C-Suite Perspectives On AI: Tim Kane Of Across America Insurance Services On Where to Use AI and Where to Rely Only on Humans
An Interview With Kieran Powell
Consider the savings. Lots of things sound effective and seem efficient, but what are the cost impacts? Unless you are dealing with extremely high volume, implementing AI that saves a few seconds or a minute per transaction is probably not worth the effort. The technology might be great, but if it’s not going to save you something noteworthy — be that time or money, it might be best to focus on improvements in other areas.
As artificial intelligence (AI) continues to advance and integrate into various aspects of business, decision-makers at the highest levels face the complex task of determining where AI can be most effectively utilized and where the human touch remains irreplaceable. This series seeks to explore the nuanced decisions made by C-Suite executives regarding the implementation of AI in their operations. As part of this series, we had the pleasure of interviewing Tim Kane.
Tim Kane is Chief Operating Officer for Across America Insurance Services, a specialized wholesale insurance brokerage serving the commercial trucking and transportation industry. With nearly 25 years of experience in the insurance industry, Tim manages Across America’s IT systems, workflow, data analysis and more. Tim is an expert in the ConceptOne operating system and has worked with many insurance companies to implement the system into their organizations.
Thank you so much for your time! I know that you are a very busy person. Our readers would love to “get to know you” a bit better. Can you tell us a bit about your ‘backstory’ and how you got started?
I joined the insurance industry in 1998 when I was hired to implement a software system for an insurance company in Ft. Lauderdale, Florida. The deal fell through shortly after I began because the vendor wouldn’t convert our data. This challenge inspired me to find a new software application.
In my approach to find a new software application, I started the requirements gathering process from scratch. While I was given three main criteria that the new system had to support, each member of the team added their own critical needs, leading to a lengthy requirements document. Ultimately, we narrowed our list of vendors down to three and selected ConceptOne as our agency management system. After finding ConceptOne, I joined the implementation team for the insurance company and ran their IT department for nearly three years before moving to Phoenix to work for the company that developed the application. I worked there for nearly four years before moving into consulting for the next 18 years. I joined Across America Insurance Services (AAIS) about seven years ago and took on the COO role about a year and a half ago.
It has been said that our mistakes can be our greatest teachers. Can you share a story about the funniest mistake you made when you were first starting? Can you tell us what lesson you learned from that?
It is important to find humor and improved learning in our mistakes rather than dwell on them. I often find humor in miscellaneous typos that can appear in my work. Those mistakes, while minor and humorous, have taught me to be more detail oriented and careful in my proofreading.
As important as it is to not take ourselves too seriously when small mistakes occur, it is just as important to learn from those mistakes and apply them going forward. One mistake I made that, while not particularly humorous, carried an important lesson was logging into the server to run a particularly large and slow query. At the time our software had logic in it that would check the system date and perform updates based on when you first logged in every day. In this instance, the data on the server was set to years in advance, triggering an update which expired every policy on the server and updated other unnecessary projects. We had developers from the vendor working overnight at home to undo the update. In the end, we had to restore from backup and reapply transactions which had the entire company down the following day. While the mistake had major implications, it did lead to learnings for the vendor, prompting them to write logic into the server to prevent such an update from happening again.
Are you working on any exciting new projects now? How do you think that will help people?
Most of the projects I find exciting have to do with automation. I enjoy taking a process that someone does and finding a way to automate it or make it more efficient. Such automation typically involves freeing someone up from mundane or repetitive tasks. Types of automation I have worked on include integrating applications to eliminate duplicate data entry or having the system generate a report in the format someone needs so they don’t have to cobble the data together from multiple sources. Ultimately, I enjoy automating processes that free people to do more interesting, fulfilling work.
Thank you for that. Let’s now shift to the central focus of our discussion. In your experience, what have been the most challenging aspects of integrating AI into your business operations, and how have you balanced these with the need to preserve human-centric roles?
From my perspective, the most challenging aspect of integrating AI is finding the right AI to integrate. For instance, 10 years ago every software vendor was touting blockchain because it was trending. In the moment, it feels as though that is where AI is now. And while I feel like the push towards AI has more legitimacy and staying power than blockchain did, how many of the hot new AI products out there will be around in 5 years? It is important to look past the “trends,” to what technology and AI applications are sustainably effective.
As far as balancing the need to preserve human-centric roles, we have yet to see AI that is ready to replace entire roles. We have seen success using Chat GPT to refine letters, compose emails and complete other administrative tasks, however even that application still requires a human to ask the questions and then receive and refine the results. While we are in the course of implementing AI as part of our underwriting process, we are adamant about not removing the underwriter entirely. Instead, we are focusing on the periphery, using AI to augment the underwriter’s skillset and automating the tedious aspects of their job like reviewing prior year claims data and driver motor vehicle records (MVRs) both of which come in numerous different formats.
Can you share a specific instance where AI initially seemed like the optimal solution but ultimately proved less effective than human intervention? What did this experience teach you about the limitations of AI in your field?
One of the more popular spaces in the insurance sector where AI is being discussed is underwriting, but I have yet to see AI that is advanced enough to input all the data (i.e. the name of an individual or business and the coverage for which you want) and get an accurate or complete rate. For AI to be able to complete underwriting from start to finish would be the proverbial holy grail. Instead, we are left with applications that can apply AI to a specific part of the underwriting process to augment an underwriter, but not completely replace an underwriter.
I can’t say I’ve run into a situation that ultimately proved less effective, but only because we’re taking a very slow and measured approach to AI. While the AI itself might be effective, it is not always effective in other areas. Key questions to ask when considering AI include: is it cost effective? How much time is it actually saving in the process? How much will it cost to implement? Software vendors often gloss over the difficulty in implementing their solution into an existing business process. In addition to the limitations of what is still new technology, I would say the limitations surrounding the implementation of that technology can be significant. You should carefully consider what savings the AI will bring in to make a process faster, more efficient or less error-prone and weigh that against the cost to purchase or develop the software and integrate it into an existing business process.
How do you navigate the ethical implications of implementing AI in your company, especially concerning potential job displacement and ensuring ethical AI usage?
That is a question people will be debating for decades to come. We are a growing company in an industry that struggles to find good talent. As a result, our focus will be more on using AI to stretch the resources we already have rather than replacing any employees. From my perspective, AI can augment an employee, allowing them to do more challenging and rewarding work while letting AI aid in the more administrative tasks. However, the day will come when ethical implications will become a real dilemma. One could foresee a time in the not-too-distant future where the cumulative effects of employers augmenting existing workers leads to a reduction in overall hiring. Half an employee here and half an employee there eventually adds up to jobs being done by AI that would have been done by humans.
I don’t see ethical issues using AI to do things such as aiding in data analysis, but I do see this as an area of concern, particularly in the creative fields. AI can produce works of art that mimic the style of original artists — be they painters or even authors. The usage of original works to train AI raises ethical considerations when the authors of those original works are not compensated. For instance, the recent New York Times lawsuit against OpenAI for the use of their copyrighted material to train AI underscores this issue. AI needs vast amounts of data to learn, and while the end product might be useful, many would argue that the ends do not justify the means.
At Across America, we have no plans to use AI to create new material beyond emails, marketing documents or other such minor projects. In an insurance environment, such materials would be fairly standard. However, if we were in a creative field and using AI to generate material, I’d be more concerned about the ethical implications of the AI using source material in an unethical manner in order to generate so-called “new” material.
Could you describe a successful instance in your company where AI and human skills were synergistically combined to achieve a result that neither could have accomplished alone?
We are testing different solutions so I’m not at liberty to discuss details, but one application that appears promising is with data analysis. We receive information that is incredibly similar from one submission to the next, but the format can vary greatly. It’s time consuming and tedious for one person to review, interpret and rekey that information while AI can accomplish the same task at a much faster rate and with greater accuracy. We still have humans review the information, but it’s been both standardized and digitized so team members don’t have to interpret it or rekey it. Using AI to augment a human role drastically cuts the time required — especially on larger submissions which could have hundreds of records to review.
Based on your experience and success, what are the “5 Things To Keep in Mind When Deciding Where to Use AI and Where to Rely Only on Humans, and Why?” How have these 5 things impacted your work or your career?
- Consider the savings. Lots of things sound effective and seem efficient, but what are the cost impacts? Unless you are dealing with extremely high volume, implementing AI that saves a few seconds or a minute per transaction is probably not worth the effort. The technology might be great, but if it’s not going to save you something noteworthy — be that time or money, it might be best to focus on improvements in other areas.
- Consider the cost. Most software is expensive and (arguably), good software almost never comes at a low cost. While the development cost or purchase price can be significant, don’t overlook the recurring costs as well. If the software you are purchasing has recurring costs take a hard look at what those recurring costs are, but also the maximum annual increases specified in your contract. Ultimately, the recurring costs will increase and how fast they do so should be part of the equation.
- Consider the impact. While the purchase price and recurring costs can be notable, there are other costs to consider as well. You might have to upgrade ancillary software or purchase additional hardware. Implementation costs and the “soft” costs of things like retraining, testing and modifications to the existing business process can also add up quickly causing workplace disruptions that many don’t account for when planning projects.
- Beware of the Hype. Go to any technology conference now and a large portion of the vendors purport to have AI, but I think they are stretching the definition of the word. An elaborate algorithm is not AI, so if someone was looking for an off the shelf product, I would suggest they dig deeply into the vendor’s claims of AI to see if they measure up. The product may very well be useful, but a lot of what is called AI doesn’t fit the true definition and it can be difficult to tell what’s hype and what isn’t.
- Humans are vital. If you get past the first four items and implementing AI still seems like a good idea, consider the people that make up your organization. Software in general does well when everything works perfectly, but humans are adept at working on the fringes and they are masters at adaptation. If your scenario has a lot of outliers, consider if human intervention will be required and if so, how much intervention will be required and what will it look like within the new process you’re creating.
At some point, it will still make sense to use AI or technology to replace the job someone is doing. Don’t lose sight of the fact that there is a person behind the job. That person has bills to pay and maybe a family to support. If a job is going to be eliminated, be transparent, plan ahead and do your best to upskill the person or find another role in which they can be successful. If a person does have to be let go, consider using a portion of the ROI to help them have a successful transition to another job.
Looking towards the future, in which areas of your business do you foresee AI making the most significant impact, and conversely, in which areas do you believe a human touch will remain indispensable?
AI has great potential for both data analytics and the automation of tasks. On the data analytics side, AI can be used for both the standardization of data sets and in forecasting. AI can efficiently analyze large sets of historical data to predict future results. On the underwriting side of insurance, AI can be used to review past policies and the resulting claims to analyze pricing adequacy. Similarly in claims, looking at prior claims that share the same attributes of an existing claim to project the cost for the existing claim or even analyzing the text of phone calls, emails and notes to gauge the claimant’s sentiment. AI will be able to do those things very well, but a human touch is still necessary on the front lines dealing with customers and claimants. It’s generally the software that makes customers upset. At Across America we don’t believe more software will alleviate those types of frustrations.
You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger. 🙂
For your audience I would encourage a movement to educate people on matters of personal finance. I would require every student to complete a personal finance course before graduation. My course would emphasize things like budgeting, saving, retirement planning and investing with a focus on the miracle (or curse) of compound interest. I don’t have any kids, but for my nieces and nephews, I started much earlier than their 18th birthday!
How can our readers further follow your work online?
You can follow both my work and what is happening at Across America Insurance on our social media accounts including LinkedIn and Facebook.
This was very inspiring. Thank you so much for joining us!
About The Interviewer: Kieran Powell is the EVP of Channel V Media a New York City Public Relations agency with a global network of agency partners in over 30 countries. Kieran has advised more than 150 companies in the Technology, B2B, Retail and Financial sectors. Prior to taking over business operations at Channel V Media, Kieran held roles at Merrill Lynch, PwC and Ernst & Young. Get in touch with Kieran to discuss how marketing and public relations can be leveraged to achieve concrete business goals.
C-Suite Perspectives On AI: Tim Kane Of Across America Insurance Services On Where to Use AI and… was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.