Par ChadhaHandsOn Global Management is the brainchild of Founder, CEO and Chief Information Officer, John Hands. He has spent decades building and scaling tech-enabled business. He founded HandsOn Global Management, Inc. in 2001 and has invested in big data, cognitive technologies, artificial intelligence, and robotic process automation. He was also the co-founder of Rule14, and served previously as chairman and executive chairman of Exela Technologies as well as Chairman SourceHOV.
HandsOn Global Management (HGM) Limited It is a tech-driven business that combines capital investment with operational experience to scale and build technology-enabled companies. It is focusing on AI healthcare services such as revenue cycle management and medical coding. The company also uses AI to automate and optimize enterprise workflows. HGM describes their model as an amalgam of a diversified services company and investment firm, which uses technology integration and operations execution to drive long-term revenue growth in markets like North America.
HandsOn Global Management, which you founded in 2001 before AI became a popular investment topic, is a company that has remained steadfastly committed to its mission. Why did you start the business? Has its investment philosophy changed as a result of advances in AI, workflow automation and data intelligence?
HandsOn is how I see the organization. If you cannot be hands-on—if you are hands-off—you don’t belong in our organization. My expectation is that those who lead businesses and myself will practice servant leadership. We have a duty to the people we work with and assist them in becoming more successful.
The technology has evolved tremendously but the question that remains is: What does it allow businesses to achieve better with the new technology?
Flex capacity is what I always refer to when I think about automation. When automation enables a person who can manage 100 transactions to also handle 250 with the help of an automated system, it creates a capacity for growth. This makes the company more profitable, scalable and flexible.
AI takes this a step further. AI workflows are being developed that orchestrate many tasks with the help of people, and AI agentics. I believe that these types of businesses will be very in demand. They are also some of the most disrupting companies.
The technology may have evolved but the philosophy is still the same: Understand the problem and where you can create productivity. Turn the technology into business.
How can you, both as CEO and CIO, tell the difference between an AI firm that has the potential to be a long-term business versus one that benefits primarily from short term excitement about the technology?
I make the difference based on whether the investment can be turned into a profitable business. If you don’t, then investment is just an expense.
Intellectual property, or new technology can generate a great deal of interest. But someone has to make use of it. This product or service must be able to generate revenue, solve some problem and have an attractive value proposition.
Take automation as a example. Also, you need to be aware of the functions that are being automated. These functions are quite different. Automating the business must take into account what it does. The needs of a gas station are very particular. The needs of a restaurant are very specific.
The companies that last will be the ones where customers can see the difference—more profitability, more scalability, more capacity, or the ability to do something they could not economically do before. It is crucial that the technology becomes a viable business.
HandsOn Global Management is a company that takes a hands-on approach to building, buying, and transforming ecommerce companies. This hands-on model changes the way that you view AI investments in comparison to a venture capital or private investment model.
It’s not my style to tell others what to do. I have always believed that people working for you are not successful because they lack certain skills. Your job is to fill in those gaps.
When we consider an investment we think operationally. Who will run it? What is its position? What are its customers and partners like? What’s the value proposition for your customers and partners? It can it be scaled?
It is important that the person in charge of operations takes the lead. I’ll introduce the person, then let them take it over. They can stay copied and available. We do not see owning an asset and running a company as two totally separate things.
AI has been adopted by some enterprises in mission-critical areas, while others have not. What distinguishes those organizations who move beyond isolated pilots and into production-scale adoptions?
The organization’s willingness to change is a major factor.
Technology can be faster than people. People are coming out of college who have a good understanding of AI and agents. They can be productive quickly. People who have had five, ten, or twenty years experience may find it hard to shift from their comfortable zone into the new. The same thing happens in America, India, Philippines, and Europe.
It is not only a problem of technology, but also an issue for the organization.
The company that makes the switch will change their workflow. You can change the workflow once you start orchestrating different tasks using people and AI agentics.
Someone else will do it if companies fail to make the shift. Do or die, I believe. Companies that are disruptive will be able to move ahead, while those that do not will face disruption.
HGM’s Healthcare initiatives include voice and Conversation Intelligence, Predictive Analytics, AI Agents that work across administrative workflows and clinical workflows, as well as autonomous medical coders. What areas do you think AI could reduce the most friction in healthcare, without compromising patient privacy or accuracy?
We digitized certain functions in the healthcare industry, but did not connect them all. Claims management, enrollment, pharmacy, care-management, provider services and analytics can be run as standalone systems. It creates inefficiency and higher costs.
No, I do not think that the solution is just to install another program. The intelligence has to be integrated into the workflow. Data, processes, and people can be brought together to allow AI to understand and predict future events, suggest actions, and automate work which can be done.
The first place to begin is with administrative work. The administrative burden, the manual process and other issues still plague providers. You can create more care time and capacity for professionals if you use technology to simplify workflows.
In healthcare, trust is vital. The architecture must include security, governance and compliance. The technology should reduce complexity and not increase it. AI is not the goal. The goal is to provide better care and lower costs.
How can companies decide which decisions should still be made by humans in industries like healthcare, finance, insurance, or government?
The line that separates us is accountability.
In businesses that are regulated, someone is ultimately accountable for the information being presented. Cybersecurity is one example where regulators might require boards and management to share information as quickly as possible. Senior management could be required to sign official statements. Problem is, nobody can know everything so quickly.
AI brings together tools, creates a baseline and organizes information. It can make processes more efficient. This does not mean that accountability is gone.
A good example is healthcare. AI has the ability to detect, predict, automate and recommend. But not all functions are equally risky. Architecture must recognize when automation is right and when people should remain involved. Governance, accountability and explainability become more critical the more significant the decision.
It’s not a question of choosing between fully autonomous AI or completely manual work. This is about where AI will be most effective and where humans still need to make decisions or back up the outcome.
Rule14 is a platform that allows users to monitor, summarize, and analyze information in real-time. What has changed in the data mining field since large language models, generative AI and other AI technologies were introduced? And what data-related challenges remain to be resolved?
The use of large-scale language models can make the informational work more flexible.
In businesses, hundreds of professionals and lawyers work on content and information. They have historically used legacy tools rather than big language models. I am interested in how to take platforms that were built with one goal, package them properly and then use them at scale for something else.
A model cannot simply summarize something. It is possible to change how information in large quantities are packaged, processed and delivered.
However, I wouldn’t assume you already know it all just because you use a huge language model. Information is still important, but in reality it can be unreliable or not available when you want to use it. These models help us be more productive but do not remove the importance of knowing what data you have.
HGM’s strategic ecosystem includes organizations working in autonomous medical coding and ambient clinical documentation. It also encompasses companies that work on payments, data-processing, AI platforms, and intelligent workflows. How can you foster meaningful collaboration between companies in a portfolio, without forcing them into an artificial ecosystem that is forced to exist?
Use each business for its best qualities.
In cybersecurity, I referred to this as a small club. The other companies have expertise in different fields, but we are the ones who provide the service. Each company is used to its fullest potential. They get to expand into our customer base, while we benefit from what they do best.
This is not the same as saying that every company must use all other companies because they are in the portfolio. This is about combining capabilities to create something that the client can actually use.
In my own company, I use a similar strategy. I make sure the correct people are in place and then let the operator lead. Then they decide what can be done together. They also look at the budgets and whether the project belongs on the roadmap.
Collaboration must create value. There is no need to force collaboration.
What have you learned about adapting AI products to different regulatory environments, business cultures, languages, and levels of digital maturity? What lessons have you learned from adapting AI to diverse regulatory environments, cultures of business, languages and digital maturity levels?
Personal relationships and culture are both important.
It is impersonal to know someone only by their office phone number or through email. This may be fine when things are normal. Knowing the person you will be dealing with is important when building or expanding something, fixing it, or having trouble.
You learn that not all markets are the same when you travel. Travel teaches you that markets are not all the same. You can learn about what people like to eat and how they live, their sports, schooling systems, cultural sensitivity, or what causes unnecessary barriers. You can reduce the distance between you and your partner by learning more about their country, people, and lifestyle.
Similarities in technology exist. The markets have different stages. Some countries may be ahead of the U.S. when it comes to certain forms of professional services while we could be ahead on other types.
The same model cannot be used everywhere. Understand how this market operates and develop relationships with people who use the product.
What opportunities are you underestimating as agentic AI and cognitive automation continue to merge with physical AI? And what advice do you have for founders who want to build companies that will be ready for the next stage of automation in this new era?
I think AI-based workflow is going to be in very, very high demand—the orchestration of a variety of different tasks with people in the loop and agentic AI in the loop. They are the ones I expect to have the biggest impact.
It will also affect a wider range of people than expected. Most people think that automation only affects lower-level workers. I think it often affects the middle-layer. Middle layer is less necessary for senior people because they can accomplish more. Automating coding, testing and data entry, as well as data analysis is becoming more common. Workflows and dashboards also reduce the number of people needed to perform these functions.
You have to consider the physical aspect. The automation of restaurants is increasing. Robots, drones, and autonomous machines are replacing people who used to be needed during the harvesting or planting season. This landscape is rapidly changing.
This transition will be painful. We need to be fewer in order for productive engines to work. We will not be able compete against countries who are much larger and with different cost structures. If we can get this right, then I believe that it could be a start. a new American century.
Focus on workflows and productivity when you’re a founder. Develop something that will become part of the workflow, identify where your people belong, and realize that those organizations who adopt the technology you provide are going through an important workforce shift.
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