[{"innerBlocks":[],"name":"core\/freeform","postId":125816,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"Lately, everyone\u2019s \u2018doing AI.\u2019 Leaders now understand the urgency, but the \u2018how\u2019 remains elusive: according to our research<\/a>, more than 75 percent of executives know how to pilot but are struggling to scale AI across their businesses. But scaling well matters. Our data indicates Strategic Scalers (those who successfully scale their AI) are seeing as much as three times the return on investment, compared with their counterparts stuck in the Proof of Concept (POC) stage. My experience on the ground confirms the stats. My clients frequently ask me how to become great at scaling AI. Here\u2019s what I\u2019ve learned about the key elements to consider if you want to scale AI well, repeatedly.Focus on value: think of your business strategy and AI strategy as one and the same<\/b>If you can define what really matters to your business, you can align your AI agenda to the company\u2019s highest-level strategic goals, e.g., organic growth, or development of new products. The trick is to focus intently on value as it stands today, while at the same time having a vision for tomorrow. Where is your organization and your industry headed, and what does value look like three to five years from now?While you\u2019re thinking about value, get your data strategy in place. It\u2019s the foundation and drives value as much as the AI itself. Strategic Scalers understand this and have a clear design and intent around what data is being captured, in what way, and for what purpose. Reimagine how your business and your people work.<\/b>Deconstruct job roles and look at which tasks will be automated, which will require human-machine collaboration, and how this might change how people and teams intersect and interact\u2014and their skilling needs. That boundary may shift as the organization\u2019s AI maturity increases. For example, applying AI to real-time data can help support decision making\u2014but that doesn\u2019t mean the decision itself is being offloaded to AI. Establish the right talent mix and be mindful of thinking data scientists are the only ones who matter when it comes to creating a route scaling AI. A diverse set of voices and talents \u2013 be they HR, data integration experts, business analysts, marketers, or software engineers, among others. server wiring.jpg<\/bsp-image>When it comes to the build\/buy decision, take advantage of what\u2019s out there for success at speed and scale. Reuse, partner or buy before you even consider building new proprietary technologies in-house. Get responsible AI on the board\u2019s agenda<\/b>We\u2019ve seen cybersecurity and data privacy moved from departmental to board-level issues, and AI should be no different. Responsible governance of AI must be elevated to the boardroom. If a machine\u2019s decision turns out to be erroneous or unlawful, the potential fines and sanctions could threaten the business\u2019s commercial sustainability. Bias and unintended consequences must be \u2018designed out\u2019 as far as practical using robust ethical frameworks (which will have to be defined before legislative change catches up with technology). Obsess about getting to production to realize value <\/b>This is the crux of it and the most confused aspect. Much of the confusion comes from the fact that the AI roadmap looks and feels different from the time-honored software implementation approach. Here\u2019s a scenario I\u2019ve seen more than once: a company spends six months on a proof of concept without considering what is needed to put it into production, the risks of the concept, data bias, data privacy, ethical considerations, etc. And when the time comes to move to production, the team realizes the POC isn\u2019t built for scale and they\u2019ve reinvented the wheel for no clear benefit. To mitigate this risk, we use an AI start-to-end roadmap<\/a> with our clients to define an AI use case's route to live. It lays the path for how to multiply value from the use case through continuous engineering, optimization and the extension of the feature to new use cases.There\u2019s even an argument that the POC will fall away entirely for companies who are well versed in AI already. We may almost be entering a \u2018post-POC\u2019 world, where we now know enough about AI\u2019s potential to skip the pilot altogether and move straight to production in AI-mature businesses.The prize is for the taking.","saveContent":"Lately, everyone\u2019s \u2018doing AI.\u2019 Leaders now understand the urgency, but the \u2018how\u2019 remains elusive: according to our research<\/a>, more than 75 percent of executives know how to pilot but are struggling to scale AI across their businesses. But scaling well matters. Our data indicates Strategic Scalers (those who successfully scale their AI) are seeing as much as three times the return on investment, compared with their counterparts stuck in the Proof of Concept (POC) stage. My experience on the ground confirms the stats. My clients frequently ask me how to become great at scaling AI. Here\u2019s what I\u2019ve learned about the key elements to consider if you want to scale AI well, repeatedly.Focus on value: think of your business strategy and AI strategy as one and the same<\/b>If you can define what really matters to your business, you can align your AI agenda to the company\u2019s highest-level strategic goals, e.g., organic growth, or development of new products. The trick is to focus intently on value as it stands today, while at the same time having a vision for tomorrow. Where is your organization and your industry headed, and what does value look like three to five years from now?While you\u2019re thinking about value, get your data strategy in place. It\u2019s the foundation and drives value as much as the AI itself. Strategic Scalers understand this and have a clear design and intent around what data is being captured, in what way, and for what purpose. Reimagine how your business and your people work.<\/b>Deconstruct job roles and look at which tasks will be automated, which will require human-machine collaboration, and how this might change how people and teams intersect and interact\u2014and their skilling needs. That boundary may shift as the organization\u2019s AI maturity increases. For example, applying AI to real-time data can help support decision making\u2014but that doesn\u2019t mean the decision itself is being offloaded to AI. Establish the right talent mix and be mindful of thinking data scientists are the only ones who matter when it comes to creating a route scaling AI. A diverse set of voices and talents \u2013 be they HR, data integration experts, business analysts, marketers, or software engineers, among others. server wiring.jpg<\/bsp-image>When it comes to the build\/buy decision, take advantage of what\u2019s out there for success at speed and scale. Reuse, partner or buy before you even consider building new proprietary technologies in-house. Get responsible AI on the board\u2019s agenda<\/b>We\u2019ve seen cybersecurity and data privacy moved from departmental to board-level issues, and AI should be no different. Responsible governance of AI must be elevated to the boardroom. If a machine\u2019s decision turns out to be erroneous or unlawful, the potential fines and sanctions could threaten the business\u2019s commercial sustainability. Bias and unintended consequences must be \u2018designed out\u2019 as far as practical using robust ethical frameworks (which will have to be defined before legislative change catches up with technology). Obsess about getting to production to realize value <\/b>This is the crux of it and the most confused aspect. Much of the confusion comes from the fact that the AI roadmap looks and feels different from the time-honored software implementation approach. Here\u2019s a scenario I\u2019ve seen more than once: a company spends six months on a proof of concept without considering what is needed to put it into production, the risks of the concept, data bias, data privacy, ethical considerations, etc. And when the time comes to move to production, the team realizes the POC isn\u2019t built for scale and they\u2019ve reinvented the wheel for no clear benefit. To mitigate this risk, we use an AI start-to-end roadmap<\/a> with our clients to define an AI use case's route to live. It lays the path for how to multiply value from the use case through continuous engineering, optimization and the extension of the feature to new use cases.There\u2019s even an argument that the POC will fall away entirely for companies who are well versed in AI already. We may almost be entering a \u2018post-POC\u2019 world, where we now know enough about AI\u2019s potential to skip the pilot altogether and move straight to production in AI-mature businesses.The prize is for the taking.","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

4 steps to getting practical about scaling AI for success

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