It’s no secret that [third-party data](https://faraday.ai/blog/first-party-vs-third-party-data-marketing) is one of the most critical components of effective, data-driven AI/ML projects. It adds additional layers of depth and complexity to predictive models to help them perform better, provides rich insight into who your customers and leads are outside of their engagement with your business, and more.

In Faraday, responsibly sourced third-party data is [built in](https://faraday.ai/features/faraday-identity-graph), and we recently pushed our biggest-ever update to the Faraday Identity Graph (FIG). 827 brand new traits alongside fresh updates to hundreds of existing FIG traits means that your customer predictions are now even more powerful–without any action required on your end, as Faraday automatically considers these new traits when building models for you.

From lifestyle [traits](https://faraday.ai/docs/abstractions/traits) like `Environmentally focused household` to financial segments like `Affluents`, Faraday’s algorithms have even more lenses through which your customer data can be analyzed, resulting in even more powerful predictions.

For example, a financial institution might find that a prominent feature in their [outcome](https://faraday.ai/docs/abstractions/outcomes) is another new trait, `Likely to use an investment broker`, which could indicate that they should leverage their brokerage service more heavily. They could append this trait to their pipeline that includes a likelihood to convert outcome so that, maybe instead of sending an email to people for whom this trait is true, they reach out directly via phone to talk about investment services.

Similarly, a business looking to focus their ad spend might use `Instagram user` to find their leads that are most likely to be active on Instagram. With this knowledge, they can then target their ads specifically on Instagram as opposed to using a widespread approach to see what sticks.

Predicting customer behavior is nothing without a strong foundation of first and third-party data. Faraday’s data is regularly refreshed with both updated and new traits so that your predictions are as accurate as possible. If you’re interested in learning more about these new traits, contact [support](https://support.faraday.ai) or your account manager.

The Faraday Identity Graph recently received its biggest-ever update, with over 800 brand new traits and updates to hundreds of existing traits.

1,000+ new and updated traits now live in Faraday

At Faraday, we automatically detect and correct for geographic bias in our training data through a process we call “geonormalization”.

## Why geonormalize?

It's easiest to explain through some examples, so let's walk through two.

**Example 1: to re-balance an unbalanced training population**

Say you have a business with two customer bases: California and Nebraska. The business started in California, so its customer base in California is 10x what it is in Nebraska. Without geornormalization, because the business has more customers in California and those customers look totally different to those in Nebraska (e.g. higher household income in California), everyone in California is scored higher than those in Nebraska.

**Example 2: to allow for a prediction to generalize over geographical areas not covered in the training data**

If your business started in New York, so all of your data–demographics, financials, and so on–is New York-based, and now you're trying to find _brand new_ business in California, the predictive model won't have been able to learn about what makes California unique in terms of that data, so the model will struggle to learn and its predictions will be unreliable.

Neither scenario is particularly helpful for growth!

Geonormalization adjusts both of these types of geographic bias with geography-based scaling instead of a raw value: Jane Doe is in the top 10% of scores in her state instead of the top 10% of scores for everyone.

Without geonormalization, you might define someone as "wealthy" if their net worth is $500k. Having a definition this static can present problems when comparing people from different demographic and financial backgrounds. With geonormalization, and keeping the above examples in mind, the definition of wealthy becomes more dynamic: "they have a net worth over twice as much as the average in their county."

Just because someone lives in a location that’s different from your customer data doesn’t necessarily mean that they should score lower than everyone that resembles your customer data.

## How does Faraday geonormalize?

Hence the need for a correction factor, to ensure that we are able to find the most likely customers in all of the regions our client wants to expand into. Our approach requires a detection algorithm and a correction algorithm

### Detection

We use a simple, but effective, algorithm. We compare the market penetration (customers / total possible customers) among each distinct geographic area at the desired level of analysis. If the largest market penetration is greater than 100x the smallest market penetration then we perform our correction algorithm during training.

For detections we start at the largest geographic area of interest, the state level, and work our way down to the smallest geographic area of interest, the postcode level. If at any point during the detection, the 100x threshold is met we perform the correction for that geographic level. Therefore, we are always applying the correction at the largest level possible.

### Correction

We use a two-pronged approach to correction: instance weighting for the training examples, and bias correction for the training features.

#### Instance weighting

We assign a weight to the positive examples calculated from the penetration rate of those positive examples in their respective geographic area. This in essence levels the playing field and gives a boost to the positive examples from areas where there are relatively fewer examples (unsaturated markets) and gives a penalty to the positive examples from saturated markets.

From a modeling perspective, we are tricking the model to believe that the good examples are just as likely to exist in any of the geographic segments. This leads to less bias amongst the features used to distinguish good examples from bad examples.

#### Feature correction

Our other method we employ in tandem with the instance weighting, is to transform features so that we level the playing field across the geographic segments. Instead of providing the model with raw features, we transform them so each person is represented by the percentile they occupy within their segment. Again, this one is probably easiest to explain via an example.

Instead of providing raw household income values of \$84k for CA resident A and \$66k for NV resident B, we provide the model with how they rank amongst their peers within their respective states. I’ve chosen the median income for each state, so the model would receive 50 for resident A and 50 for resident B.

By correcting the features, we provide the model with fewer discriminatory features with which to determine that everyone from the more saturated geographic segment is a better fit than everyone from the less saturated segments.

## Conclusion

Geographic bias correction, aka “geonormalization”, means that when using Faraday to expand your business into new markets, you can do so confidently knowing that our models will not just default to recommending people from markets in which you already have a significant presence. Grow your business confidently with predictions from Faraday.

At Faraday, we automatically detect and correct for geographic bias in our training data through a process we call “geonormalization”.

Geonormalization in Faraday

Predictive patterns change over time. When it comes to predicting customer behavior, it’s entirely likely that a person who’s been a lead for say, three months, is going to behave differently than someone who just signed up for your mailing list. The former has been receiving your email drip for a few weeks, is aware of your offerings, and probably has a decent idea of what they’re looking for. The latter is much closer to the top of the funnel, and is just getting to know your business better.

Why does this matter? Well, the person that just subscribed to your mailer might be less likely to convert compared to someone who’s been interacting with your business for 3 months - or they might not be! If the behavior “visited your site 3x” tends to be highly predictive of conversion, that’s only relevant to leads who’ve had the time in the funnel to actually visit your site more than once, so a different model should be used for fresh leads. Additionally, aggregated data is used to capture the situation of a person at the time of prediction. For example, has a new email been sent since we last produced a prediction?

With dynamic prediction, we create predictive models for several tenure bins, or periods of time, that someone has been in the eligibility cohort that you’re using for your prediction (i.e. their funnel stage). Then, we use the best possible model to make predictions on an individual based on that tenure bin. In the below example, you can see two strategies applied (A and B) across four tenure bins (0, 10, 20, and 30 days). In it, strategy A scored higher for the bins of 10 and 20 days, while B scored higher for 0 and 30 days, which means the optimal scoring combination is to use strategy A to score people that have been in the cohort for 0-10 days, strategy B for those in it for 11-20 days and 21-30 days, and A again for those in it for 30+ days.

[![Graphic of dynamic prediction tenure scoring](/images/blog/dynamic-prediction/dynamic-prediction-1.png)](/images/blog/dynamic-prediction/dynamic-prediction-1.png)

To illustrate this point further, below is an example of a pipeline that uses tenure bins to select the best model for each tenure group. In it, you can see the **pipeline population** group is broken down into three tenure bins, each of which has a “best” predictive model that’s used for scoring.

[![Graphic of dynamic prediction pipeline](/images/blog/dynamic-prediction/dynamic-prediction-2.png)](/images/blog/dynamic-prediction/dynamic-prediction-2.png)
_For further reading on Faraday model calibration and probability scores, check out [this blog post](/blog/faraday-probability-scores)._

In short, dynamic prediction enables us to provide scores that update with your data, ensuring accurate, powerful predictions for all stages of the customer lifecycle.

[Sign up free](https://faraday.ai/signup).

Predictive patterns change over time. Faraday uses dynamic predictions to choose the best model for you based on subject tenure.

How Faraday uses dynamic predictions to choose the best model for you

## What is identity resolution?

Identity resolution is the process of taking all of the files and databases of people data in your organization and figuring out "who is who" so that you can unify individual records into a single entity. For example, one database might have email, and another database might have street address; how do you connect them? Identity resolution is the answer.

Within identity resolution, there are two broad categories: **known identity** and **anonymous** (unknown) **identity**. Known identity is figuring out the email, phone number, and/or street address of someone. If you have known identity, you can find someone in the real world. Anonymous identity is defining some IP address, device ID, browser fingerprint, or cookie that represents a person without actually knowing who they are in the real world. In either case, you’re trying to get a join key or universal ID that lets you combine data from different sources across time.

How does identity resolution work? In a few different ways:

- You _explicitly_ collect known identifiers like email in web forms or addresses in orders/transactions.
- You _implicitly_ collect anonymous identifiers like IP addresses, cookies, as they browse.
- You stitch together known and anonymous identifiers when people click links in emails, log in, enter billing and shipping data, etc.
- You connect known identifiers with marketing data like age, gender, and household income using third parties like Faraday.
- You de-duplicate and "master" records: e.g. by saying that two different emails belong to the same person
- You upload unknown identifiers to ad networks and they resolve them into users on their platforms.

Finally, it's important to know the terms first- and third-party data. **First-party data** is data that you collect and own, and can include both known and anonymous identifiers. It also includes transaction amounts, order details, product details, etc. **Third-party data**, on the other hand, is data that is owned by others, but is available for specific uses. (You may also hear about “zero-party data,” which is a special kind of first-party data that users explicitly provide, such as by filling out surveys.)

When you unify first and third party data, you can get age, gender, household income, and other traits for a user so that you know both what they bought (first-party data) and who they are (third-party data). You’ll generally get the highest enrichment or match rate by starting with known identity, but it's possible to start with anonymous identity, too.

## Why does identity resolution matter?

If your business has more than just one useful database of information (which is highly likely), identity resolution is mandatory in order to properly understand not only who’s who within your data, but also how they’re engaging with your business. Doing so opens up a world of use cases that can be done efficiently because you’re able to group similar customers and leads together: [personalize their journeys](https://faraday.ai/templates/thematic-personalization), recommend first-and-[next best offer](https://faraday.ai/templates/next-best-offer), plan [retention campaigns](https://faraday.ai/templates/churn-scoring), and more.

For example, transaction data stored in back office systems can be combined with behavior observations in the marketing or product teams, with call logs from customer-facing teams, and so on. To illustrate this latter point, customer [churn prediction](https://faraday.ai/blog/churn-prevention-machine-learning) is often much stronger when it takes into account activity like messaging or calling.

Taking this a step further–when generative AI is available–identity resolution can help take unstructured data from call center transcriptions and combine it with structured data like transaction histories. This allows you to use sentiment analysis or entity extraction to create data attributes (i.e. predictive features) that can be combined with other first and third party features in predictive models, such as [lead scoring](https://faraday.ai/solutions/lead-management).

## Why identity resolution is difficult to implement

Identity resolution is important, but it's hard to implement. To start, the datasets involved need to have PII that can be compared. For example, maybe you have transactions with billing addresses included in one dataset, and views/clicks by email address in another. To resolve the identities in these two datasets, you’d need to purchase third-party email hashes connected to postal addresses so that both have billing address and email data, _then_ you’d need to ensure the data is standardized so that they can be compared.

The process is further complicated when you consider that people aren’t static–they move addresses, they have kids, their habits change–life goes on. You'll need to rent or purchase historical address data to make your graph complete. Your customers and leads change, and your data should be able to change with them.

## What does the identity resolution market look like?

There are a wide variety of identity resolution products on the market. Some are pre-integrated into the systems that use them, such as traditional "packaged" CDPs, purpose-built person-level AI infrastructure like Faraday, and others. These identity resolution systems primarily serve the purpose of the software they're integrated with, and offer the fastest time-to-value. Software in the master data management space like Reltio is dedicated to resolving and de-duping records for use by downstream systems.

There are also data warehouse-native identity resolution packages, such as Hightouch Identity Resolution or AWS Entity Resolution that run directly inside of a customer's cloud account or data warehouse. These provide standard functions for resolving identity that can be applied to any business, though they won't be immediately usable out of the box. TransUnion and other traditional data aggregators offer identity resolution packages on the Snowflake Marketplace that serve to connect first-party data to third-party identifiers and data. Finally, consultants like Deloitte will often build custom identity resolution projects on a per-client basis, if that client has previously purchased third-party data.

Due to competitive pressure from all of the data warehouse-native and standalone identity resolution providers, some software with built-in identity resolution like Faraday now allow data warehouse-native identity resolution too, which can be a good option that combines the low time-to-value with a flexible, proven base for other software and processes to be developed on top of.

## How Faraday’s identity resolution works

Faraday’s advanced identity resolution is built in, which means you don’t need to separately license any data or go through the laborious process of manually unifying it with your own to create distinct profiles of your customers and leads. All parts are included: from data and data unification that’s continuously up-to-date and responsibly sourced, to identity resolution and AI-powered customer predictions. Whether you’re interested in prioritizing your best leads and rejecting leads that won’t end up converting, seeing which customers are going to churn or finding their next best offer, Faraday has you covered from end to end.

To start, Faraday securely ingests your data through a [connection](https://faraday.ai/docs/abstractions/connections) to wherever your data is stored (or via CSV file), after which you describe your data to Faraday via [datasets](https://faraday.ai/docs/abstractions/datasets). In your datasets, you’ll tell Faraday what personally identifying information (PII) fields to look for (column mapping), as well as what [events](https://faraday.ai/docs/abstractions/events) take place in your data, which you’ll use to define your customer prediction objectives. Everything Faraday does is [fully API-enabled](https://faraday.ai/docs/reference), too.

With your data defined, we search the Faraday Identity Graph (FIG) for matching PII through fields like `email`, `first_name`, and `address`. Once a match is found, all data that you have on this individual is unified with and enriched by their data in FIG. From demographics, to financials, property data, and more, 1,500+ robust traits supplement your data on how they interact with your business with how they exist _outside_ of those interactions.

[![Graphic of Faraday identity resolution](/images/blog/identity-resolution/identity-resolution.png)](/images/blog/identity-resolution/identity-resolution.png)

The result is a unified, enriched customer profile that’s embedded directly into your applications and workflows–with customer predictions on top–for all of your customers and leads, ensuring that your teams have all of the information they need to engage effectively.

At the end of the day, identity resolution is critical to unlocking truly data-driven marketing and AI/ML projects. With it, all of your data comes together from its disparate sources to form unique identities for each individual. These identifiers cover their engagement with your business through visits, email addresses, transactions, etc, their marketing highlights like age and income, and any other traits that you have. With identity resolution, you can effectively predict which leads are most likely to convert and which you shouldn’t waste time and money on, which customers are most likely to churn, their buyer persona, what product or service to recommend next, and more.

Faraday handles the ML struggle so that you can focus on your downstream applications and workflows.

[Sign up free](https://faraday.ai/signup).

Faraday unifies your data with rich third-party data and applies advanced identity resolution techniques to reveal panoramic views of your customers and leads.

How Faraday unifies your data with the Faraday Identity Graph for advanced identity resolution

AI is everywhere. Businesses of all kinds are using it for every use case imaginable. From creating copy for their emails or website, to generating graphics for their blog, to [predicting customer behavior](https://faraday.ai/features/customer-behavior-predictions) and beyond. While using AI for the more innocuous cases like brainstorming ideas or generating art tends to leave you with the set-it-and-forget-it mentality, that’s not the case in the realm of predicting customer behavior. Here, [explainable AI](https://faraday.ai/blog/what-is-explainable-ai-why-is-it-important) is critical in helping non-data scientists understand what data your predictive models used to come to their conclusions, and whether or not you can trust them.

[![Graphic of dynamic prediction tenure scoring](/images/blog/transparent-reporting/transparent-reporting.png)](/images/blog/transparent-reporting/transparent-reporting.png)

For this reason, every model in Faraday comes with at-a-glance performance reporting so that you can quickly assess how strong your predictive model is. Beyond this, you can dig into things like [feature importances and directionality](https://faraday.ai/blog/faraday-model-reports) to see what individual features from the Faraday Identity Graph and [your own data](/blog/first-party-features) had the greatest impact in creating the predictive model, and even where specifically within those features they had such impact. Additionally, reporting on ROC and lift are included.

On top of this at-a-glance reporting, each model comes with a complete, in-depth technical report that digs into the nitty-gritty that data scientists love \- from analysis of training data to model calibration, cross-validation, and more. Further, Faraday strongly believes in [Responsible AI](https://faraday.ai/features/ai-safety-and-responsible-ai), which means that this technical report also includes [score explainability](https://faraday.ai/blog/faraday-score-explainability) and background on how Faraday came to the conclusion of its bias and fairness assessments.

Don’t _just trust us_ (or any other AI that you use\!) \- our reports speak for themselves.

[Sign up free](https://faraday.ai/signup).

From at-a-glance performance reporting to feature importances with directionality to in-depth technical reporting, Faraday helps you understand the models we build.

Transparent predictive model reporting with Faraday

## What is a feature in machine learning?

In machine learning, a feature is a piece of data that’s used to train a predictive model. A feature can come in a variety of different formats, be it a static trait like age, whether or not someone’s a frequent home remodeler, or some value that’s related to the events taking place in your own data.

Once these features have been fed into the machine learning algorithms, predictive models begin learning from the data by looking for patterns to analyze, and the end result is predictions that help your business find your most likely-to-convert leads–and reject the least likely-to-convert ones–find more high-LTV customers, and more.

## Creating first-party features from your data

Once your customer and lead data is ingested through a [connection](https://faraday.ai/docs/abstractions/connections) to your data warehouse or a CSV upload, you’ll point Faraday toward the [events](https://faraday.ai/docs/abstractions/events) and [traits](https://faraday.ai/docs/abstractions/traits) within your data. While doing so, you’ll define when an event took place, its value, and any relevant event properties (e.g. the acquisition channel of the event).

[![Screenshot of an event stream's configuration in Faraday](/images/blog/first-party-features/event-features.png)](/images/blog/first-party-features/event-features.png)

Then, when you’re creating a prediction in Faraday, we’ll use these properties that you define to engineer features to be used alongside the [Faraday Identity Graph’s 1500+ vivid traits](https://faraday.ai/features/faraday-identity-graph). While analyzing the predictions you’ve created–like the likely-to-buy-again outcome below–you’ll find your first-party features with a sparkle next to them.

[![Screenshot of an outcome's feature importance in Faraday](/images/blog/first-party-features/outcome-features.png)](/images/blog/first-party-features/outcome-features.png)

In the above example, a first-party feature was created based on the date the last subscription box was purchased by a customer of this client’s in `last_box_purchased`, and another for the category of that box from an event property in `orders category` (orders being the event stream, category the event property defined within the stream). Lastly, a feature was created for the `first_box_assigned` trait–designating which category of subscription box the customer received on signup (kitchen, camping, etc).

These three features had a major impact on the scores of people in this outcome, and were highly predictive of the behavior the client was predicting for: likelihood of buying again.

You can read more about how individual features impact an outcome’s predictions in the data section of our [outcomes](https://faraday.ai/docs/abstractions/outcomes) docs.

[Sign up free](https://faraday.ai/signup).

Faraday automatically engineers machine learning features based on your first-party data to be used as predictors.

How Faraday creates first-party features from your data

Let's take a look at some of the latest features in Faraday.

## Dependency visualization

In Faraday, any given resource can be connected to one or more other resources, which we call dependencies. For example, a cohort you’ve built might be used to define an outcome, which is then used in a pipeline. This type of interconnectedness can make it difficult to troubleshoot issues, to see what impact editing a resource might have on other resources, and so on.

We’ve found that it can be helpful to zoom out and look at the bigger picture of where a resource fits in the larger picture of your Faraday account. To achieve this, we’ve added a dependency visualization to every resource’s detail page in the Dashboard.

[![Screenshot of the resource dependency graph](/images/blog/product/dependency-visualization.png)](/images/blog/product/dependency-visualization.png)

The dependency visualization reveals the overall layout of resources in your Faraday account, and highlights which individual resources are connected to the one you’re currently viewing. In the above example, a _signups_ event stream was used to define the _leads_ cohort, which was used to define the _lead conversion_ outcome.

This new visualization should help you understand which resources are necessary for your predictions, and which you can safely [archive](/blog/archive-resources-and-updated-deployments) or remove.

## Sortable date columns

In the past, resources you created in Faraday were sorted alphabetically by name. While alphabetical sort is simple to understand and use, we found it a bit awkward to create something only to have it be dropped into the middle of a list.
To improve this workflow, we’ve made the majority of columns sortable, and added new columns for relevant date information.

[![Screenshot of sortable columns](/images/blog/product/sortable-columns.png)](/images/blog/product/sortable-columns.png)

The created date is–you guessed it–the date that the resource was created. Modified refers to the date that the resource’s configuration was last modified (e.g. a trait was added to a cohort’s definition). Lastly, processed is the last time the output was processed by Faraday (i.e. the output changed).

---

Check out our [changelog](/changelog) for a dated list of updates.

Explore the latest Faraday updates with a visualization of how resources interact with each other, and new sortable list columns.

What's new: dependency visualization and sortable list columns

Let's take a look at some of the latest features in Faraday.

## Archiving resources

We’ve found that Faraday users love to experiment — implementing pipelines in Faraday to predict all kinds of behaviors in different ways. An unfortunate side effect of this is that your Dashboard can get pretty cluttered. Of course, you could always delete your resources, but what if you actually want them back someday?

For this reason, we’ve added the ability for you to archive resources you’ve created in Faraday.

[![Screenshot of the resource archival screen](/images/blog/product/archive-resource.png)](/images/blog/product/archive-resource.png)

To archive a resource in the Dashboard, simply click the … button on the resource, and click archive. If the resource you’re archiving has downstream dependencies–like an outcome that uses a cohort you’re trying to archive—those will be archived as well. Resources that you've archived can be found using the _archived_ filter in the list view of that resource type. To unarchive a resource, click the unarchive button in the same menu.

For API users, each resource has a POST request specifically for archiving and unarchiving, e.g. [archive cohort](/docs/reference/archivecohort).

## Deployments: new look & the ability to rename

We’ve given the Dashboard's [pipeline](/docs/abstractions/pipelines) deployment card a facelift so that it’s easier to tell, at-a-glance, what any given deployment’s purpose is–and added the ability to rename them, too.

[![Screenshot of the deployment screen](/images/blog/product/new-deployment-card.png)](/images/blog/product/new-deployment-card.png)

The new deployment card now only displays the most critical info by default: [connection](/docs/abstractions/connections) you’re deploying to, representation format (hashed, identified, etc), and any filters you’ve added (e.g. top 20%). Additional info can be found inside "more details."

By default, a new deployment’s name will be assigned as “Deploy to [connection_name],” but you can now rename them via the ... menu in the upper right of the deployment card. Additionally, you’ll find an indicator for when [preview mode](/docs/abstractions/pipelines#preview-mode) is enabled in a deployment card.

For API users, deployments (targets) can be renamed via the PATCH request [update a target](/docs/reference/updatetarget)'s `name` option.

---

Check out our [changelog](/changelog) for a dated list of updates.

Explore the latest Faraday updates with the ability to archive resources and a new look for pipeline deployments.

What's new: archiving resources & a new look for deployments

We're happy to announce a new partnership between Faraday and Simpli.fi, the leading Advertising Success Platform, which provides programmatic advertising solutions and workflow software to over 2,000 media teams, agencies, and brands.

Under this partnership, Simpli.fi will incorporate Faraday's [identity graph](/features/faraday-identity-graph) of more than 1,500 consumer attributes and individual-level [customer behavior predictions](/features/customer-behavior-predictions)into its sophisticated media buying program. Simpli.fi users can now create highly effective lookalike audiences, powered by Faraday, that enable marketers to target their best-fit customers with relevant engagement at scale.

"It's about combining the science and the art," Faraday CEO Andy Rossmeissl says while chatting with Simpli.fi's David McBee about lookalike audiences on [Simpli.fi TV](https://simpli.fi/simplifi-tv/andy-rossmeissl-simpli-fi-tv). Faraday's data science–combined with the artistic approach in which Simpli.fi makes these insights actionable–results in campaigns that show real ROI.

Clients interested in leveraging customer behavior predictions to create lookalike audiences of likely buyers and deploy them through Simpli.fi can contact their account manager to get started.

Faraday's identity graph and customer behavior predictions come together with Simpli.fi's media buying program to create lookalike audiences that enable marketers to target likely buyers with relevant engagement at scale.

Introducing a new partnership between Faraday and Simpli.fi

Is the artificial intelligence hype cycle approaching the inevitable trough of disillusionment, as hype and reality collide head-on? Is the AI bubble, much like the internet bubble of years past, about to burst?

AI is moving at a frantic pace, and with its advancement comes a massive amount of investment, which has led to inevitable market saturation and a lot of inflated expectations. As of the summer of 2023,[ ](https://www.gartner.com/en/newsroom/press-releases/2023-11-28-gartner-hype-cycle-shows-ai-practices-and-platform-engineering-will-reach-mainstream-adoption-in-software-engineering-in-two-to-five-years)**[Gartner](https://www.gartner.com/en/newsroom/press-releases/2023-11-28-gartner-hype-cycle-shows-ai-practices-and-platform-engineering-will-reach-mainstream-adoption-in-software-engineering-in-two-to-five-years)** had already identified that generative AI was reaching the 'peak of inflated expectations' and was about to enter the dreaded 'trough of disillusionment'.

Even the ever-promoting, all-is-well tech titans are starting to sound the alarm on overpromising about the power of AI. Recently both Amazon and Google have reportedly been[ ](https://www.theinformation.com/articles/generative-ai-providers-quietly-tamp-down-expectations?rc=kz8jh3&ref=wheresyoured.at)**[telling their sales teams](https://www.theinformation.com/articles/generative-ai-providers-quietly-tamp-down-expectations?rc=kz8jh3&ref=wheresyoured.at)** to tone down on hyping AI too much to customers. Today's AI leaders are concerned that they are overpromising, and underdelivering.

But all is not hype when it comes to AI, there are many real-world business applications where AI is making a big difference and delivering true value to both the business and its customers. Accordingly,[ ](https://www.gartner.com/en/webinar/567177/1278324)**[Gartner](https://www.gartner.com/en/webinar/567177/1278324)** expects 2024 to be the year where the companies that successfully deploy AI do so through a more targeted and disciplined approach.

So, what is AI hype and what is AI reality? And how are people using AI successfully in the real world to make a measurable difference to a business's bottom line?

## AI hype

The amount of AI hype is extraordinary, even in the world of technology hype. Many of us who have been around the block a few times have heard this story before. An amazing new technology arrives that will change the world, and then companies hype the technology endlessly, leading to market saturation, end-user fatigue, and missed expectations. Eventually, the technology makes its way into mainstream adoption, but typically through more distinct use cases and targeted real-world applications. This process is what **[Gartner's Hype Cycle](https://www.gartner.com/en/articles/what-s-new-in-artificial-intelligence-from-the-2023-gartner-hype-cycle)** attempts to represent graphically.

What is AI hype? When you go to a technology conference and an AI futurist talks in vague generalities about how AI will solve every future problem while giving no specific use cases. Or when omnipresent tech CEOs recycle their chatbot marketing from 30 years ago, put an AI in front of it, and tell you your AI assistant will help you in every conceivable way, like a virtual butler. This is AI hype.

For the past year or so, the AI hype discussion has been dominated by generative AI. And while some demos of generative AI were promising, their real-world application at this time has been oversold somewhat to the general public and business world.

## AI reality

While up to now generative AI has gotten much of the attention, predictive AI has become the hot topic of conversation in business circles for delivering immediate business value in challenging economic times. Predictive AI can work in tandem with generative AI leveraging the best of both technologies, but you need the right software to combine the algorithms for true value.

With predictive AI, more specifically predictive modeling and predictive analytics, companies today generate real business value in the short to mid-term by predicting customer behaviors by enabling more effective and efficient customer engagement within the customer experience.

By using software like the[ ](https://faraday.ai/)**[Faraday](https://faraday.ai/)** predictive analytics and predictive modeling platform, you can have the AI infrastructure needed to predict customer behaviors like likelihood to convert, likelihood to churn, forecast spend, and product recommendations. With these predictions, you can drive data-driven customer experience use cases like AI lead scoring, churn prevention, adaptive discounting, and next-best offer.

Platforms like Faraday are real-world targeted AI, not some general-purpose AI platform for generating news headlines. Faraday focuses on predicting the behavior of individuals, with built-in Responsible AI, including features like bias mitigation and prediction explainability.

## More real-world AI use cases

While improving the customer experience with predictive modeling and predictive analytics is a top use case for real-world AI implementation, there are several other primary use cases where AI is making significant inroads.

One of the areas already making a huge impact is predictive AI and generative AI being used to empower software developers, aka employee empowerment, allowing them to code quicker, improve time-to-market, and enhance their data with predictive algorithms.

Let's face it, existing developers are maxed out with work, and they can't build things as fast as companies want, plus they don't enjoy their coding jobs when they work on an assembly line. Today's AI coding assistants are intended to handle more routine tasks, allowing developers to focus on higher-value work. But to work well, they need to be combined with predictive AI engines like Faraday powered with Responsible AI.

"AI-augmented and machine learning (ML)-powered software engineering is changing the way software is being created, tested and operated, and the need for responsible AI is growing," said Dave Micko, Senior Director Analyst at Gartner. "Practices such as platform engineering will begin injecting insights from deployed systems into the systems being developed."

## Conclusion

The AI hype machine has done its job. People are inundated with AI marketing to the point no one knows what is hype and what is reality. Yes, generative AI is exciting and has tons of future promise. But right now it's more in the demo stage of development.

While many generative AI apps do not need predictive algorithms, for many traditional business applications, using predictive AI plus generative AI can be a good optimization to move the needle on your business.

Predictive modeling and predictive analytics are where businesses need to put their technology investment dollars to start to see real returns from AI, in the short-to-mid term.

But how a company moves forward in this process is critical. AI must be done safely and responsibly to ensure that the consumer's, business's, and community-at-large best interests are maintained.

This post explores the current state of AI, contrasting the widespread hype with real-world applications that deliver tangible business value. It highlights the overpromising nature of generative AI and emphasizes the immediate benefits of predictive AI, urging companies to adopt a targeted and responsible approach to AI implementation.

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