, as we’ve been saying, understand its capability and then embrace it. Go to a seminar, talk to your peers, and identify one or two processes in your business that could be streamlined. That’s the entry point.
Jefferson Health aims to reclaim 10 million clinician hours by 2028. Could you detail the implementation process for the ambient AI listening tools, from pilot to rollout? What were the biggest hurdles you faced in training staff and achieving the reported 77% improvement in job satisfaction?
The Jefferson Health story is a masterclass in targeted AI implementation. They didn’t try to boil the ocean; they focused on a massive pain point: the documentation burden that pulls clinicians away from patients. The process started with identifying ambient AI listening tools that could passively monitor a conversation between a doctor and patient. The real magic is that the AI is smart enough to extract the medically relevant information and place it directly into the patient’s chart. The initial rollout was likely a controlled pilot with a group of early adopters to work out the kinks. The biggest hurdle in these situations is rarely the technology itself, but the human element—building trust. You have to prove to dedicated professionals that this tool will make their lives easier and won’t compromise patient care. The fact that 83% of clinicians say it reduces their documentation burden and 66% feel it improves note accuracy shows that trust was earned. Achieving a 77% jump in job satisfaction is a phenomenal outcome, a true win-win-win for the patients, the caregivers, and the entire healthcare system.
The piece discusses how AI can dramatically boost efficiency while raising data privacy concerns in sectors like law and finance. What specific criteria do you use to vet a “closed system” AI tool, and what does your step-by-step training process look like for ensuring your team uses it responsibly?
This is the most critical question for any professional services firm. The core criterion for vetting an AI tool is ensuring it is a truly “closed system.” This means we need absolute certainty that the data we input is not being used to train the model for other users and that it is completely siloed and secure. We scrutinize the service agreements and technical specifications to confirm that our client’s sensitive information cannot, under any circumstances, be accessed by others. Our training process is methodical. First, we educate the team on the inherent risks of generative AI, showing them examples of data leakage from open systems. Second, we provide hands-on training exclusively with the vetted, closed-system tool, like the CoCounsel platform mentioned. Finally, we establish clear protocols: what type of data is permissible to use with the tool, how to review AI-generated summaries for accuracy, and how to maintain ultimate human oversight. It’s about building a culture of responsible use, not just a technical checklist.
The article quotes a 68-year-old lawyer who warns that avoiding AI means being “left behind.” For other experienced professionals who might be hesitant, could you describe your personal “a-ha” moment with this technology and the practical steps you took to start integrating it into your daily work?
I completely identify with that sentiment. As someone who has been in this field for decades, it’s easy to be skeptical of the next big thing. My “a-ha” moment was very similar to the one described by Mitchell Kaplan. I was given a demonstration where a paralegal had spent the better part of a day summarizing a massive trove of discovery documents. We then fed the same documents into an AI platform. In about 15 minutes, it produced a summary that was not only faster but, frankly, more comprehensive because it could identify trends and connections a human might miss after hours of reading. The feeling was not one of being replaced, but of being empowered. My first practical step was to identify the most time-consuming, low-value task I did regularly—in my case, drafting initial summaries of proposed legislation. I started using AI for that first draft, which I would then edit and refine. It freed up hours for higher-level strategic thinking. For any hesitant professional, that’s the path: find one painful, repetitive task and let the technology show you what it can do.
What is your forecast for Pennsylvania’s AI landscape five years from now, specifically concerning job creation versus job displacement for the average worker?
My forecast for Pennsylvania in five years is one of profound transformation, not simple replacement. The narrative of “robots taking our jobs” is too simplistic. With $90 billion in investment flowing in, we are going to see a surge in job creation in areas that support this new economy: building and maintaining data centers, AI development, and cybersecurity roles to protect these new systems. For the average worker, the key word will be augmentation. We will see more stories like the clinicians at Jefferson Health, who aren’t being replaced but are being freed from tedious work to focus on the human-centric parts of their jobs. Of course, roles that are purely repetitive and data-driven will face displacement, but this will be countered by the immense need for a reskilled workforce. The focus will shift to continuous education and training, empowering people to work with AI. Five years from now, AI proficiency will be seen less as a specialized skill and more as a fundamental component of professional literacy, much like using a computer is today.
