What Is Enterprise AI?
Enterprise artificial intelligence (AI) refers to the wide range of AI-enabled technologies that businesses and other organizations use in their operations. AI can be used for smaller, more siloed tasks such as email or resume sorting. It can also be used for more complex tasks that directly affect the entire enterprise.
Examples of larger-scale AI solutions include supply chain optimization or retrieval augmented generation (RAG), which allows users to grab documents from internal libraries.
What Is AI?
AI is capable of learning, adapting, and exercising complex reasoning on its own — much like human intelligence. It uses techniques such as machine learning (ML), natural language processing (NLP), and computer vision. In contrast, traditional software operates on a set of preprogrammed instructions, which don’t change until a human programmer intervenes.
For an illustration of the difference, think about the spell-check function. In traditional word processing software, spell-check referred to a preset dictionary, highlighting words that were not in that dictionary. Modern spell-check can use machine learning algorithms to grow its vocabulary, adapt itself to context, and suggest more apt corrections.
Some common AI techniques include machine learning, natural language processing, and computer vision. This chart depicts their role in the broader AI field:
| Machine Learning | Natural Language Process | Computer Vision | |
|---|---|---|---|
| Definition | Allowing systems to identify patterns and make predictions from data | Understanding and generating human language | Interpreting and analyzing visual information |
| Use Case | Predictive analytics | Chat bots | Identifying defects on a production line |
These techniques are used to develop AI models, which are trained on specific datasets relevant to the enterprise. Data can come from many sources, including internal sources (e.g., customer and product data), external sources (e.g., smart meters, weather and satellite data), and sensor networks (e.g., monitors installed at a factory). These data-trained models are then used in software to perform specific functions, such as predictive analytics, enabling consumer chatbots, and optimizing quality inspection.
In short, AI is designed to perform tasks that would otherwise require human intelligence in ways that human minds can’t. Cognitive computing, a distinct but related concept, refers to technologies designed to resemble human cognition. The two approaches overlap, as in virtual assistants designed to communicate in a human-like manner with users. Some experts believe that cognitive computing is a subfield of AI. Others believe it is simply a euphemism. Learn more in this article on cognitive computing applications.
AI systems can be standard or generative. Standard AI works with preexisting data to make analyses and recommendations, or to perform tasks. Generative AI (gen AI) can synthesize data that didn’t exist before. For example, it can create texts (such as product descriptions or white papers) and design images based on user prompts. Currently, the most famous example of generative AI is likely OpenAI’s ChatGPT, which launched publicly on November 30, 2022. It is widely considered the fastest-growing consumer application in history.
Generative AI is currently generating the most hype. But plenty of standard AI techniques are highly valuable for the enterprise.
AI can complement existing enterprise resource planning (ERP) systems. In fact, AI functionality is increasingly packaged with ERP platforms. These systems also provide historical data that can be used to train AI models.
Current Adoption of Enterprise AI
“AI has been around for over 30 years,” says Seth Earley, author of The AI-Powered Enterprise and CEO of Earley Information Science. “At the turn of the 2000s, the saying was that no AI ever works, because as soon as it works, it's co-opted by another field and called something else. Word processing was one of the first applications of AI; it took the knowledge and expertise of a human typesetter, and it codified that into rules and approaches that could be used to lay out documents. We don't say, ‘I'm going to use my AI now to create this document.’ We say, ‘I'm going to use Word.’”
In other words, AI is already part of how we work. “Search, you’re already using it,” Earley adds. “Word processing, you’re already using it. Spell-check, you’re already using it. It’s not a matter of if you’re going to use it — you are using it.”
While excitement about AI in the enterprise runs high, enterprise AI adoption at scale is still relatively rare. In late 2023, Intel’s cnvrg.io surveyed 430 technology professionals for its annual ML Insider report. It found that 58 percent of respondents had fewer than 5 AI models running across their organization. Only 11 percent were running more than 50.
Around the same time, IBM surveyed 8,584 IT professionals across multiple continents. It found that 42 percent of organizations with more than 1,000 employees were actively using AI, while 40 percent were currently exploring it. The financial services industry was found to have adopted AI most widely.
The above statistics refer to AI adoption in the enterprise overall. Demand for generative AI is high: Accenture, in its Q2 2024 earnings call, announced that new bookings for generative AI services totaled $1.1 billion through the first half of its fiscal year.
Importance of Enterprise AI
AI for enterprise holds serious potential. AI can solve complex problems that would once have required a great investment of time and money — if they’re solvable at all. It provides companies with crucial predictions and insights.
As more and more companies adopt AI solutions, deploying enterprise AI might seem necessary to remain competitive.
AI could offer major advantages in medical research and drug discovery. It can also help to avert disaster. “A great example is a use case called predictive maintenance,” says Jarvinen. For example, an energy or transportation enterprise could “deploy sensors and other types of data inputs to predict when the pipeline or the energy grid could go down, and then take preemptive action… That can prevent a terrible human tragedy, in the case of a train derailment or a loss of a lot of money in the case of a pipeline or an energy grid going down.”
At the same time, AI could pose serious threats to human welfare and the environment if it is not regulated and monitored responsibly. As of now, the technology is developing faster than the uptake — and faster than the regulatory frameworks we need to ensure it is adopted safely.
It is a cliche that AI is the most significant technological upheaval since the industrial revolution. There is no doubt that AI in the enterprise will have a transformative impact. But this will likely be a cumulative transformation.
“Just like any technological breakthrough, it doesn’t happen immediately, overnight,” says Zachary Scott Jarvinen, author of Enterprise AI for Dummies and Vice President at Exact Payments. “The factories of England or the Model T production line — we look back at them as something magical that happened. The reality is it was a new way of doing things that took tuning and optimization. The same applies for AI.”
Advantages of Enterprise AI
AI can offer significant advantages in the enterprise — if deployed wisely and well. These advantages range from cost savings and greater efficiency. It also offers more strategic gains that reach to the heart of the enterprise and provide greater yields in the long run.
Advantages of enterprise AI include the following:
- Greater Efficiency: AI can streamline repetitive tasks across the enterprise, from screening potential job candidates to sorting and summarizing documents to generating code. This saves time and frees up employees to focus on more complex, value-adding activities.
- Customer Engagement: Chatbots and virtual assistants can provide effective, personalized engagement with customers and clients 24/7. Data from these interactions can be used to improve products and services in the future.
- More Effective Marketing: AI can analyze customer preferences and habits, along with consumer and market data, to produce more targeted and personalized marketing campaigns.
- Increased Security: AI systems can tighten cybersecurity by identifying suspicious patterns in network activity, and responding rapidly to threats. AI pattern detection also mitigates against fraud in the financial services sector.
- Enhanced Decision-Making: AI systems can be trained on vast amounts of data to forecast market conditions and product demand, along with other factors in strategic decision-making.
- Product Innovation: AI systems can forecast demand, identify gaps in the market, and produce insights about a company’s customer base, contributing to product ideation. Generative AI can also help develop those products.
- Quality Assurance: Computer vision systems can analyze images and sensor data to identify issues with physical products.
- Human Health: AI can be used to detect abnormalities in diagnostic images (from MRI or CT scans, for example). It can analyze patient data from disparate sources, enable more sensitive monitoring procedures, and recommend treatment plans.
- Accessibility: AI-powered features such as translation and text-to-speech transcription can assist with communication across language barriers and increase accessibility for users with visual and/or hearing impairment.
Risks of Enterprise AI
The risks associated with enterprise AI are well documented. AI systems are built by human beings, who are prone to error. This makes responsible oversight imperative. AI systems can pose hazards to human well-being and to the environment. They can also pose risks for individual companies, such as copyright and privacy violations.
Here are some of the major risks posed by enterprise AI:
- Reliability: Large language models (LLMs) may produce false or inaccurate responses that nonetheless sound plausible — these are known as hallucinations.
- Bias and Harm: AI models can perform superhuman feats, but they are trained on human-curated datasets and employed for human tasks. Datasets can contain biases, stereotypes, and hurtful content. AI systems trained on those datasets can then reproduce this content, while obscuring its source. For example, financial systems could deny loans or inflate insurance rates based on demographics. Predictive systems could flag someone as a threat based on their race or ethnicity. AI systems used in militaristic endeavors, including autonomous weapons or surveillance systems, can automate decisions about who is targeted for violence. These systems are capable of employing harm at a large scale.
- Copyright Violation: LLMs are trained on huge volumes of text materials. This training data can consist of many copyrighted works and may be used without permission. (ChatGPT has been the subject of lawsuits by media companies, authors, and other creators who have alleged copyright infringement and other violations.) AI companies have argued that this constitutes fair use, but the matter has not yet been settled definitively. Additionally, companies using third-party AI software do not necessarily own the IP they generate.
- Unethical Labor Practices: Tasks such as data labeling and content moderation are often outsourced to workers whose working conditions and wages are sometimes grossly inadequate. Additionally, AI-powered surveillance and tracking systems can subject workers to stringent, depersonalized oversight that risks invading their privacy and negatively affecting their mental health.
- Data Privacy: AI models can be trained on private and proprietary company information, or information that violates privacy standards and agreements. They can also receive sensitive information in the course of use. This data becomes vulnerable to exposure, posing ethical and legal challenges and real-world consequences.
- Job Loss: AI-enabled redundancies will cost people their jobs and leave many without a clear and immediate path to future employment. Over the past two years, workers and labor advocates (including the Writers Guild of America) have raised concerns about the threats to livelihood posed by AI systems.
- Wealth Concentration and Inequality: As AI becomes more capable and more autonomous, companies may eliminate more of their workforces, concentrating economic gain among fewer stakeholders. AI could also pose threats to human well-being and democracy — for example, through the spread of disinformation or by powering autonomous decision-making systems with far-reaching consequences.
- Environmental Impact: Training and running an AI system requires massive computing power that consumes huge amounts of energy and other resources. Data centers and other essential infrastructure leave a significant carbon footprint.
Enterprise AI Use Cases
Enterprise AI can assist with siloed activities, such as resume sorting, or optimize procedures across the entire supply chain. Uses cases vary from industry to industry, but many are widely applicable.
Here is a list of common use cases for enterprise AI:
- Cybersecurity and Fraud Detection: AI systems can detect patterns and identify network abnormalities in real time, reducing the risk of fraud and mitigating threats to cybersecurity. This offers particular advantages for the banking and financial services sector.
- Recommendations: AI algorithms analyze user behavior to offer more personalized content suggestions, service packages, and product recommendations.
- Predictive Maintenance: By analyzing maintenance records, sensor data, and a host of other production factors, AI tools can predict equipment malfunctions before they occur. This reduces downtime and repair costs while increasing safety and productivity.
- Supply Chain Optimization: AI technologies can forecast demand, optimize inventory levels, track goods in transit, and predict shipping route disruptions.
- Medical Diagnosis: AI systems can analyze diagnostic images, such as CT scans and radiology imaging, to detect abnormalities with precision, speed, and consistency. This has the potential to help with diagnosis and treatment planning, as well as improve patient outcomes overall.
- Chatbots and Virtual Assistants: Chatbots are used by many retailers and service providers to help customers troubleshoot and make purchasing decisions. Companies can then draw on the data generated by these interactions.
- Personalized Marketing: AI algorithms analyze customer data (including purchasing and browsing behavior), allowing companies to customize their marketing efforts.
- Quality Assessment: Computer vision systems can analyze images and sensor data to identify defects and inconsistencies in material goods.
- HR Optimization: AI can generate job postings and descriptions, analyze resumes, identify candidates, automate repetitive tasks such as payroll processing, and aid in training and onboarding efforts.
How Have Enterprise Use Cases Evolved
“The earlier stages of use cases were really about the analytics side of things,” Seth Earley explains. “They were about trying to make predictions based on patterns, in fraud detection or risk analysis, where you looked at a large number of transactions. Many applications were about financial management, financial tracking, and things like predictive maintenance — let’s analyze the signals that are coming from our equipment and try to understand when things are going wrong.”
Over time, he adds, these functions became more advanced. In recent years, “it started to move into, how do I build a conversational interface, to do things like offload support documents? How do I improve the customer experience, how can I personalize that experience, how can I take signals from the customer interactions and use those?”
Large language models (LLMs) are much better at interpreting context and nuance, allowing chatbots and virtual assistants to engage more effectively with users and customers. This allows people to interact with technology in new ways. LLMs can allow professionals to access huge volumes of data in much faster and more intuitive ways. Accenture worked with the Spanish Ministry of Justice to design a gen AI-powered search engine that allows users to look up judicial information and receive it in plain language.
Pharmaceutical researchers could use generative AI to extract specific knowledge from scientific publications, trial data, and patent documents, which could assist with drug innovation. Genentech, a major biotech corporation owned by Roche Group, recently announced a partnership with hardware giant NVIDIA to accelerate the “discovery and development” of new drugs.
Unlike standard AI, generative AI is capable of generating content that didn’t exist before. Developers can use it in writing code and debugging. Designers can use image-generation software to mock up their ideas instantly. Writers (or non-writers tasked with writing) can use tools for text generation.
Current Examples of Enterprise AI
Many companies are exploring how they can incorporate AI tools in the way they work. PepsiCo is using enterprise AI to monitor machine maintenance in its factories. Shopify is using AI to enhance the shopping experience for customers and merchants.
PepsiCo has worked with the tech firm Augury to implement predictive maintenance systems throughout its factories. Wireless sensors pick up sounds from factory equipment and transmit the data to a cloud platform. The data is then analyzed by AI software, which is trained on vast amounts of machinery sounds indicating different stages of fitness. The AI-powered system can “hear” when something is wrong before human maintenance workers can and alert them to potential failures and disruptions before they occur.
In April 2023, the commerce platform Shopify launched Magic, a suite of generative AI tools for shoppers and merchants. Magic includes an enhanced search feature for customers, who can describe what they’re looking for in conversational terms and receive more tailored responses. Features for merchants include Sidekick, a commerce assistant that helps with business analysis and decision-making. They also include a product description generator and, as of 2024, an image editing feature that allows sellers to add sleeker backgrounds to their product shots. These tools are particularly helpful for smaller, independent sellers without the budgets for high-end photo shoots or editing capabilities.
BMW has launched a range of gen AI initiatives across its enterprise. On the manufacturing side, BMW collaborated with Zapata Computing to optimize its production scheduling. It also worked with Accenture to create EKHO (Enterprise Knowledge Harmonizer and Orchestrator), a search platform that offers easy, intuitive access to the company’s vast stores of internal data. EKHO is meant to be used across departments: for example, by salespeople responding to customer requests, or by marketing teams refining their campaigns. BMW has also worked with Amazon Web Services to create a cloud assistant to help its DevOps team with infrastructure optimization.
Estée Lauder offers a “voice-enabled makeup assistant” that helps visually impaired users apply cosmetics. The app can “look” at a user’s face to determine how they’ve applied their makeup, then offer audio instruction and feedback to help them apply it more evenly.
Enterprise AI Trends
There is an extraordinary demand for gen AI, at least among major corporations. In addition to brand-new functions and capabilities, AI technologies are becoming more commonplace. ChatGPT was released to the public in November 2022. Within months it had become the fastest-growing consumer app in history. Today, many use it daily without a second thought.
In its recent survey of IT professionals, IBM found that major drivers of AI adoption included “advances in AI tools that make them more accessible” (45 percent) and “the increasing amount of AI embedded into standard off the shelf business applications” (37 percent).
“Increasingly you’re seeing more organizations bake it in,” Earley comments. “It’s under the covers, it’s in their application, and it’s less about understanding data science, and more about understanding business processes and business objectives. AI doesn't have to be mysterious… The use cases have gone from highly technical that only the data scientists and data engineers and computer science experts could understand, to things that more businesspeople can configure and interact with.”
A vanguard area for AI technology is agency: AI models that are empowered to make and execute decisions. “It’s going to be about: How can I get the systems to do something, without human intervention?” Earley says. “This whole idea of building autonomous agents that can then perform actions. A lot of that is kind of built into self-healing networks and technology optimization where, when problems are identified, the system can take action and remediate.”
Companies are increasingly gunning for scalability, expanding AI solutions across their value chain. Many of the newer use cases for enterprise generative AI have to do with knowledge empowerment: compiling internal data from many disparate sources and making it easy to retrieve. AI systems can point users to documents within vast libraries that would be very hard to navigate otherwise.
“I think the biggest impact of any AI on any organization is going to be retrieval augmented generation (RAG),” Earley says. “Which is saying: I’m not going to ask the LLM or the GPT for the answer itself. I'm going to ask it to find that answer in my database, in my knowledge base, and then present it conversationally. So you can make faster and better decisions, and get information in people’s hands so that you can go to market more quickly. You can respond to customer problems and employee questions more quickly. People will no longer be retrieving stuff all over the place and asking questions over and over again. You'll talk to your systems.”
Areas of Recent Advancements in Enterprise AI
According to Zachary Scott Jarvinen, a major breakthrough for enterprise AI has been the usability of unstructured data. Structured data is formatted in a way that makes it easy to sort and search. Unstructured data refers to items such as text files, social media posts, video files, and images, which don’t follow typical data models and aren’t sortable in the same way.
“The unstructured data has just been sitting there,” Jarvinen says. “But because of things like LLMs and the ability of AI now to intake unstructured data in addition to structured, that opens up another [dimension]. Some people say 98 or 99 percent of the data out there is in unstructured words, not in rows and columns. That’s what’s now becoming available.”
“AI’s only as good as the data set,” Jarvinen continues. “We just made the data set 99 percent better because we’re able to take in that unstructured stuff as well.” And this makes new use cases possible. “Assisting legal — before, the contracts and the review of documents hasn’t been something AI can touch. Now that’s touchable, and that’s a big industry ripe for disruption. Same thing with the medical profession, and intaking notes from patients or reviewing radiology.” We will still have radiologists, but “they’re going to be prompted and assisted to look more keenly, or to notice patterns.”
“It’ll be a progressive wave as the technology gets better and as the enterprises figure out what they can do — what works for their business and what needs more tuning.” That tuning will include monitoring for fairness and other ethical considerations. “A good example is AI for helping with hiring or employee monitoring. Those are great areas, but there are also risks of bias in AI — it reads the resumes and notices that kids who go to the best schools get to the top of the stack, and maybe that pushes out others, and that’s not fair. So enterprises need to tune the models to make sure that they’re providing the value that’s being sought — which is to provide the best service or drive the most income or hire the best talent.”
Strategic Planning for Enterprise AI
Currently, there is a huge amount of hype about AI for enterprise, and new products are released constantly, promising exciting new AI-enabled capabilities. But to generate real value, AI adoption has to be planned carefully and with the big picture in mind.
We’ll look at strategic planning for enterprise AI from three different perspectives. First, we’ll look at an AI framework, which describes the key components of an AI project and how they interrelate. Second, we’ll look at AI strategy: how AI tools can serve your greater objectives and help your bottom line. Finally, we’ll take a brief look at the specific tools and approaches that companies are using to implement AI.
Enterprise AI Strategic Planning Kit
Download Enterprise AI Strategic Planning Kit
Use this free starter kit to help you plan and implement your enterprise AI projects. This kit includes templates for evaluating use cases, strategic planning, and managing workflow.
In this kit, you’ll find:
- An enterprise AI use case cheat sheet for Microsoft Word and Adobe PDF to help you evaluate relevant enterprise AI use cases.
- An enterprise AI strategic planning checklist for Adobe PDF to help you manage the strategic planning process for enterprise AI.
- An enterprise AI workflow template for Adobe PDF and Microsoft Word to help you visualize and plan your enterprise AI workflow.
Enterprise AI Frameworks
An enterprise AI framework is like a conceptual blueprint for an AI project. It identifies core components and how they relate to one another. Think of it as a way of putting AI enterprise architecture to use.
Here is a framework for applying AI in the enterprise:
- Data Architecture and Management: Data is ingested from multiple sources, stored, and processed for use in AI model training.
- Model Development: AI models are designed, trained, and tested by data scientists and machine learning engineers, using a variety of tools and libraries.
- Model Deployment: The AI models “go live” in production environments, where users interact with them for specific tasks.
- Monitoring and Governance: AI models are watched and evaluated to make sure they’re working as intended and to ensure compliance with policies and regulations
- Integration: AI models are “introduced” and made to interact smoothly with other systems in use across the enterprise.
- Security and Privacy: This includes the various measures used to keep data and IP safe and protected (authentication, authorization, encryption mechanisms).
- User Interfaces and Visualization: The AI must work properly, but it also has to be usable by anyone needing (and authorized) to use it. Accessible interfaces invite users in.
Enterprise AI Strategy
AI systems alone don’t provide strategic advantage. Before you introduce the technology — and incur the costs of doing so — you need to understand how it aligns with your strategic objectives.
- Focus on Data: “There’s no AI without IA — there’s no artificial intelligence without information architecture,” Seth Earley says. “That is number one. Get that reference data in place, analyze and understand your processes, and then build a metrics-driven framework for guiding the decisions and the changes that need to be made on an ongoing basis.”
- Assess Feasibility and Value: Start with what you can do, and work toward longer-term strategic initiatives that leverage your company’s unique strengths.
- Responsibility: Set up governance principles to ensure ethical and compliant AI practices that meet regulatory standards. Test and monitor AI systems for fairness, transparency, and safety.
- Assess the Risks, and How to Mitigate Them: Establish security measures to ensure that systems are safe from incursions and leaks.
- Set Clear Goals and Metrics: Focus on business outcomes such as growth, cost-efficiency, and consumer success.
- Build Competencies and Trust: Invest in reskilling your existing workforce while recruiting new talent.
- Start with Smaller Use Cases: This will minimize risk while allowing for experimentation and provide an opportunity to gather lessons learned.
- Scale Wisely: Use successful pilot outcomes to reinvest short-term gains into longer-term strategic initiatives.
Consult our guide to enterprise project management for more information on implementing AI in the enterprise.
Steps for Implementing Enterprise AI
Implementing enterprise AI is a significant task, but it means starting with the essentials. Companies need to determine their business needs, and how AI systems will help achieve them. They also need to ensure that they have good data for AI systems to work with.
Here are six steps for implementing enterprise AI:
1. Lead With Vision
Your business needs should determine your AI strategy — not the other way around. “The first thing about AI is forget about AI,” Seth Earley advises. “Understand the business problem. What are we trying to accomplish?”
2. Know Your Data
Determine what data you have access to, and how you will store and prepare it for use. Invest in data architecture.
“You can’t automate what you don’t understand,” Earley says. “Start at the data level, then move to the process level, and then to the outcome level, and then to the strategic level. You have linkage up and down that food chain.”
3. Identify Use Cases
Prioritize areas where enterprise AI systems can generate the most value, based on feasibility and strategic alignment.
4. Build Your Team
Determine the necessary skills and competencies required to implement AI in the enterprise. Invest in upskilling employees in addition to recruiting new talent.
5. Build a Development Plan
Establish a framework and select the right tools. Determine timelines and benchmarks.
6. Pilot Projects
Joshua Gilchrist, VP of Strategy and Technology at Marketing Automation Canada, advises enterprises to start wisely: "One of the keys to success for enterprise companies is just taking bite-sized, focused rollouts of pilot projects and proving the use case internally before expanding towards further digital transformation, or investing more deeply in their tech stack.”
7. Integrate and Scale
Integrate AI models with existing enterprise systems. Build out a longer-term plan for scaling, based on insights from pilot projects.
8. Measure and Track
Monitor AI systems for performance and alignment, and adjust as necessary.
9. Prioritize Responsible AI
Make sure your AI practices meet ethical and regulatory standards.
Enterprise AI Workflows
The steps and tasks involved in implementing and running a successful enterprise AI project start with identifying the problem. You’ll need to look at the data, build a model, and test it. Once you deploy the project, you’ll need to monitor its progress and provide governance.
Any enterprise-level AI system will likely involve different people at varying different levels and departments of the organization. For that reason, it’s important to have a clear workflow in place, from start to finish:
- Problem Identification: Identify the problem you’re trying to solve with AI. This should involve high-level stakeholders at the organization, as well as technology professionals: it’s a business problem first, a tech problem second.
- Data Ingestion: Data can come from many sources, including internal sources (e.g. sales and customer data), external sources (e.g. market data, meteorological and satellite data), and sensor networks (e.g. smart meters). Data can also be structured or unstructured.
- Data Preparation: This data must then be prepared: cleaned, preprocessed, and evaluated for bias, quality, and relevance.
- Model Training: At this stage, data scientists and machine learning engineers will use the data to train AI models.
- Model Testing and Evaluation: Models are evaluated for performance and according to governance procedures.
- Integration and Deployment: Integrate the models into existing systems and launch for real-time use.
- Monitoring and Testing: Monitor AI systems continuously to ensure they’re meeting performance metrics and responsibility standards.
- Feedback Analysis: Users and stakeholders — anyone who interacts with the AI system in the course of their job — should recommend improvements. Analyze the feedback and use it to refine the AI models.
- Governance: Throughout the lifecycle, organizations must ensure their AI initiatives are ethical and compliant. Factors to consider are data security, bias, transparency, and explainability.
AI systems can also be used to automate workflows within the organization. “Most AI implementations would have an associated workflow, and sometimes AI can be used to help govern those workflows,” explains Gilchrist.
He gives the example of an ad design project: “There would be checks and balances along the way for the development of a particular asset, perhaps an image-based ad. The designer would conceptualize the ad and then use AI to generate a variety of image assets. Then that person's manager would have to approve whether or not that image meets the brand guidelines and [represents] a good option for them. You could use AI to determine whether or not it meets the brand guidelines, and to help the designer determine which asset might be most likely approved by their manager based on past approvals."
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