Each day, more documents are created and used across companies to make decisions. However, the value in these documents is primarily expressed as unstructured data, which makes the value difficult and manually intensive to extract and use for business processes. 

As the number and variety of documents used by businesses proliferate, machine learning (ML) solutions need to be more flexible to handle the broader set of use cases. That’s why we introduced the first Document AI Workbench model, Custom Document Extractor (CDE), in Public Preview at Google Cloud Next ‘22. CDE makes it fast and easy to apply ML to virtually any document-based workflow to extract structured data from unstructured document types, to automate business processes. 

CDE lets developers and analysts use their own data to train models and extract fields from documents needed for the business. CDE lets organizations build models faster and with less data — thus accelerating time-to-value for processing and analysis of data in documents.

Today, we announce that Document AI Workbench is Generally Available (GA), open to all customers, ready for production use through APIs and the Google Cloud Console. Document AI Workbench is covered by the Document AI SLA — online and batch document prediction is supported with >=99.9% uptime. Furthermore, Document AI Workbench is now covered by Google Cloud’s GA product terms. For example, Google will notify customers at least 12 months before significantly modifying a customer-facing Google API in a backwards-incompatible manner.

In this blog post, we’ll explore ways customers are already using CDE and Document AI Workbench’s updated capabilities.

What users are saying about Workbench

Deliver higher model accuracy with Workbench

Users leverage Workbench to ultimately save time and money. A third party evaluated Document AI Workbench and concluded that it extracts data more accurately¹ than several competing products for document types with variable layouts (e.g. invoice, receipt, bank statements, paystubs). Better accuracy drives higher automation rates, helping Workbench users save time and money.  

Chris Jangareddy, managing director for Artificial Intelligence & Data at Deloitte Consulting LLP said, “Google Cloud Document AI is a leading document processing solution packed with rich features like multi-step classify and text extraction to automate sorting, classification, extraction, and quality assurance. By combining Document AI with Workbench, Google Cloud has created a forward-thinking and powerful AI platform for intelligent document processing that will allow for process transformation at an enterprise scale with predictable outcomes that can benefit businesses.”

Mansoor Khan, CEO of OneClinic said, “We help medical professionals scale their clinics through automation. We used Google’s Document AI Workbench to create a model to automatically extract data from patients’ insurance cards as part of our patient check-in software. Workbench is easy to use and we are really happy with the model accuracy — it extracts data more accurately than what we would expect from human data entry.”

Rajnish Palande, VP, Google Business Unit for BFSI, TCS said, “The Google Cloud Document AI Workbench leverages artificial intelligence (AI) to manage and glean insights from unstructured data. The Workbench brings together the power of classification, auto-annotation, page-number identification and multi-language support to help organizations rapidly deliver enhanced accuracy, improved operational efficiency, higher confidence in the information extract, and increased return on investment.” 

Build production ready models faster with Workbench

Document AI Workbench helps users create machine learning models faster. For example, a third-party evaluation shows Document AI Workbench trains machine learning models up to 3x faster than a leading competitor. This is an important improvement which lowers total cost of ownership and increases value.

Dallas Dolen, Partner, Google Alliance Leader at PwC said, “Google Document AI Workbench helps to accelerate our custom parser models training as well as improves accuracy and performance using a custom document extractor with human in the loop. It helps us solve complex business problems for our clients in the financial services and healthcare industries.”

Ziang Jia, Senior DocAl Development Lead at Resultant, said, “Document AI Workbench has unlocked a brand-new machine learning development experience for information extraction solutions. Its simplicity and robustness enabled us to build models and deliver a highly accurate outcome in an agile way for a large government agency. We couldn’t be more impressed by its simplicity and robustness and are excited to see how the product will evolve in the future.”

Sean Earley, VP of Delivery Services of Zencore said, “Document AI Workbench allows us to develop highly accurate document parsing models in a matter of days. Our customers have automated tasks that formerly required significant human labor. For example, using Document AI Workbench, a team of two trained a model to split, classify and extract data from 15 document types to automate Home Mortgage Disclosure Act reporting. The mean trained model accuracy was 94%, drastically reducing the operational cost of our customer’s compliance reporting procedures.”

What’s new with Document AI Workbench 

The latest Workbench capabilities make it even easier to train and deploy an extraction model: 

• With Workbench’s public APIs, you can programmatically create, delete, train, evaluate and deploy models.
• Our updated dataset management tools automatically detect and create existing schema labels from your pre-annotated documents. They also provide you more flexibility when creating and managing schema.
• Our new DocAI Toolkit includes a labeled document converter so that you can easily convert your labeled documents to DocAI’s format and start training faster.
• We’ve reduced the cognitive load for labelers with efficiency enhancements to our Labeling UI.    
• The revamped Processor Gallery helps you quickly identify the best model for your use case.

 What’s next for Document AI Workbench

We continue to invest in Document AI Workbench to help you automate document processing. Here are a few things we’re working on that we’re excited about:

• Classify document types with the Custom Document Classifier (CDC), coming soon in public preview
• Copy processor versions across projects and processors to streamline managing development and production environments
• Support larger documents (e.g., longer than 50 pages) so you can process a wider array of documents
• Broader (non-latin) language support–equivalent to Document AI OCR
• And many more investments, using state of the art technology, to help you build world class models faster to automate document processing

Document AI Workbench is in GA and ready for production workloads. Learn more via Document AI Workbench documentation or try it out in the Google Cloud Console.

^{Acknowledgements: Tomas Moreno, Outbound Product Manager, Lukas Rutishauser, Software Engineering Manager, Michael Kwong, Software Engineering Manager, Rajagopal Janani, Software Engineering Manager, Michael Lanning, UX Designer.}

^{1. When trained with 200+ documents}

Financial institutions across the globe rely on real-time indices to inform real-time portfolio valuations, to provide benchmarks for other investments, and as a basis for passive investment instruments including exchange-traded products (ETPs). This reliance is growing—the index industry dramatically expanded in 2020, reaching revenues of $4.08 billion.

Today, indices are calculated and distributed by index providers with proximity and access to underlying asset data, and with differentiating real-time data processing capabilities. These providers offer subscriptions to real-time feeds of index prices and publish the constituents, calculation methodology, and update frequency for each index.

But as new assets, markets, and data sources have proliferated, financial institutions have developed new requirements. Financial institutions will need to quickly create bespoke and frequently updating indices that represent a specific actual or theoretical portfolio, with its unique constituents and weightings.

In other words, existing index providers and other financial institutions alike will need mechanisms for rapid creation of real-time indices. This blog post’s focus—an index publication pipeline collaboratively developed by CME Group and Google Cloud—is an example of such a mechanism. 

The pipeline closely approximates a particular CME Group index benchmark, but with far greater frequency (in near real time vs. daily) than its official counterpart. It does so by leveraging open-source models such as Apache Beam and cloud-based technologies such as Dataflow, which automatically scales pipelines based on inbound data volume.

Machine learning’s production problem

In the past decade, advances in AI toolchains have enabled faster ML model training—and yet a majority of ML models are still not making it into production. As organizations endeavor to develop their ML capabilities, they soon realize that a real-world ML system is comprised of a small amount of ML code embedded in a network of complex and large ancillary components. Each component brings its own development and operational challenges, which are met by bringing a DevOps methodology to the ML system, commonly referred to as MLOps (Machine Learning Operations). To apply ML to business problems, a firm must develop continuous delivery and automation pipelines for ML.

This index publication collaboration is instructive because it demonstrates MLOps best practices for just such a pipeline. One Apache Beam pipeline, suited for operating on both batch and streaming data, extracts insights and packages them for downstream consumers. These consumers may include ML pipelines that, thanks to Apache Beam, require only one code path for inference across batch and real-time data sources. The pipeline is run inside Google Cloud’s Dataflow execution engine, greatly simplifying management of underlying compute resources. 

But the collaboration’s value is not constrained to the ML and data science realm. The project shows that consumers of the Apache Beam pipeline’s insights may also include traditional business intelligence dashboards and reporting tools. It also demonstrates the simplicity and economy of cloud-based time series data such as CME Smart Stream, which is metered by the hour, quickly and automatically provisioned, and consumable at a per-product-code (not per-feed) level.

A focus on real-time processing for financial services

To illustrate the above points, the collaboration applies data engineering and MLOps best practices to a financial services problem. We chose the financial services domain because many financial institutions do not yet have real-time market data processing or MLOps capabilities today, owing to a significant gap on either side of their ML/AI objectives.

Upstream from ML/AI models, financial institutions often experience a data engineering gap. For many financial institutions, batch processes have sufficiently addressed business requirements. As a result, the temporal nature of the time series data underlying these processes is deemphasized. For example, the original purpose of most trade booking systems was to capture a trade and ensure that it found its way to the middle and back office for settlement. It was not built with ML/AI in mind, and its underlying data therefore has not been packaged for consumption by ML/AI processes. 

And downstream from ML/AI models, financial institutions often encounter the aforementioned “ML production problem.”

As ML/AI becomes ever more strategic, these two gaps have left many financial institutions in a conundrum—unable to train ML models for lack of properly packaged time series data, and unmotivated to package time series data for lack of ML models. By recreating a key energy market index using open-source libraries and cloud-based tools, this collaboration demonstrates that for the financial services domain a solution to this conundrum is more accessible today than ever. 

Creating a new index

We modeled our new index after one of CME Group’s many index benchmarks. The particular index expresses the value of a basket of three New York Mercantile Exchange—listed energy futures as a single price. Today, CME Group publishes the index at the end of the day by calculating the settlement price of each underlying futures contract, and then weighing and summing these values. 

While CME Group does not currently publish this index in real time, this collaboration aims to create a near real-time solution leveraging Google Cloud capabilities and CME Group market data delivered via CME Smart Stream. However, in order to publish the value so frequently—every five seconds, with 40-second publish latency—this collaboration’s pipeline has to solve a number of challenges in near-real time.

First, the pipeline must process sparse data from three separate trades feeds in memory to create open-high-low-close (OHLC) bars. More specifically, for five-second windows for each of the three front-month (and sometimes second-month) energy contracts, a bar must be produced. This is solved by using the Apache Beam library to implement functions which, when executed on Dataflow, automatically scale out as input load increases. The bars must be time-aligned across the underlying feeds, which is greatly simplified by Beam’s watermark feature. And for intervals in which no tick data is observed, the Beam library is used to pull forward the last value received, yielding perfect gap-free bars for downstream processors.

Second, the pipeline must calculate volume-weighted average price (VWAP) in near real-time for each front-month contract. The VWAP calculations are also written using the Beam API and executed on Dataflow. Each of these functions requires visibility of each element in the time window, so the functions cannot be arbitrarily scaled out. Nonetheless, this is tractable because their input—OHLC bars—is manageably small.

Third, the pipeline must replicate CME Group’s specific settlement price methodology for each contract. The rules specify whether to use VWAP or another source as price, depending on certain conditions. They also specify how to weigh combinations of monthly contracts during a roll period. The pipeline again encapsulates these requirements as an Apache Beam class, and joins the separate price streams at the correct time boundary.

The end result is a new stream publishing bespoke index data to a Google Cloud Pub/Sub topic thousands of times daily, enabling AI models as well as traditional industry index usage, dashboards, and other tools to assist real-time decision making. The stream’s pipeline uses open source libraries that solve common time series problems out-of-the box, and cloud-based services to reduce the user’s operational and scaling burden.

The importance of cloud-based data

The promise of cloud-based pipeline execution services cannot be realized using legacy data access patterns, which often require market data users to colocate and configure servers and network gear. Such patterns inject expense and scaling complexity into the pipeline’s overall operation, diverting resources from the adoption of MLOps best practices. Instead, a newer, cloud-based access pattern—in which resources subscribe to data streams inexpensively, rapidly and programatically—is necessary.

In 2018, CME Group identified the customer need for accessible futures and options market data. CME Group collaborated with Google Cloud to launch CME Smart Stream, which distributes CME Group’s real-time market data across Google Cloud’s global infrastructure with sub-second latency. Any customer with a CME Group data usage license and a Google Cloud project can consume this data for an hourly usage fee, without purchasing and configuring servers and network gear.

CME Smart Stream met this index pipeline’s requirements for cost-effective, cloud-based streaming data, but this is just one use case. Since the launch of a CME Smart Stream offering on Google Cloud, globally dispersed firms have adopted the solution. For example, Coin Metrics has been using the offering to better inform its customers in the crypto markets. According to CME Group, Smart Stream has become popular with new customers as the fastest, simplest way to access CME Group’s market data from anywhere in the world. 

Adapt the design pattern to your needs

By combining cloud-based data, open-source libraries, and cloud-based pipeline execution services, we created a real-time index using the same constituents as its end-of-day counterpart. Additionally, financial institutions will find this approach addresses many other challenges—real-time valuation of a large set of portfolios; benchmark creation for new ETPs; or external publication of new indices.

Give it a try

This approach is available to help you meet your organization’s needs. Please review our user guide, whose Tutorials section provides a step-by-step guide to constructing a simple Apache Beam pipeline to generate metrics on streaming data in real-time, and connecting a new data source to the pipeline. We’ll be discussing this topic in CME Group’s webinar End-to-End Market Data Solutions in the Cloud at 10:30 am ET on June 16th.

Editor’s note: Earth Day reminds us that we all can contribute to creating a cleaner, healthier, and more sustainable future. Google Cloud is excited to celebrate innovative startup companies developing new technology and driving sustainable change. On this year’s Earth Day we’re highlighting Enexor BioEnergy and their initiative to produce clean, sustainable energy from plastic and agro-waste with the help of Google Cloud solutions.

Picture a world without electricity where billions of people burn dangerous fuels and trash from landfills to cook meals and heat their homes. Children constantly cough, choking on toxic smoke that lingers in houses and sickens entire families. Because there are no pumps or purification plants, contaminated water is hauled in buckets from dirty rivers and polluted wells. Without power or lights, hospitals and emergency medical clinics can only provide basic services during the day.

Although it is 2022, this harsh life is a reality for billions of people living in developing countries around the world.

Producing clean energy from plastics and organic-waste

This Earth Day, let’s imagine what life would be like in these countries if people had access to safe and inexpensive bioenergy. In this world, indoor air is fresher. Clean water runs from faucets, while homes, businesses, and schools have electricity. Doctors and nurses have the power to treat patients 24 hours a day and save lives with advanced medical equipment. Local economies boom and entrepreneurs thrive.

This is the sustainable future Enexor BioEnergy and its partners are building—one village at a time. With the Enexor Bio-CHP™ system, we produce clean and sustainable energy from discarded plastics, organic, and biomass-waste such as rice and corn husks, seaweed, coconuts, and other biomatter while offsetting significant Carbon emissions. The Bio-CHP can be commissioned and easily installed in just a single day, with multiple systems installed side-by-side when more power is needed. By being offered under its novel Energy-as-a-Service business model, Enexor ensures that the Bio-CHP can generate immediate environmental positive impact and customer adoption.

Each Bio-CHP unit generates enough energy to power over 100 homes—and provides much-needed renewable electricity and thermal power for schools, businesses, manufacturing facilities, water pumps, Wi-Fi systems, and telecommunications towers. The Bio-CHP creates clean energy by safely oxidizing plastics and biomatter in a secure container using a high-temperature system to power a micro-turbine. This produces bioenergy, significantly reducing harmful greenhouse gas emissions.

By solving a community’s waste issues while providing more affordable renewable energy, the Bio-CHP also creates new economic opportunities and improved outcomes where it is installed. This includes using its inexpensive power to expand local services and offerings, and empowering local community clean up efforts by incentivizing waste collection via blockchain-enabled digital currencies and services such as PayGo. Additionally, the Bio-CHP is an ideal solution for businesses who desire to maximize their own sustainability efforts, gain greater energy resiliency, and save money on their current energy and waste costs.

Engaging in Google for Startups Accelerator: Climate Change

When developing the Bio-CHP, we realized we needed a reliable and experienced partner to help us incorporate the best in class machine learning and artificial intelligence into our technology. That’s why we joined the Google for Startups Accelerator and participated in the Google for Startups Accelerator: Climate Change.

The accelerator introduced us to other companies working to create a sustainable future and opened new opportunities for collaboration and partnerships. The accelerator also continues to give our small team access to the best of Google Cloud’s people, programs, and solutions. We especially want to highlight Google Cloud’s dedicated startup experts and the incredible technical support they provide, as well as the Google Cloud research credits we used to explore new solutions.

As an example, Enexor is developing a predictive maintenance tool that automatically adjusts Bio-CHP operations to match fuel composition. Another tool in development proactively identifies and flags potential Bio-CHP system and component failures—before they occur.

Google Cloud’s dedicated startup experts work closely with Enexor to develop predictive models for these tools and Enexor also used the Google Cloud research credits that were provided to build these advanced models on TensorFlow, Vertex AI, Cloud CDN and AutoML. Since each system is remotely monitored from Enexor’s global headquarters in Tennessee, predictive maintenance tools also increase the safety, reliability, and efficiency of Bio-CHP in off-grid locations.

Generating 13 billion kWh of clean power by 2040

In the future, Enexor plans to explore additional Google Cloud solutions such as BigQuery to crunch larger datasets, Google Data Studio to build dashboards, and perhaps even Google Cloud Tensor Processing Units (TPUs) to more efficiently process machine learning (ML) workloads.

These solutions will help Enexor to achieve three primary goals by 2040: to generate 13 billion kWh of clean power, reduce 120 million tons of CO₂, and provide one million people with access to clean and sustainable energy. With each Bio-CHP system, Enexor annually reduces up to 2,000 metric tons of CO₂ equivalent emissions by decreasing methane emissions released from landfills, offsetting fossil fuel-based power generation with carbon credits and minimizing waste disposal transportation emissions.

Enexor is now preparing to deploy the Bio-CHP in Accra, Ghana where it will convert organic and plastic manufacturing waste into sustainable energy. Enexor is also looking forward to working with the local Accra community and NGOs to collect discarded plastics and organic waste diverting it from ending in the rivers and oceans and instead using them as renewable fuel sources. We can’t wait to see what we accomplish next as we celebrate Earth Day 2022 and think about the sustainable future Enexor is helping to empower.

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.

The future of AI is better AI—designed with ethics and responsibility built in from the start. This means putting the brakes on AI-driven transformation until you have a well-functioning strategy and process in place to ensure your models deliver fair outcomes. Failing to recognize this imperative is a threat to your bottom line. The following post provides a simple framework to follow to keep your business on the right track as you place more trust in algorithms. 

AI is inherently sociotechnical. AI systems represent the interconnectedness of humans and technology. They are designed to be used by and to inform humans within specific contexts, and the speed and scale of AI means that any lack of responsibility—such as bias, safety, privacy, scientific excellence etc—will also replicate at that same speed and scale. Without ethics and responsibility built in by design, AI systems lack the critical “inputs” or societal context that enable long term success. 

Lawsuits stemming from AI systems that are biased towards certain groups are stacking up. In August 2020, IBM was forced to settle a lawsuit with the city of Los Angeles for misappropriating data it collected for its weather channel app. Health services company, Optum, is being investigated by regulators for creating an algorithm that allegedly recommended that doctors and nurses pay more attention to white patients than to sicker black patients. And Facebook, which granted Cambridge Analytica, a political firm, access to the personal data of more than 50 million people, is buried in legal work.  Google has also run into its share of issues with algorithms making egregious mistakes. 

While lawsuits are real, the foundational reason ethical AI is critical to your bottom line is trust. Without it, increasingly, consumers will ignore you and choose a brand they do trust. Research from Kantar, which runs one of the largest global brand equity studies (4 million consumers, 18,000 brands, across 50 markets), revealed that almost 9% of a brand’s equity is driven by corporate reputation, of which responsibility is a key attribute. Over the last decade, the importance of responsibility to consumers in relation to making brand choices has tripled. 

The study stated brands perceived to be among the world’s most trusted and responsible shared three crucial factors that proved particularly important for building consumer trust and confidence, even when a brand might be new to a market. These are:

• Honesty and openness
• Respect and inclusion
• Identifying with and caring for customers

Brands that develop these associations more strongly tend to outperform their competitors in defending and growing their brand value.

Technology and business leaders need to focus on four areas to accomplish a well-functioning ethical AI strategy. Lopez Research refers to this group of tasks as SEED, which stands for security, ethics, explainability, and data (SEED). Each of these topics could be an article in itself, but this post will define several essential components. 

SECURITY (S)  

It might not seem obvious, but a robust AI strategy requires an embedded security strategy. Companies should look for hardware-level security in components such as GPUs and CPUs. IT leaders should build software security into models to minimize attacks such as poisoning, evasion, deepfakes, backdoors, and model extraction. The threat of adversarial data poisoning attacks machine learning models by maliciously introducing inaccurate data designed to corrupt the model’s ability to be accurate. Another security threat is model extraction, also known as model cloning, where a hacker finds a way to either reconstruct a black-box machine learning model or extract the training data. The first line of defense against all security attacks is to design security at the outset, but the next best step is to frequently test models to ensure they are operating as planned. Business leaders, data science experts, and IT leaders must work together to regularly review the outcomes of AI models.

ETHICS (E)

Today, organizations must understand that ethics should be designed into the solution at its outset. The ethics process starts with defining the potential positive and negative outcomes of the model that your business is creating. Once the team has evaluated potential harmful effects, which means unpacking the systems, beliefs, power hierarchies and dynamics that interconnect with the technology, it’s your responsibility to eliminate or minimize the impact of these outcomes. It’s also critically important to review the impact of models in production and shut down models demonstrating issues. An example of this was the public beta release of the Tay chatbot that Microsoft deployed and rapidly shut down because it propagated negative biases. 

Yet, many organizations aren’t taking this action. The FICO study revealed that 93% of companies said responsible AI was critical for success but only 33% of these companies were measuring AI model outputs to ensure these models were operating as expected (measuring for model drift). Another survey by Pew Research revealed that 68% believe that ethical principles focused primarily on the public good will not be employed in most AI systems by 2030. 

Regulations may turn this tide, regardless of whether organizations plan to adopt an ethical AI framework. Laws governing the ethical use of data in AI are expected to be finalized as soon as 2022, such as the European Commission’s proposed legal framework for AI. Organizations that start with ethical use of AI in mind will be better positioned to deal with customer privacy concerns and regulatory compliance.

EXPLAINABILITY(E)

As models have become more sophisticated, it’s also become increasingly difficult to explain why a model created a specific outcome. In the FICO Responsible AI  report, 65% of respondents could not explain how specific AI model decisions or predictions are made, and only 35% said their organization made an effort to use AI in a way that was transparent and accountable.  However, it’s never been more important to clarify how AI models came to conclusions such as why a loan was denied, why a particular strategy should be implemented, and how AI selected a set of resumes to review for a position. The goal is to create an explainable AI model from the outset but many of today’s models lack this capability. Every business should review its existing models and use open-source toolkits that can be found on Github.com that support the interpretability and explainability of machine learning models. 

Keep in mind that explainability isn’t one-size-fits-all. Different stakeholders need different types of information. Much of explainability to date has focused on “opening the black box” which gets equated to information that is only useful for other data scientists. That’s important, but it doesn’t help the line of business users whose workflows AI is integrated into, or end users who deserve information about how decisions are made; or policymakers who don’t have data science backgrounds, and so on. 

DATA (D) 

An equally important item in ethics is data. Ethics starts with ensuring you have the correct data to create and update models. Three main issues include representative data, inherent biases within existing data, and inaccurate data. A critical issue that most companies miss in creating models is that current data sets frequently lack full market representation. A recent Capgemini Research Institute report revealed that 65% of executives “were aware of the issue of discriminatory bias” with these systems.

Awareness is the first step, but organizations must take action to remedy this issue. Historical data may no longer serve a company’s current needs for model creation. Historical records may contain biases against certain groups. For example, historical criminal data records show an imbalance in ethnic groups’ incarceration, which would lead to model biases. Additionally, laws and societal norms also change. Certain groups were prosecuted for sexual preference in the past, but today this information would create an inaccurate model. 

Companies have also discovered that using demographic data, a common practice in marketing, can also lead to model bias. For example, individuals that primarily used cash for transactions and others that lived in specific zip codes were at a disadvantage in banking models to determine creditworthiness. To minimize these issues, a company needs to augment its data with full representation in areas such as ethnicity, gender, age, behavioral and economic profiles.

Design AI models with a continuous feedback loop

Another, more prominent, yet tricky issue is data accuracy. As the adage says, garbage in, equals garbage out. The least appreciated but arguably the most essential component of the AI model lifecycle is ensuring the model has accurate data at all times. Inaccurate data from either poor data hygiene or data that was tampered with for security purposes can cause model failures. Organizations need to invest the time and resources to ensure they have the correct data. Data privacy is another key element that businesses must address, but the concepts of data privacy, sovereignty, and security are significant enough that we will come back to this in a separate article. 

Overall, it’s clear that while we may have an abundance of data, it most likely doesn’t represent what we want to model for the future. A successful AI strategy is an ethical AI strategy that requires the organization to be thoughtful in its model creation by ensuring it has a broad representation of accurate data and testing the outcomes to ensure the models are secure and operating as expected. 

Organizations that define an AI model lifecycle with a continuous feedback loop will reap the benefits of better intelligence. This will increasingly mean stronger, longer lasting trust with customers and staying on the right side of new laws and regulations.