How can AI provide value to information services companies?<\/h2>\nExecutives and information services leaders generally approach generative AI in one of two ways: jumping in head first, or taking the \u201cwait-and-see\u201d approach.\n\nAlthough those approaches seem like polar opposites, they\u2019re both tech-based decision-making frameworks, not value-based. Here\u2019s the difference:\n\n \t- Tech-based approach<\/b>\u2014should we adopt this new technology?<\/li>\n \t
- Value-based approach<\/b>\u2014can this new technology add user and business value?<\/li>\n<\/ul>\nBecause information services companies live and breathe data\u2014often proprietary\u2014you have a unique competitive advantage to offer complex, comprehensive, and time-sensitive insights that no one else can. This is a gold mine for AI development, and if you\u2019re not capitalizing on it, then you\u2019re going to fall behind.\n\nBut it goes deeper than that. There are two broad categories of AI adoption: everyday and game-changing. Game-changers not only have the potential to realize an ROI on AI adoption, but can outpace competitors by redefining the terms of engagement and delivering customer value that\u2019s exponential\u2014not incremental.\n
The 5 critical areas of AI readiness<\/h2>\nWhether you anticipate adopting AI tomorrow or later in 2024, taking these steps now will set you up for success and enable you to move quickly when the time comes.\n1. Data<\/h3>\nAI rises or falls depending on the data used to build and train it. At the risk of over-simplification, it\u2019s an input vs. output situation. Good inputs lead to good outputs. Bad inputs lead to bad outputs.\n\nThe AI development process doesn\u2019t begin when you start building the product, but with a more fundamental component\u2014data. To understand why data is so important, we need to take a step back and discuss some core concepts related to generative AI<\/a>: machine learning and deep learning.\n\n \t- Machine learning<\/b> (ML) is based on data-trained algorithms. These algorithms ingest data, detect patterns, then learn how to make predictions and recommendations based on that data. This differs from traditional programming in that the machine creates its own instructions based on the patterns observed in the training data.<\/li>\n \t
- Deep learning<\/b> is a subset of machine learning that uses neural networks\u2014based on the ways neurons interact in the human brain\u2014to ingest data and process it through multiple iterations. This approach allows the AI to learn increasingly complex attributes of the data itself.<\/li>\n<\/ul>\nIn short, AI\u2019s fundamental functions require high quality data inputs to drive high quality data outputs. This is why 3Pillar advocates having a unified data store<\/a>, processed in the cloud, made available to all your AI models.\n
2. Personnel<\/h3>\nThe people you have on your team and in your corner matter. In addition to data preparedness, here are five types of personnel you need to achieve AI success:\n\n \t- User research experts. <\/b>These people deeply understand users, customer journeys, and customers’ goals, pain points, and jobs to be done.<\/li>\n<\/ul>\n
\n \t- AI product designers. <\/b>These people translate user needs and goals into AI-powered product experiences.<\/li>\n<\/ul>\n
\n \t- AI product managers. <\/b>These people understand the complex interactions of customer needs, business needs, and AI capabilities.<\/li>\n<\/ul>\n
\n \t- AI \/ ML engineers. <\/b>It goes without saying that to build an AI-based product, you need someone who gets how AI and ML work.<\/li>\n<\/ul>\n
\n \t- Data scientists. <\/b>Data quality is key to AI readiness. Fortunately, most information services companies have in-house data scientists who can meet this need.<\/li>\n<\/ul>\n
3. Product strategy<\/h3>\nWhen it comes to digital product success, technology is never a differentiator. No user actually cares about which machine learning model or deep neural network powers your product. Rather, they care about what the product will do for them. <\/i>In other words, value > capabilities.\n\nThis goes back to the tech- vs. value-centricity we mentioned earlier. When preparing for your new AI product, you need to start with a clear understanding of the value you\u2019re delivering before you start the build. <\/i>Otherwise, you\u2019ll waste time and money building something that no user will use\u2014and that you won\u2019t be able to monetize.\n4. User testing<\/h3>\nYou don\u2019t want to build an AI model, think it\u2019s working well, then release it into the wild and have your users break it. You don\u2019t want it generating unusable or false insights because you didn\u2019t test it thoroughly.\n\nThat\u2019s why user testing must be a critical component of building\u2014and validating\u2014your AI-enabled products and customer experiences. Unfortunately, it\u2019s too often an afterthought. A lot of people think they\u2019re really smart on these subjects, but it turns out they\u2019re wrong because they misunderstood the user.\n\nFor example, Casetext (acquired by Thomson Reuters) invested nearly 4,000 hours of training and fine-tuning their AI product, CoCounsel, based on over 30,000 legal questions. The result: a product that was better positioned to provide real user and business value.\n\nInstead, build the prototype, put it into the hands of users, and let them break it. Then figure out what went wrong, iterate, and try again. Once you\u2019re sure you\u2019ve got a product that delivers value to users, only then are you ready to take it to market.\n5. MLOps<\/h3>\nIn addition to data readiness, it\u2019s critical that your machine learning operations (MLOps) are also prepared to manage and productize your generative AI model. The steps that go into the machine learning lifecycle\u2014data ingest, data prep, model training, model tuning, model deployment, model monitoring, explainability, and more\u2014require active management and rigor to avoid producing errors.\n\nMLOps can result in the following benefits:\n\n \t- Driving reproducibility of ML pipelines, enabling more tightly-coupled collaboration across data teams<\/li>\n \t
- Reducing conflict with devops and IT, and accelerating release velocity<\/li>\n \t
- Enabling regulatory scrutiny and drift-check to offer greater transparency and greater compliance with company policies<\/li>\n<\/ul>\nBecause of our extensive experience working with information services companies, 3Pillar Global can help you assess your AI readiness and pursue game-changing AI innovation. We\u2019ll help ensure you\u2019re well positioned to drive transformational change and open up opportunities for additional revenue within your business.\n\nContact 3Pillar Global<\/a> and we\u2019ll help you assess your readiness and help you fill in the gaps.<\/i>\n\n\n
<\/p>\n\n\n
The 5 critical areas of AI readiness<\/h2>\nWhether you anticipate adopting AI tomorrow or later in 2024, taking these steps now will set you up for success and enable you to move quickly when the time comes.\n1. Data<\/h3>\nAI rises or falls depending on the data used to build and train it. At the risk of over-simplification, it\u2019s an input vs. output situation. Good inputs lead to good outputs. Bad inputs lead to bad outputs.\n\nThe AI development process doesn\u2019t begin when you start building the product, but with a more fundamental component\u2014data. To understand why data is so important, we need to take a step back and discuss some core concepts related to generative AI<\/a>: machine learning and deep learning.\n\n \t- Machine learning<\/b> (ML) is based on data-trained algorithms. These algorithms ingest data, detect patterns, then learn how to make predictions and recommendations based on that data. This differs from traditional programming in that the machine creates its own instructions based on the patterns observed in the training data.<\/li>\n \t
- Deep learning<\/b> is a subset of machine learning that uses neural networks\u2014based on the ways neurons interact in the human brain\u2014to ingest data and process it through multiple iterations. This approach allows the AI to learn increasingly complex attributes of the data itself.<\/li>\n<\/ul>\nIn short, AI\u2019s fundamental functions require high quality data inputs to drive high quality data outputs. This is why 3Pillar advocates having a unified data store<\/a>, processed in the cloud, made available to all your AI models.\n
2. Personnel<\/h3>\nThe people you have on your team and in your corner matter. In addition to data preparedness, here are five types of personnel you need to achieve AI success:\n\n \t- User research experts. <\/b>These people deeply understand users, customer journeys, and customers’ goals, pain points, and jobs to be done.<\/li>\n<\/ul>\n
\n \t- AI product designers. <\/b>These people translate user needs and goals into AI-powered product experiences.<\/li>\n<\/ul>\n
\n \t- AI product managers. <\/b>These people understand the complex interactions of customer needs, business needs, and AI capabilities.<\/li>\n<\/ul>\n
\n \t- AI \/ ML engineers. <\/b>It goes without saying that to build an AI-based product, you need someone who gets how AI and ML work.<\/li>\n<\/ul>\n
\n \t- Data scientists. <\/b>Data quality is key to AI readiness. Fortunately, most information services companies have in-house data scientists who can meet this need.<\/li>\n<\/ul>\n
3. Product strategy<\/h3>\nWhen it comes to digital product success, technology is never a differentiator. No user actually cares about which machine learning model or deep neural network powers your product. Rather, they care about what the product will do for them. <\/i>In other words, value > capabilities.\n\nThis goes back to the tech- vs. value-centricity we mentioned earlier. When preparing for your new AI product, you need to start with a clear understanding of the value you\u2019re delivering before you start the build. <\/i>Otherwise, you\u2019ll waste time and money building something that no user will use\u2014and that you won\u2019t be able to monetize.\n4. User testing<\/h3>\nYou don\u2019t want to build an AI model, think it\u2019s working well, then release it into the wild and have your users break it. You don\u2019t want it generating unusable or false insights because you didn\u2019t test it thoroughly.\n\nThat\u2019s why user testing must be a critical component of building\u2014and validating\u2014your AI-enabled products and customer experiences. Unfortunately, it\u2019s too often an afterthought. A lot of people think they\u2019re really smart on these subjects, but it turns out they\u2019re wrong because they misunderstood the user.\n\nFor example, Casetext (acquired by Thomson Reuters) invested nearly 4,000 hours of training and fine-tuning their AI product, CoCounsel, based on over 30,000 legal questions. The result: a product that was better positioned to provide real user and business value.\n\nInstead, build the prototype, put it into the hands of users, and let them break it. Then figure out what went wrong, iterate, and try again. Once you\u2019re sure you\u2019ve got a product that delivers value to users, only then are you ready to take it to market.\n5. MLOps<\/h3>\nIn addition to data readiness, it\u2019s critical that your machine learning operations (MLOps) are also prepared to manage and productize your generative AI model. The steps that go into the machine learning lifecycle\u2014data ingest, data prep, model training, model tuning, model deployment, model monitoring, explainability, and more\u2014require active management and rigor to avoid producing errors.\n\nMLOps can result in the following benefits:\n\n \t- Driving reproducibility of ML pipelines, enabling more tightly-coupled collaboration across data teams<\/li>\n \t
- Reducing conflict with devops and IT, and accelerating release velocity<\/li>\n \t
- Enabling regulatory scrutiny and drift-check to offer greater transparency and greater compliance with company policies<\/li>\n<\/ul>\nBecause of our extensive experience working with information services companies, 3Pillar Global can help you assess your AI readiness and pursue game-changing AI innovation. We\u2019ll help ensure you\u2019re well positioned to drive transformational change and open up opportunities for additional revenue within your business.\n\nContact 3Pillar Global<\/a> and we\u2019ll help you assess your readiness and help you fill in the gaps.<\/i>\n\n\n
<\/p>\n\n\n
- \n \t
- Machine learning<\/b> (ML) is based on data-trained algorithms. These algorithms ingest data, detect patterns, then learn how to make predictions and recommendations based on that data. This differs from traditional programming in that the machine creates its own instructions based on the patterns observed in the training data.<\/li>\n \t
- Deep learning<\/b> is a subset of machine learning that uses neural networks\u2014based on the ways neurons interact in the human brain\u2014to ingest data and process it through multiple iterations. This approach allows the AI to learn increasingly complex attributes of the data itself.<\/li>\n<\/ul>\nIn short, AI\u2019s fundamental functions require high quality data inputs to drive high quality data outputs. This is why 3Pillar advocates having a unified data store<\/a>, processed in the cloud, made available to all your AI models.\n
2. Personnel<\/h3>\nThe people you have on your team and in your corner matter. In addition to data preparedness, here are five types of personnel you need to achieve AI success:\n
- \n \t
- User research experts. <\/b>These people deeply understand users, customer journeys, and customers’ goals, pain points, and jobs to be done.<\/li>\n<\/ul>\n
- \n \t
- AI product designers. <\/b>These people translate user needs and goals into AI-powered product experiences.<\/li>\n<\/ul>\n
- \n \t
- AI product managers. <\/b>These people understand the complex interactions of customer needs, business needs, and AI capabilities.<\/li>\n<\/ul>\n
- \n \t
- AI \/ ML engineers. <\/b>It goes without saying that to build an AI-based product, you need someone who gets how AI and ML work.<\/li>\n<\/ul>\n
- \n \t
- Data scientists. <\/b>Data quality is key to AI readiness. Fortunately, most information services companies have in-house data scientists who can meet this need.<\/li>\n<\/ul>\n
3. Product strategy<\/h3>\nWhen it comes to digital product success, technology is never a differentiator. No user actually cares about which machine learning model or deep neural network powers your product. Rather, they care about what the product will do for them. <\/i>In other words, value > capabilities.\n\nThis goes back to the tech- vs. value-centricity we mentioned earlier. When preparing for your new AI product, you need to start with a clear understanding of the value you\u2019re delivering before you start the build. <\/i>Otherwise, you\u2019ll waste time and money building something that no user will use\u2014and that you won\u2019t be able to monetize.\n
4. User testing<\/h3>\nYou don\u2019t want to build an AI model, think it\u2019s working well, then release it into the wild and have your users break it. You don\u2019t want it generating unusable or false insights because you didn\u2019t test it thoroughly.\n\nThat\u2019s why user testing must be a critical component of building\u2014and validating\u2014your AI-enabled products and customer experiences. Unfortunately, it\u2019s too often an afterthought. A lot of people think they\u2019re really smart on these subjects, but it turns out they\u2019re wrong because they misunderstood the user.\n\nFor example, Casetext (acquired by Thomson Reuters) invested nearly 4,000 hours of training and fine-tuning their AI product, CoCounsel, based on over 30,000 legal questions. The result: a product that was better positioned to provide real user and business value.\n\nInstead, build the prototype, put it into the hands of users, and let them break it. Then figure out what went wrong, iterate, and try again. Once you\u2019re sure you\u2019ve got a product that delivers value to users, only then are you ready to take it to market.\n
5. MLOps<\/h3>\nIn addition to data readiness, it\u2019s critical that your machine learning operations (MLOps) are also prepared to manage and productize your generative AI model. The steps that go into the machine learning lifecycle\u2014data ingest, data prep, model training, model tuning, model deployment, model monitoring, explainability, and more\u2014require active management and rigor to avoid producing errors.\n\nMLOps can result in the following benefits:\n
- \n \t
- Driving reproducibility of ML pipelines, enabling more tightly-coupled collaboration across data teams<\/li>\n \t
- Reducing conflict with devops and IT, and accelerating release velocity<\/li>\n \t
- Enabling regulatory scrutiny and drift-check to offer greater transparency and greater compliance with company policies<\/li>\n<\/ul>\nBecause of our extensive experience working with information services companies, 3Pillar Global can help you assess your AI readiness and pursue game-changing AI innovation. We\u2019ll help ensure you\u2019re well positioned to drive transformational change and open up opportunities for additional revenue within your business.\n\nContact 3Pillar Global<\/a> and we\u2019ll help you assess your readiness and help you fill in the gaps.<\/i>\n\n\n
<\/p>\n\n\n
- Data scientists. <\/b>Data quality is key to AI readiness. Fortunately, most information services companies have in-house data scientists who can meet this need.<\/li>\n<\/ul>\n
- AI \/ ML engineers. <\/b>It goes without saying that to build an AI-based product, you need someone who gets how AI and ML work.<\/li>\n<\/ul>\n
- AI product managers. <\/b>These people understand the complex interactions of customer needs, business needs, and AI capabilities.<\/li>\n<\/ul>\n
- AI product designers. <\/b>These people translate user needs and goals into AI-powered product experiences.<\/li>\n<\/ul>\n
- Deep learning<\/b> is a subset of machine learning that uses neural networks\u2014based on the ways neurons interact in the human brain\u2014to ingest data and process it through multiple iterations. This approach allows the AI to learn increasingly complex attributes of the data itself.<\/li>\n<\/ul>\nIn short, AI\u2019s fundamental functions require high quality data inputs to drive high quality data outputs. This is why 3Pillar advocates having a unified data store<\/a>, processed in the cloud, made available to all your AI models.\n
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