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Artificial Intelligence: Your New Digital Overlord?
What movie did you watch over and over as a kid? For me, it was Star Wars. I remember going to the theater and watching it probably 3 times in a row, and doing that for many weeks in a row. I was fascinated by the story line, the special effects, and it all seemed so real. Who didn’t want to be as cool as Hans Solo, or have C3PO as their trusted friend?
The craze around Artificial intelligence (AI) reminds me of the craze around Star Wars. AI is taking the insurance and general business world by storm, promising to revolutionize everything from supply chains to customer service. But is this clever new technology a savior or the Empire in disguise?
In this overview, we’ll explore what AI is, what it definitely isn’t, and how leaders like you can start harnessing its power without accidentally enabling the robot uprising.
What is AI Anyway?
At its core, AI refers to computer systems that can perform tasks normally requiring human intelligence, such as visual perception, speech recognition, and decision making. Unlike standard software that relies on predetermined rules, AI leverages machine learning algorithms that get better over time by processing data and experiences.
#AI is not that guy from your college dorm who thought he knew everything. Think of it as your digital butler, always ready to help, but without the British accent (unless you program it that way). From recommending your next binge-worthy Netflix series to helping doctors diagnose diseases, AI is everywhere.
What it is Not!
Before you start thinking AI can whip up a gourmet meal like the <strong>Foodarackacycle</strong> machine from the Jetsons, or replace your yoga instructor, let’s set some boundaries. AI isn’t magic. It can’t predict the future (so no lottery numbers, sorry), and it’s not about to start an emotional relationship with you. It’s a tool, not a sentient being. So, if you’re hoping for an AI best friend, you might be waiting a while.
AI capabilities typically fall into two main buckets:
<strong>Narrow AI</strong> – Also known as “weak AI,” this type focuses on excelling at specific, well-defined tasks like playing chess, translating languages, or identifying objects in images. No need to worry about Terminators here…yet.
<strong>General AI</strong> – Referred to as “strong AI,” this human-level intelligence does not exist, though technologists aspire to create it one day. Don’t let the hype fool you; we are decades away from a HAL 9000 or C-3PO.
Current business applications leverage narrow AI to enhance a wide range of functions:
- Predictive analytics – Identify risks/opportunities from data patterns
- Computer vision – Automate visual inspection and quality control
- Natural language processing – Parse written and verbal communications
- Virtual agents – Automate customer service and support
While impressive, today’s AI has some major limitations:
- Brittle – Algorithms break when given unfamiliar data
- Narrow – Specialized to specific tasks vs general intelligence
- Opaque – Difficult for humans to interpret underlying logic
- Biased – Perpetuates systemic prejudices found in data
- Costly – Requires huge data sets, computing power, natural resources like water, and talent
As AI hype heats up, it’s important to separate facts from fiction when assessing potential.
Rise of the Machines? The Dangers of AI
While AI has its perks, it’s not all rainbows and unicorns. For instance, if you’ve ever been bombarded with ads for that one pair of shoes you looked at once, you can thank AI for that. Overzealous marketing, biased algorithms, and the occasional robotic hiccup can sometimes make AI seem like that overeager friend who just doesn’t know when to quit.
It’s tempting to imagine that AI will rapidly ascend toward superintelligence, resulting in systems that turn hostile toward puny humans. But before you start prepping your bunker, let’s discuss how current AI could go wrong:
<strong>Unfair outcomes:</strong> AI systems fed biased data can perpetuate and amplify discrimination in areas like employment, lending, insurance, and criminal justice. For example, resume screening algorithms have been shown to preferentially recommend male candidates.
<strong>Dangerous failures:</strong> Deep learning algorithms are vulnerable to unpredictable mistakes and adversaries. For instance, adding stickers to a stop sign could cause an autonomous vehicle to misclassify it, with potentially fatal consequences. How would you price that in your actuarial models?
<strong>Job losses:</strong> While AI will generate new roles, it could automate certain jobs out of existence. According to one estimate, 30% of activities across most occupations could be automated using current AI capabilities. Leaders will need to examine how workforce needs shift.
<strong>Loss of control:</strong> There are fears that super-intelligent AI could become uncontrollable by humans. But today’s AI lacks basic reasoning skills needed for independent initiative or deception. For the foreseeable future, AI will remain simplistic and require extensive human supervision.
The dangers are real but often exaggerated in mainstream depictions. With proper safeguards, companies can minimize risks as they integrate AI capabilities.
AI Governance: Managing Risk and Ethics
AI might be smart, but it still needs boundaries. Think of it as a toddler with a crayon. Without guidelines, you’re going to have scribbles all over your walls. Ensuring AI adheres to regulations and is used ethically is a must. Unless you fancy a chat with regulators over your AI’s latest escapade.
Implementing AI responsibly requires assessing readiness across three dimensions:
<strong>1. Workforce</strong> – Employees will need new skills and mindsets to deploy AI effectively:
- Data proficiency – Understanding available data and how to prepare it for algorithms
- Analytics acumen – Identifying use cases and interpreting algorithmic outputs
- Hybrid thinking – Combining computational insights with human judgment
- Design orientation – Focusing on user needs and experience
Training programs, hiring criteria, and performance management should encourage these competencies across the business.
<strong>2. Regulations</strong> – Authorities are only beginning to catch up to AI risks:
- Privacy laws – Restrict uses of personal data that can power AI
- Algorithm audits – Review high-risk systems for fairness and safety issues
- Reporting mandates – Require disclosing harmful incidents and metrics
- Certification regimes – Enforce standards for quality control and validity
You should monitor emerging regulations applicable to their AI activities or even the AI that is embedded in solutions they procure. Think about all the security cameras you might have installed at your office as one simple example.
<strong>3. Ethics</strong> – Even legal uses can raise moral quandaries:
- Bias and discrimination – Ensure fairness across gender, race, age, and other factors
- Transparency – Communicate how AI systems function and influence outcomes
- Accountability – Retain human control over critical decisions
- Security – Safeguard AI from misuse by internal and external threats
- Response Planning – Develop a response strategy for when AI goes awry
Organizations should formalize ethical principles tailored to their AI initiatives. Moreover, in the age of data breaches and cyber-attacks, leaving your AI unprotected is like leaving your house’s front door wide open with a sign that says “Free Chocolate Chip Cookies Inside.” Ensure your AI’s data is locked up tighter than Harry Winston Jewelers.
Turning AI into ROI
With the risks and responsibilities covered, it’s time to explore AI’s potential. The key is matching promising use cases to business priorities:
- Form cross-functional teams with business and technical experts to ideate on AI opportunities.
- Inventory available data sets and infrastructure to assess feasibility.
- Prioritize uses that enhance customer value, optimize operations, or open new opportunities.
- Start with targeted pilots, measure their impact, and scale what delivers results.
- Remember to make sure that your use cases are regulatorily compliant and ethical!
<strong>Some proven use cases to consider:</strong>
- Churn modeling – Retain more customers
- Anomaly detection – Spot fraud and abuse
- Sales forecasting – Improve budgeting and inventory
- Recommendation engines – Increase purchase frequency
- Chat or voice bots – Resolve customer and agent queries faster
- Predictive maintenance – Cut equipment downtime
The AI revolution brings boundless opportunities, but also potential pitfalls. As this overview illustrates, realizing the benefits requires methodical planning and vigilance. Failing to manage risks proactively could leave your organization exposed – or worse, liable – as algorithms become embedded in operations.
To stay ahead of the curve, leaders need qualified guidance tailored to their strategic context. Insurtech Advisors helps executives and board members stay current on the latest AI developments and chart an optimal path forward. Our experts can assess your readiness, help formulate policies, identify use cases, and define success metrics. We also partner with legal counsel to ensure regulatory compliance and mitigate liability.
Bring in objective advisors with real-world AI expertise across industries.
The AI revolution is here, but it requires responsible adoption tailored to human needs. Don’t leave your organization’s future to chance. With prudent planning and risk management, companies can thrive in the age of smart machines, avoiding the pitfalls of runaway technology. The future remains in our hands – for now! Just remember to keep your hands and feet inside the vehicle at all times.
<strong>Partner with us today to start mapping out your winning AI strategy.</strong>
Kaenan is a professional in the areas of block chain, telematics, wearables, analytics, artificial intelligence (AI) and Insurtech. He has played a key role in innovating many start-ups and established carriers. His advice has been widely appreciated in the financial community, which resulted in multiple quotes and publications in various media.
Most recently he was Practice Lead for Innovation, Fintech, and Strategic Insights at EY. Throughout his career he has held leading roles within Marketing Strategy and Decision Management with top Insurance, Banking and Finance companies, including USAA, Citibank and Sallie Mae.