How do you verify AI-generated insights?

As a chief of staff in the fast-paced annuity distribution landscape, I've come to see that harnessing the power of AI is a game-changer, but only when approached with a well-defined strategy. Before tapping into the vast potential of gen AI models, it's crucial to establish clear and measurable objectives. What insights are you seeking? What problems are you trying to solve? Without this North Star, it’s easy to get lost in an ocean of data and recommendations. We've seen tremendous value in using AI as an assistant, but only after carefully outlining our goals and providing precise parameters. This focused approach ensures the insights we receive are tailored, actionable, and aligned with our business objectives.

Diving into AI without a roadmap is like going on a road trip without a destination—you might end up anywhere, but probably not where you intended. Think of defining objectives as setting your GPS. Know your destination (your business goal), the route (KPIs and metrics), and the estimated time of arrival (deadlines). That way, you can course-correct if you take a wrong turn, and you'll know when you've arrived.

It’s important to verify that the results are accurate, reliable, and ethical. Here’s some best practices for verifying: - Define your objectives - Choose the right data - Validate your models - Interpret your insights - Apply your insights It is important to apply the insights to inform and improve strategies, operations, and outcomes, and measure their impact and value. Verifying AI-generated insights is an ongoing process that requires continuous evaluation and improvement. It is also important to have domain experts who understand the business or industry to verify the insights. For example, medical professionals should verify AI-generated insights with their expertise and medical knowledge before making any critical decisions.

As I get closer to finishing my degree, I have reflected on how much has changed since I started the program, which was a little over a year ago. The amount of progress that has been made since then has allowed me to think about how AI models will change the landscape of instructional design. Some are already seeing the effects and positive impacts Gen AI can have on a learning and development organization. To write effective clearly defined objectives, I encourage regular practice with writing prompts. When you've defined your objectives, you can use along with AI to set realistic goals.

The impact of AI on the enterprise is expected to be different from previous technological disruptions, affecting every part of our organization and potentially leading to new products, services, and job creation.

Verify AI insights by defining clear goals, selecting clean data, understanding results, and applying insights with intent. Domain experts play a crucial role in ensuring accuracy and ethical use

AI-driven digital innovation, the adage, "garbage in, garbage out" has never been more relevant. Data is AI's lifeblood. However, not all data is created equal. From my experience, even large datasets can mislead models if not representative or if they harbor unnoticed biases. Collaboration between domain experts and data scientists becomes essential. They together ensure the data embodies the real-world scenario it aims to address. Moreover, while the tech world rapidly evolves, ethical considerations should remain static. Ensuring consent and maintaining transparency in data collection isn't just a legal requirement; it's the foundation of stakeholder trust in AI-generated insights.

With the ability to utilize data and harness the power of AI, it highlights the important aspect of collaboration between those who are building software that creates and stores the data; those that retrieve, analyze and share it; and the end-user/receiver of the information. These can be large hurdles to overcome in companies that have older data models their employees have to work with until upgrades are made. This will be a challenge when setting up the objectives; understand your data and its limitations.

By feeding the Bots information if falsely feed it will not improve over time, however if fed the right content it knows how to gain better knowledge and connection. Sometimes though to test that it is on the right path, it would be best if you fed it a bit of false information to see if it responses differently base on your expectation.

The more data you give the AI to work with, the better chances of getting quality output. Actually, I've found that my brain works the same way at times. There's a literal difference between the phrases, "Clean the living room." and "Put the pillows on the couch, fluff them up, pick up all loose items off the floor, and vacuum the floor to finish the job.", though the person using both of these phrases has the same meaning in their mind.

I've emphasized one core tenet: continual validation. AI models aren't static; they evolve and adapt. While initial validation on diverse datasets sets the stage, it's the iterative feedback loop post-deployment that refines them. Leveraging tools like confusion matrices, ROC curves, and anomaly detection can highlight subtle biases or errors. Beyond quantitative metrics, soliciting domain-specific feedback provides qualitative insights that may otherwise be overlooked. Remember, an AI model's credibility isn't just in its inception but in its sustained accuracy and fairness across varied real-world scenarios.

AI is essentially rapidly applied statistics, so all the tools that validate statistical models apply here. One costly mistake in the field is to directly deploy AI models in production and show their results to customers after they proved to have good test scores. AI and statistical models in general shine when you give them multiple samples before drawing a conclusion. If a model is correct 8 out of 10, that means, given the same input 10 times, it will be correct 8 times. The remaining 2 may destroy the product experience if we are presenting the model result immediately to customer. Instead, one should create a buffer zone, say of 3 samples, and look at the majority of results in that buffer zone before presenting it to the customer.

While AI can be a great tool to assist with analysis, a review the raw data source should be followed to ensure accuracy. The KPIs you are targeting will be hard to achieve if the model is not properly set up.

Model validation ensures the accuracy and reliability of your AI-generated insights. Use techniques like cross-validation, confusion matrices, and performance metrics to test your models. For instance, validate the models by testing them on a diverse set of data and comparing the output with expected results. Regularly update and retrain your models to adapt to new data and improve performance over time.

Although this is an amazing tool. Its created by humans who make mistakes and have biases. With that, we must review and verify to make sure the content is a true reflection of our voice and not another's.

AI and digital innovation, interpreting AI insights isn't solely about numbers or patterns—it's about storytelling. Every insight carries a narrative pertinent to business goals. It's essential to translate the AI's intricate language into actionable strategies for stakeholders. I've often leveraged visualization tools to make complex insights digestible. More so, it's crucial to maintain humility with AI; while its insights can be transformative, they aren't absolute. Always present findings with both their potential and their bounds. In doing so, we foster not just understanding, but also trust in AI-powered decision-making.

Interpreting AI insights is similar to deciphering a new language. While the data might provide the words, it's the context and nuances of your specific business that provide the true meaning. It's crucial not just to accept AI outputs at face value but to challenge and understand them deeply, ensuring their relevance and accuracy for your business context. Remember, a model's prediction is only as good as the story it tells within your specific business landscape.

Interpreting AI-generated insights is a crucial bridge to actionable decisions. Think of it as crafting a meaningful narrative. Consider an e-commerce AI suggesting a price hike based on historical data. Interpretation means assessing if this aligns with market dynamics, customer behavior, and your pricing strategy. Effective communication here isn't just about numbers; it's about telling a story that guides your pricing decisions, considering both AI data and the bigger market picture, ensuring that you're responsive and competitive.

Back to the cooking analogy. You've worked hard on your tacos - and now we'll give it a taste. Is it perfect? Do we need to add more spices? Each time we cook the outcome can be a little different based on the inputs into the meal. If we look back on the times when your tacos were just perfect, what were its attributes - spicy vs mild? beef vs chicken? Based on your former success criteria, what should we add, reduce now or next time to ensure we get as close to perfect as possible?

It is always critical to add your own color to the information that has been gathered and or generated by your AI assistant. Content without context has the danger of becoming useless.

AI and digital innovation, actioning AI insights has been both an art and science. Mere possession of insights doesn't guarantee success; strategic application does. The most potent AI insights have driven proactive rather than reactive decisions in my projects. It's paramount to foster an iterative feedback mechanism, where the application's outcomes refine the model further. Additionally, while AI can guide decision-making, the ethical compass must be human. Prioritizing stakeholders' well-being has not just been an ethical choice but a strategic one, building lasting trust and sustainable growth in every initiative.

Having insights that are not actionable is like having a box of nails with no hammer. You have to constantly be asking yourself 'so what?' in order to really make a difference an achieve your outcome.

Building trust in AI-generated insights among stakeholders can be a challenge if there is lack of understanding on how the insights are generated. Transparency about the process is important.

Applying AI insights can transform a retail business. For instance, if AI reveals that customers prefer online shopping during weekends, you can adjust staffing schedules and marketing campaigns accordingly. This application optimizes operations and customer experience. It also exemplifies responsible use, as the data remains anonymous and respects privacy. The result? Enhanced profitability and customer satisfaction, showcasing how AI-driven insights can steer business growth while upholding ethical standards.

I used my insights as a funder to understand what historical data between our organization and the grantee and how it made sense for the current recognition we would be writing about.

When you apply your personal edits and insights to AI-generated copy, it's important that your communications/content pieces reflect: - Your brands tone - Clearly demonstrated personal experience - How your expertise adds value

Regularly track your progress against the defined metrics. Adjust your approach as needed to stay on course. Always HITL or human in the lead.

As you provide your own personal inputs, monitor the process you have put in place as well to make sure they are up to date and if not update them.

When working with AI generated material, you always want to check and double check the end product. Adding the human touch is how to effectively incorporate AI material into your every day life

As you contribute your insights, keep an eye on your established processes to ensure they remain current. Regularly measure progress against defined metrics and adapt your approach as necessary to stay on track as much as possible.

Make sure to glean more information from the Generative AI system. Ask for sources, ensure that they are credible. Ask for clarification on ambiguous or unclear output.

1. Cross-Referencing with Expert Opinions: a. Seek input from domain experts or professionals in the relevant field. Their expertise can validate or challenge the insights generated by AI models. b. For instance, if an AI system predicts stock market trends, consult seasoned financial analysts to validate its predictions. Their insights can provide a valuable benchmark. 2. Semantic Consistency Analysis: a. Examine the coherence and consistency of AI-generated content. Look for logical gaps, contradictions, or abrupt shifts in tone. b. Consider an AI-generated medical diagnosis report. By cross-referencing terminology and recommendations with established medical literature, you can assess its reliability.

Don't just use or trust the first response you get from AI, continue to delve in further asking for more details, verification or sources.

Reach out to colleagues that have experience with Generative AI for tips and tricks to push you along. You don't have to learn it all alone!