is artificial intelligence just another overhyped tech bubble or is it truly the game Cher everyone's been talking about if you're caught between AI skepticism and unbridled enthusiasm you're not alone the truth is we're riding the roller coaster of technological Evolution as described by the Gartner hype cycle welcome to navigating the AI Revolution beyond the hype I'm Richard Walker from l today and today we're going to demystify the journey of AI particularly generative Ai and give you the information you need to decide on whether your Project's needs are best met with Gen or whether they will
be better served with another technology in order to do that in this video we're going to take a closer look at the 12 most common use cases of AI Solutions these include applications such as classific ation planning prediction content generation and decision intelligence for each of the 12 use cases will describe the key attributes of these applications and give a couple of real world examples of systems in these areas to make the use case more concrete we'll then take a look at the six most common families of techniques in AI naturally this includes generative Ai
and llms but also includes rule-based systems machine learning and optimization will then match up the use cases with the tools and techniques to show which is the best tool for the job while it's fair to say that generative AI agents and llms have areas where they Excel there are use cases where you would not expect great outcomes using llms and would be better off using other techniques so by the end of this video you'll have a better understanding of which tool to use for your particular job and a far greater chance of successful outcomes in
your AI projects here's the thing every transformative technology from the internet to blockchain has followed this hype cycle initial excitement inflated expectations disillusionment and finally productive implementation gen is no different but here's where it gets interesting and where you can gain a crucial Advantage the firms that will dominate in the AI era aren't those blindly throwing generative AI at every problem no the winners are those who Master the art of matching the right AI technique to the right application it's not about having the shiniest new AI toy it's about having the right tool for the
job by the end of this video you'll have a clear road map for navigating the AI landscape you'll know when to Leverage The Power of generative Ai and when to look at other technology techniques and most importantly how to identify the perfect use case for each application in your business we're not just going to talk about avoiding pitfalls we're going to show you how to turn an understanding of what tool to use for what use case into your secret weapon for AI success in capital markets and other Industries there are several areas where careful consideration
and selection of the right tools adds value and saves otherwise wasted resources let's look at the landscape of use cases and Technology tools and then match them up with which technology is suited for each application in a world where everyone is claiming that their system system is powered by AI we'll see that often times AI or geni is not the most effective way to attack and solve a problem other tools and approaches offer way better outcomes in a towards AI post by Christopher to inspired by a Gartner conference in the summer of 2024 Christopher identifies
12 common use cases or application constellations that use AI these include prediction and forecasting this technique uses historical data in Trends to estimate future outcomes in capital markets it's applied in fixed income research helping analysts predict bond yields and interest rate movements outside of Finance it's crucial for supply chain demand forecasting enabling businesses to optimize inventory levels and reduce waste the planning constellation focuses on designing strategies and allocating resources effectively Capital markets utilize this for optimal trade execution scheduling minimizing Market impact and transaction costs manufacturers leverage planning AI to optimize production processes reducing downtime and
maximizing output decision intelligence analyzes data to provide actionable insights in finance a great use case is algorithmic trading by processing vast amounts of Market data to make split-second trading decisions hospitals use decision intelligence for resource allocation ensuring optimal distribution of Staff equipment and beds moving on to autonomous systems which operate without human intervention in finance we see this in automated portfolio rebalancing where AI adjusts investment allocations based on market conditions in transportation self-driving Vehicles showcase how autonomous systems systems can revolutionize travel and delivery services classification techniques divide data into segments based on specific attributes in
finance this automates credit rating classification streamlining loan approval processes other industries use it for customer churn prediction identifying atrisk customers for targeted retention efforts recommend systems suggest options based on user behavior and preferences they can personalize investment product recommendations in capital markets matching investors with suitable financial instruments in e-commerce these recommended systems Drive product suggestions enhancing customer experience and boosting sales perception AI interprets sensory data like images sound or text in finance it's able to gauge realtime news sentiment gauging Market mood from countless new sources in healthcare Medical Imaging benefits from ai's ability to analyze
x-rays or MRIs intelligent automation enhances traditional automation processes to be more adaptive in capital markets it can be used to Aid Capital allocation dynamically adjusting allocation based on risk adjusted returns smart home Energy Management Systems showcase how this technology optimiz izes resource usage in everyday life anomaly detection identifies data points that deviate from expected Behavior it's crucial in spotting Market manipulation in finance flagging suspicious trading patterns fraud or money laundering in cyber security it detects threats by identifying unusual network activity or user Behavior content generation creates new text or images based on learned pattern in
capital markets it can summarize company earnings reports distilling key information for investors marketing teams can use it to create personalized email campaigns tailoring messages to individual customer preferences conversational user interfaces enable natural language interaction between humans and digital systems in finance AI powerered text tosql report generation allows non-technical users to query databases using plain language customer service virtual assistance across Industries demonstrate how this technology can enhance end user support finally knowledge Discovery identifies patterns insights and relationships within large data sets in finance it can uncover hidden Market correlations revealing nonobvious connections between different asset classes
or economic factors the pharmaceutical industry uses it for drug Discovery analyzing vast chemical databases to identify potential new treatments as you can see AI techniques form a diverse ecosystem of applications each constellation offering unique benefits across different sectors the key is matching the right technique to The Right Use case for Maximum Impact by understanding these categories and their applications businesses can make informed decisions about implementing AI to drive Innovation and efficiency now that we've explored the diverse landscape of AI use cases let's dive into the six key Technology Solutions that can address these applications again
I've used the classifications identified by Christopher to in his towards AI article each of these techniques has its own strengths and weaknesses making each technique more or less suitable for different scenarios graphs powerful tools for representing and analyzing complex relationships and workflows they excel in scenarios where interconnections are crucial their strengths well they're ideal for visualizing and processing Network like structures their weaknesses well they can become computationally expensive for very large data sets they're best suited for things like interbank lending networks modeling the interconnections between financial institutions to assess systemic risk and the potential for
contagion effects in the case of bank failures graphs are less suitable for tasks like content generation or anomaly detection rules and heuristics this approach uses predefined rules based on Expert knowledge or established practices their strengths while they're transparent they're easy to interpret and quick to implement their weakness is they lack flexibility and the ability to learn from new data their best suited for workflow systems such as trade data enrichment with clear predefined criteria rules and heuristics are less effective for tasks requiring nuanced understanding or adaption like perception or autonomous systems simulation creates models to answer
whatif questions and explore potential scenarios strengths they're excellent for scenario planning and risk assessment their weaknesses they can be very computationally intensive and rely heavily on the accuracy of the underlying model they're best suited for financial modeling such as assessing the impact of interest rate changes on Bank profitability they're less suitable for real-time decision- making or content generation tasks optimization this technique finds the best parameters for a given formula or equation to maximize or minimize specific outcomes strengths they're highly effec effective for problems with clear objectives and constraints weaknesses it can be sensitive to initial
conditions and may find local rather than Global Optima they're best suited for portfolio optimization in finance balancing risk and return it's less applicable to tasks like perception or natural language processing machine learning this Uber broad category includes various techniques like linear regression support Vector machines clustering and decision trees strengths they're capable of finding patterns in large data sets and making predictions weaknesses they require very large amounts of very high quality data and can be computationally intensive they're best suited for predictive tasks from large complex data sets such as credit risk assessment they're less suitable for
tasks requiring explicit reasoning or creativity finally generative a I models this includes large language models and other generative techniques strengths they're excellent at generating humanlike text and understanding context the weaknesses they can be unpredictable they may hallucinate and they require significant computational resources particularly to train they're best suited for Content generation tasks like creating earnings report summaries or natural language reporting interfaces they're less suitable for precise numerical or calculations or rule-based decision making understanding these Technologies and their appropriate applications is crucial for successful AI implementation for example while generative AI is excellent for creating summaries
of earnings calls or natural language reporting interfaces it would not be the best choice for detecting Market manipulation or optimizing trade execution similarly while machine learning excels at predicting customer churn or credit ratings rules and heuristics might be more appropriate for implementing clear auditable processes in Regulatory Compliance by matching the right technology to The Right Use case you can maximize the effectiveness of your AI initiatives and avoid the pitfalls of misapplied solutions remember the goal isn't to use the most advanced or hype technology but to choose the one that best solves your specific business problem
as we move forward in the AI Revolution the winners will be those who can navigate this complex landscape understanding when to Leverage The Power of cuttingedge generative models and when to rely on more traditional proven techniques by mastering this art of selection you'll be well positioned to harness the true potential of AI in your organization [Applause]
