{"id":49761,"topic":"ai","source":"Forbes","title":"The Real Reason AI Investments Fail To Transform The Business - Forbes","url":"https://www.forbes.com/sites/briansolis/2026/07/31/the-real-reason-ai-investments-fail-to-transform-the-business/","url_hash":"94b471e9fcf204a416f6232cca5dc1055e46b88d","author":"","summary":"The Real Reason AI Investments Fail To Transform The Business Forbes","content":"Most AI efforts are generating motion without momentum. New research reveals what leaders must align to move from incremental automation to business reinvention.\nDuring the early space age, many rockets successfully left the launchpad but never reached orbit. They climbed, consumed enormous amounts of fuel, lost velocity, and eventually fell back toward Earth. Their problem was not a lack of effort, it was insufficient thrust to overcome gravity. And today, many enterprises now find themselves in a similar position with AI. Budgets are rising. Pilots are expanding. Copilots are appearing across the enterprise, and agents are beginning to move from demos into workflows. Yet the business results often remain stubbornly earthbound, and siloed.\nBain & Company found that nearly 40% of companies measuring AI cost savings achieved less than 10%, despite targeting savings between 11% and 20%. Still, 90% planned to increase their budgets.\nSAP Concur research found that 38% of finance leaders and 39% of CEOs believe it is still too early to determine whether AI is delivering value. Half of finance leaders said evaluating ROI is their biggest AI deployment challenge.\nThe investment and activity real, but activity is not escape velocity.\nThat tension inspired my latest ServiceNow Futures report, Reaching Escape Velocity: From Automation to AI Business Reinvention. It’s available now, as a free download.\nAs part of the research, I reviewed 29 top reports from technology companies, consulting firms, academic institutions and media organizations, then combined that research with direct executive engagement.\nA consistent pattern emerged, most companies do not have an AI technology problem. They have a gravity problem.\nThe Gravity of Business As Usual\nFor an enterprise, gravity is the accumulated weight of legacy systems, outdated processes, functional silos, familiar metrics and legacy thinking. It is the invisible force pulling every ambitious AI initiative back toward the operating model that produced it.\nThis is not our first encounter with organizational gravity.\nDuring the digital transformation era, many companies modernized analog processes but stopped short of transforming them. Paper forms became digital forms. Old workflows moved to the cloud. Existing business models gained newer interfaces.\nMuch of what passed for transformation was digitization.\nA KPMG survey found that 51% of U.S. businesses had not seen increased performance or profitability from their digital transformation investments.\nAI adoption risks repeating that history, only faster and with substantially higher stakes. Automating yesterday’s work may make it faster and less expensive, but it can also produce a more efficient version of a business designed for a world that no longer exists.\nThe market will always prove over time that you cannot run tomorrow’s business with yesterday’s operating model.\nUnprecedented Technology, Familiar Behavior\nThe technology itself is not moving incrementally.\nResearch from the National Bureau of Economic Research found that nearly 40% of Americans adopted generative AI within two years of ChatGPT’s launch, roughly twice the internet’s adoption rate at the same stage. By late 2025, a Brookings Institution survey estimated that 57% of U.S. adults, approximately 149 million people, were using generative AI regularly.\nThe speed is extraordinary. The breadth is even more consequential.\nGenerative AI is not merely another application category. It can reshape how organizations understand customers, make decisions, develop products, coordinate operations, manage risk and create value. Agentic AI expands the shift by allowing systems to pursue goals, coordinate across applications and take action with varying degrees of autonomy.\nYet while AI capabilities advance exponentially, the typical organizational response remains incremental.\nCompanies add AI to individual tasks, distribute horizontal tools, celebrate adoption rates and count hours saved. Those gains can be useful, but they rarely alter the economics, experience, or trajectory of the business.\nThe most important question is no longer, “Where can we use AI?”\nIt needs to be, “What would we design differently if intelligence were available across the enterprise from the beginning?”\nOne Atmosphere, Three Trajectories\nAcross the research, AI adoption clustered into three distinct modes.\nThe first is incremental optimization: bolt-on tools, isolated pilots and efficiency-oriented use cases layered onto existing systems. This approach produces localized gains but leaves the underlying business largely untouched.\nThe second is AI-forward reinvention: AI becomes embedded in selected end-to-end workflows, with progress measured against business outcomes rather than tool adoption.\nThe third is AI-first reinvention: intelligence becomes a foundational layer of strategy, technology and operations. Workflows, applications, roles, governance and performance measures are redesigned as a connected system.\nThese trajectories may begin with similar technology. They do not arrive at the same destination.\nBCG estimates that 70% of potential AI value is concentrated in core functions such as sales and marketing, manufacturing, supply chain and pricing. Unlocking that value requires more than deploying a copilot. It demands systemic workflow redesign.\nMcKinsey’s Quantum Black research across 25 organizational attributes found that workflow redesign had the greatest effect on EBIT impact of any factor studied.\nPilots prove that AI works toward efficiency. Redesigning the workflows that drive revenue, cost and customer value proves consequential to business transformation.\nReaching Escape Velocity Requires Alignment\nThe report organizes the reinvention challenge around three interconnected disciplines: Think, Build and Operate.\nThink is the leadership trajectory. Reinventors do not begin with a catalogue of use cases. They begin with first principles. They ask how the company would operate, compete and create value if AI were built into its foundation rather than added afterward.\nBuild is the technological thrust. Reinventors move beyond an accumulation of disconnected models and tools. They develop an intentional stack spanning infrastructure, proprietary and consented data, models, agents, applications, orchestration, lifecycle tooling, governance, security and observability.\nOperate sustains the climb. People, processes, organizational design, governance and measurement must evolve with the technology. A BCG analysis attributes only 10% of transformation success to algorithms and 20% to technology, with the remaining 70% tied to people and processes.\nThis is where many strategies lose altitude. Leaders articulate transformation, technologists build automation, and the organization continues operating as it always has.\nEach component may be advancing. They are simply not advancing together.\nThe Executive Mandate\nReaching escape velocity is not the responsibility of the CIO alone. The CEO must define the ambition. The COO must redesign work across functions. The CHRO must prepare people for human-agent collaboration and entirely new roles. The CFO must move measurement from adoption and speculative productivity toward realized business outcomes. Risk, security, legal and compliance leaders must build trust into the architecture before autonomous systems begin acting at scale.\nThe goal is not more AI or AI fluency. The goal is a better business and experiences and outcomes because of AI.\nOnce these elements align, transformation begins to compound. Every redesigned workflow generates better data. Better data improves decisions. Autonomous processes release capacity. New capacity funds the next redesign. Learning from one workflow accelerates the next.\nThat is the moment AI stops behaving like a portfolio of projects and starts operating as a system of reinvention.\nReaching Escape Velocity was written to help leadership teams identify their current trajectory, understand the forces holding them back and build a practical path forward. It is a blueprint for changing the business while leaders still have the agency to shape what comes next.\nYour organization is already in motion.\nThe question is whether it is generating enough momentum to break free legacy debt to achieve escape velcoity.","image_url":"https://imageio.forbes.com/specials-images/imageserve/6a6aeab211d5a9dec1f7885d/0x0.jpg?format=jpg&height=900&width=1600&fit=bounds","lang":"en","published_at":"2026-07-30T12:30:00+00:00","fetched_at":"2026-07-30T13:15:05+00:00","status":"read","starred":0,"extract_state":"ok","summary_auto":"New research reveals what leaders must align to move from incremental automation to business reinvention. 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New research reveals what leaders must align to move from incremental automation to business reinvention.\nDuring the early space age, many rockets successfully left the launchpad but never reached orbit. They climbed, consumed enormous amounts of fuel, lost velocity, and eventually fell back toward Earth. Their problem was not a lack of effort, it was insufficient thrust to overcome gravity. And today, many enterprises now find themselves in a similar position with AI. Budgets are rising. Pilots are expanding. Copilots are appearing across the enterprise, and agents are beginning to move from demos into workflows. Yet the business results often remain stubbornly earthbound, and siloed.\nBain & Company found that nearly 40% of companies measuring AI cost savings achieved less than 10%, despite targeting savings between 11% and 20%. Still, 90% planned to increase their budgets.\nSAP Concur research found that 38% of finance leaders and 39% of CEOs believe it is still too early to determine whether AI is delivering value. Half of finance leaders said evaluating ROI is their biggest AI deployment challenge.\nThe investment and activity real, but activity is not escape velocity.\nThat tension inspired my latest ServiceNow Futures report, Reaching Escape Velocity: From Automation to AI Business Reinvention. It’s available now, as a free download.\nAs part of the research, I reviewed 29 top reports from technology companies, consulting firms, academic institutions and media organizations, then combined that research with direct executive engagement.\nA consistent pattern emerged, most companies do not have an AI technology problem. They have a gravity problem.\nThe Gravity of Business As Usual\nFor an enterprise, gravity is the accumulated weight of legacy systems, outdated processes, functional silos, familiar metrics and legacy thinking. It is the invisible force pulling every ambitious AI initiative back toward the operating model that produced it.\nThis is not our first encounter with organizational gravity.\nDuring the digital transformation era, many companies modernized analog processes but stopped short of transforming them. Paper forms became digital forms. Old workflows moved to the cloud. Existing business models gained newer interfaces.\nMuch of what passed for transformation was digitization.\nA KPMG survey found that 51% of U.S. businesses had not seen increased performance or profitability from their digital transformation investments.\nAI adoption risks repeating that history, only faster and with substantially higher stakes. Automating yesterday’s work may make it faster and less expensive, but it can also produce a more efficient version of a business designed for a world that no longer exists.\nThe market will always prove over time that you cannot run tomorrow’s business with yesterday’s operating model.\nUnprecedented Technology, Familiar Behavior\nThe technology itself is not moving incrementally.\nResearch from the National Bureau of Economic Research found that nearly 40% of Americans adopted generative AI within two years of ChatGPT’s launch, roughly twice the internet’s adoption rate at the same stage. By late 2025, a Brookings Institution survey estimated that 57% of U.S. adults, approximately 149 million people, were using generative AI regularly.\nThe speed is extraordinary. The breadth is even more consequential.\nGenerative AI is not merely another application category. It can reshape how organizations understand customers, make decisions, develop products, coordinate operations, manage risk and create value. Agentic AI expands the shift by allowing systems to pursue goals, coordinate across applications and take action with varying degrees of autonomy.\nYet while AI capabilities advance exponentially, the typical organizational response remains incremental.\nCompanies add AI to individual tasks, distribute horizontal tools, celebrate adoption rates and count hours saved. Those gains can be useful, but they rarely alter the economics, experience, or trajectory of the business.\nThe most important question is no longer, “Where can we use AI?”\nIt needs to be, “What would we design differently if intelligence were available across the enterprise from the beginning?”\nOne Atmosphere, Three Trajectories\nAcross the research, AI adoption clustered into three distinct modes.\nThe first is incremental optimization: bolt-on tools, isolated pilots and efficiency-oriented use cases layered onto existing systems. This approach produces localized gains but leaves the underlying business largely untouched.\nThe second is AI-forward reinvention: AI becomes embedded in selected end-to-end workflows, with progress measured against business outcomes rather than tool adoption.\nThe third is AI-first reinvention: intelligence becomes a foundational layer of strategy, technology and operations. Workflows, applications, roles, governance and performance measures are redesigned as a connected system.\nThese trajectories may begin with similar technology. They do not arrive at the same destination.\nBCG estimates that 70% of potential AI value is concentrated in core functions such as sales and marketing, manufacturing, supply chain and pricing. Unlocking that value requires more than deploying a copilot. It demands systemic workflow redesign.\nMcKinsey’s Quantum Black research across 25 organizational attributes found that workflow redesign had the greatest effect on EBIT impact of any factor studied.\nPilots prove that AI works toward efficiency. Redesigning the workflows that drive revenue, cost and customer value proves consequential to business transformation.\nReaching Escape Velocity Requires Alignment\nThe report organizes the reinvention challenge around three interconnected disciplines: Think, Build and Operate.\nThink is the leadership trajectory. Reinventors do not begin with a catalogue of use cases. They begin with first principles. They ask how the company would operate, compete and create value if AI were built into its foundation rather than added afterward.\nBuild is the technological thrust. Reinventors move beyond an accumulation of disconnected models and tools. They develop an intentional stack spanning infrastructure, proprietary and consented data, models, agents, applications, orchestration, lifecycle tooling, governance, security and observability.\nOperate sustains the climb. People, processes, organizational design, governance and measurement must evolve with the technology. A BCG analysis attributes only 10% of transformation success to algorithms and 20% to technology, with the remaining 70% tied to people and processes.\nThis is where many strategies lose altitude. Leaders articulate transformation, technologists build automation, and the organization continues operating as it always has.\nEach component may be advancing. They are simply not advancing together.\nThe Executive Mandate\nReaching escape velocity is not the responsibility of the CIO alone. The CEO must define the ambition. The COO must redesign work across functions. The CHRO must prepare people for human-agent collaboration and entirely new roles. The CFO must move measurement from adoption and speculative productivity toward realized business outcomes. Risk, security, legal and compliance leaders must build trust into the architecture before autonomous systems begin acting at scale.\nThe goal is not more AI or AI fluency. The goal is a better business and experiences and outcomes because of AI.\nOnce these elements align, transformation begins to compound. Every redesigned workflow generates better data. Better data improves decisions. Autonomous processes release capacity. New capacity funds the next redesign. Learning from one workflow accelerates the next.\nThat is the moment AI stops behaving like a portfolio of projects and starts operating as a system of reinvention.\nReaching Escape Velocity was written to help leadership teams identify their current trajectory, understand the forces holding them back and build a practical path forward. It is a blueprint for changing the business while leaders still have the agency to shape what comes next.\nYour organization is already in motion.\nThe question is whether it is generating enough momentum to break free legacy debt to achieve escape velcoity.","excerpt":"New research reveals what leaders must align to move from incremental automation to business reinvention. 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During the early space age, many rockets successfully left the launchpad but never reached orbit.","source":"Forbes","date":"2026-07-30T12:30:00+00:00","content":"Most AI efforts are generating motion without momentum. New research reveals what leaders must align to move from incremental automation to business reinvention.\nDuring the early space age, many rockets successfully left the launchpad but never reached orbit. They climbed, consumed enormous amounts of fuel, lost velocity, and eventually fell back toward Earth. Their problem was not a lack of effort, it was insufficient thrust to overcome gravity. And today, many enterprises now find themselves in a similar position with AI. Budgets are rising. Pilots are expanding. Copilots are appearing across the enterprise, and agents are beginning to move from demos into workflows. Yet the business results often remain stubbornly earthbound, and siloed.\nBain & Company found that nearly 40% of companies measuring AI cost savings achieved less than 10%, despite targeting savings between 11% and 20%. Still, 90% planned to increase their budgets.\nSAP Concur research found that 38% of finance leaders and 39% of CEOs believe it is still too early to determine whether AI is delivering value. Half of finance leaders said evaluating ROI is their biggest AI deployment challenge.\nThe investment and activity real, but activity is not escape velocity.\nThat tension inspired my latest ServiceNow Futures report, Reaching Escape Velocity: From Automation to AI Business Reinvention. It’s available now, as a free download.\nAs part of the research, I reviewed 29 top reports from technology companies, consulting firms, academic institutions and media organizations, then combined that research with direct executive engagement.\nA consistent pattern emerged, most companies do not have an AI technology problem. They have a gravity problem.\nThe Gravity of Business As Usual\nFor an enterprise, gravity is the accumulated weight of legacy systems, outdated processes, functional silos, familiar metrics and legacy thinking. It is the invisible force pulling every ambitious AI initiative back toward the operating model that produced it.\nThis is not our first encounter with organizational gravity.\nDuring the digital transformation era, many companies modernized analog processes but stopped short of transforming them. Paper forms became digital forms. Old workflows moved to the cloud. Existing business models gained newer interfaces.\nMuch of what passed for transformation was digitization.\nA KPMG survey found that 51% of U.S. businesses had not seen increased performance or profitability from their digital transformation investments.\nAI adoption risks repeating that history, only faster and with substantially higher stakes. Automating yesterday’s work may make it faster and less expensive, but it can also produce a more efficient version of a business designed for a world that no longer exists.\nThe market will always prove over time that you cannot run tomorrow’s business with yesterday’s operating model.\nUnprecedented Technology, Familiar Behavior\nThe technology itself is not moving incrementally.\nResearch from the National Bureau of Economic Research found that nearly 40% of Americans adopted generative AI within two years of ChatGPT’s launch, roughly twice the internet’s adoption rate at the same stage. By late 2025, a Brookings Institution survey estimated that 57% of U.S. adults, approximately 149 million people, were using generative AI regularly.\nThe speed is extraordinary. The breadth is even more consequential.\nGenerative AI is not merely another application category. It can reshape how organizations understand customers, make decisions, develop products, coordinate operations, manage risk and create value. Agentic AI expands the shift by allowing systems to pursue goals, coordinate across applications and take action with varying degrees of autonomy.\nYet while AI capabilities advance exponentially, the typical organizational response remains incremental.\nCompanies add AI to individual tasks, distribute horizontal tools, celebrate adoption rates and count hours saved. Those gains can be useful, but they rarely alter the economics, experience, or trajectory of the business.\nThe most important question is no longer, “Where can we use AI?”\nIt needs to be, “What would we design differently if intelligence were available across the enterprise from the beginning?”\nOne Atmosphere, Three Trajectories\nAcross the research, AI adoption clustered into three distinct modes.\nThe first is incremental optimization: bolt-on tools, isolated pilots and efficiency-oriented use cases layered onto existing systems. This approach produces localized gains but leaves the underlying business largely untouched.\nThe second is AI-forward reinvention: AI becomes embedded in selected end-to-end workflows, with progress measured against business outcomes rather than tool adoption.\nThe third is AI-first reinvention: intelligence becomes a foundational layer of strategy, technology and operations. Workflows, applications, roles, governance and performance measures are redesigned as a connected system.\nThese trajectories may begin with similar technology. They do not arrive at the same destination.\nBCG estimates that 70% of potential AI value is concentrated in core functions such as sales and marketing, manufacturing, supply chain and pricing. Unlocking that value requires more than deploying a copilot. It demands systemic workflow redesign.\nMcKinsey’s Quantum Black research across 25 organizational attributes found that workflow redesign had the greatest effect on EBIT impact of any factor studied.\nPilots prove that AI works toward efficiency. Redesigning the workflows that drive revenue, cost and customer value proves consequential to business transformation.\nReaching Escape Velocity Requires Alignment\nThe report organizes the reinvention challenge around three interconnected disciplines: Think, Build and Operate.\nThink is the leadership trajectory. Reinventors do not begin with a catalogue of use cases. They begin with first principles. They ask how the company would operate, compete and create value if AI were built into its foundation rather than added afterward.\nBuild is the technological thrust. Reinventors move beyond an accumulation of disconnected models and tools. They develop an intentional stack spanning infrastructure, proprietary and consented data, models, agents, applications, orchestration, lifecycle tooling, governance, security and observability.\nOperate sustains the climb. People, processes, organizational design, governance and measurement must evolve with the technology. A BCG analysis attributes only 10% of transformation success to algorithms and 20% to technology, with the remaining 70% tied to people and processes.\nThis is where many strategies lose altitude. Leaders articulate transformation, technologists build automation, and the organization continues operating as it always has.\nEach component may be advancing. They are simply not advancing together.\nThe Executive Mandate\nReaching escape velocity is not the responsibility of the CIO alone. The CEO must define the ambition. The COO must redesign work across functions. The CHRO must prepare people for human-agent collaboration and entirely new roles. The CFO must move measurement from adoption and speculative productivity toward realized business outcomes. Risk, security, legal and compliance leaders must build trust into the architecture before autonomous systems begin acting at scale.\nThe goal is not more AI or AI fluency. The goal is a better business and experiences and outcomes because of AI.\nOnce these elements align, transformation begins to compound. Every redesigned workflow generates better data. Better data improves decisions. Autonomous processes release capacity. New capacity funds the next redesign. Learning from one workflow accelerates the next.\nThat is the moment AI stops behaving like a portfolio of projects and starts operating as a system of reinvention.\nReaching Escape Velocity was written to help leadership teams identify their current trajectory, understand the forces holding them back and build a practical path forward. It is a blueprint for changing the business while leaders still have the agency to shape what comes next.\nYour organization is already in motion.\nThe question is whether it is generating enough momentum to break free legacy debt to achieve escape velcoity.","confidence":0.9,"diagnostics_url":"/api/diagnose?url=https%3A//www.forbes.com/sites/briansolis/2026/07/31/the-real-reason-ai-investments-fail-to-transform-the-business/","quality_bucket":"high","failure_kind":"none","retryable":false,"quality_reason":"High confidence: full text extraction produced 8361 characters.","quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 8361 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":8361,"summary_length":203,"usable_text_length":8361,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":8361,"summary_length":203}},"tags":[]},"fallback_formats":["markdown","json","html"],"actions":{"read":"/item/49761","export_markdown":"/api/items/49761/export?format=markdown","export_json":"/api/items/49761/export?format=json","diagnose":"/api/diagnose?url=https%3A//www.forbes.com/sites/briansolis/2026/07/31/the-real-reason-ai-investments-fail-to-transform-the-business/"},"formats":{"full":{"id":49761,"title":"The Real Reason AI Investments Fail To Transform The Business - Forbes","url":"https://www.forbes.com/sites/briansolis/2026/07/31/the-real-reason-ai-investments-fail-to-transform-the-business/","source":"Forbes","author":null,"published_at":"2026-07-30T12:30:00+00:00","locale":"en","topic":"ai","tags":[],"excerpt":"New research reveals what leaders must align to move from incremental automation to business reinvention. During the early space age, many rockets successfully left the launchpad but never reached orbit.","full_text":"Most AI efforts are generating motion without momentum. New research reveals what leaders must align to move from incremental automation to business reinvention.\nDuring the early space age, many rockets successfully left the launchpad but never reached orbit. They climbed, consumed enormous amounts of fuel, lost velocity, and eventually fell back toward Earth. Their problem was not a lack of effort, it was insufficient thrust to overcome gravity. And today, many enterprises now find themselves in a similar position with AI. Budgets are rising. Pilots are expanding. Copilots are appearing across the enterprise, and agents are beginning to move from demos into workflows. Yet the business results often remain stubbornly earthbound, and siloed.\nBain & Company found that nearly 40% of companies measuring AI cost savings achieved less than 10%, despite targeting savings between 11% and 20%. Still, 90% planned to increase their budgets.\nSAP Concur research found that 38% of finance leaders and 39% of CEOs believe it is still too early to determine whether AI is delivering value. Half of finance leaders said evaluating ROI is their biggest AI deployment challenge.\nThe investment and activity real, but activity is not escape velocity.\nThat tension inspired my latest ServiceNow Futures report, Reaching Escape Velocity: From Automation to AI Business Reinvention. It’s available now, as a free download.\nAs part of the research, I reviewed 29 top reports from technology companies, consulting firms, academic institutions and media organizations, then combined that research with direct executive engagement.\nA consistent pattern emerged, most companies do not have an AI technology problem. They have a gravity problem.\nThe Gravity of Business As Usual\nFor an enterprise, gravity is the accumulated weight of legacy systems, outdated processes, functional silos, familiar metrics and legacy thinking. It is the invisible force pulling every ambitious AI initiative back toward the operating model that produced it.\nThis is not our first encounter with organizational gravity.\nDuring the digital transformation era, many companies modernized analog processes but stopped short of transforming them. Paper forms became digital forms. Old workflows moved to the cloud. Existing business models gained newer interfaces.\nMuch of what passed for transformation was digitization.\nA KPMG survey found that 51% of U.S. businesses had not seen increased performance or profitability from their digital transformation investments.\nAI adoption risks repeating that history, only faster and with substantially higher stakes. Automating yesterday’s work may make it faster and less expensive, but it can also produce a more efficient version of a business designed for a world that no longer exists.\nThe market will always prove over time that you cannot run tomorrow’s business with yesterday’s operating model.\nUnprecedented Technology, Familiar Behavior\nThe technology itself is not moving incrementally.\nResearch from the National Bureau of Economic Research found that nearly 40% of Americans adopted generative AI within two years of ChatGPT’s launch, roughly twice the internet’s adoption rate at the same stage. By late 2025, a Brookings Institution survey estimated that 57% of U.S. adults, approximately 149 million people, were using generative AI regularly.\nThe speed is extraordinary. The breadth is even more consequential.\nGenerative AI is not merely another application category. It can reshape how organizations understand customers, make decisions, develop products, coordinate operations, manage risk and create value. Agentic AI expands the shift by allowing systems to pursue goals, coordinate across applications and take action with varying degrees of autonomy.\nYet while AI capabilities advance exponentially, the typical organizational response remains incremental.\nCompanies add AI to individual tasks, distribute horizontal tools, celebrate adoption rates and count hours saved. Those gains can be useful, but they rarely alter the economics, experience, or trajectory of the business.\nThe most important question is no longer, “Where can we use AI?”\nIt needs to be, “What would we design differently if intelligence were available across the enterprise from the beginning?”\nOne Atmosphere, Three Trajectories\nAcross the research, AI adoption clustered into three distinct modes.\nThe first is incremental optimization: bolt-on tools, isolated pilots and efficiency-oriented use cases layered onto existing systems. This approach produces localized gains but leaves the underlying business largely untouched.\nThe second is AI-forward reinvention: AI becomes embedded in selected end-to-end workflows, with progress measured against business outcomes rather than tool adoption.\nThe third is AI-first reinvention: intelligence becomes a foundational layer of strategy, technology and operations. Workflows, applications, roles, governance and performance measures are redesigned as a connected system.\nThese trajectories may begin with similar technology. They do not arrive at the same destination.\nBCG estimates that 70% of potential AI value is concentrated in core functions such as sales and marketing, manufacturing, supply chain and pricing. Unlocking that value requires more than deploying a copilot. It demands systemic workflow redesign.\nMcKinsey’s Quantum Black research across 25 organizational attributes found that workflow redesign had the greatest effect on EBIT impact of any factor studied.\nPilots prove that AI works toward efficiency. Redesigning the workflows that drive revenue, cost and customer value proves consequential to business transformation.\nReaching Escape Velocity Requires Alignment\nThe report organizes the reinvention challenge around three interconnected disciplines: Think, Build and Operate.\nThink is the leadership trajectory. Reinventors do not begin with a catalogue of use cases. They begin with first principles. They ask how the company would operate, compete and create value if AI were built into its foundation rather than added afterward.\nBuild is the technological thrust. Reinventors move beyond an accumulation of disconnected models and tools. They develop an intentional stack spanning infrastructure, proprietary and consented data, models, agents, applications, orchestration, lifecycle tooling, governance, security and observability.\nOperate sustains the climb. People, processes, organizational design, governance and measurement must evolve with the technology. A BCG analysis attributes only 10% of transformation success to algorithms and 20% to technology, with the remaining 70% tied to people and processes.\nThis is where many strategies lose altitude. Leaders articulate transformation, technologists build automation, and the organization continues operating as it always has.\nEach component may be advancing. They are simply not advancing together.\nThe Executive Mandate\nReaching escape velocity is not the responsibility of the CIO alone. The CEO must define the ambition. The COO must redesign work across functions. The CHRO must prepare people for human-agent collaboration and entirely new roles. The CFO must move measurement from adoption and speculative productivity toward realized business outcomes. Risk, security, legal and compliance leaders must build trust into the architecture before autonomous systems begin acting at scale.\nThe goal is not more AI or AI fluency. The goal is a better business and experiences and outcomes because of AI.\nOnce these elements align, transformation begins to compound. Every redesigned workflow generates better data. Better data improves decisions. Autonomous processes release capacity. New capacity funds the next redesign. Learning from one workflow accelerates the next.\nThat is the moment AI stops behaving like a portfolio of projects and starts operating as a system of reinvention.\nReaching Escape Velocity was written to help leadership teams identify their current trajectory, understand the forces holding them back and build a practical path forward. It is a blueprint for changing the business while leaders still have the agency to shape what comes next.\nYour organization is already in motion.\nThe question is whether it is generating enough momentum to break free legacy debt to achieve escape velcoity.","reading_time_min":6,"extraction":{"state":"ok","confidence":0.9,"error":null,"explanation":"High confidence: full text extraction produced 8361 characters.","diagnostics_url":"/api/diagnose?url=https%3A//www.forbes.com/sites/briansolis/2026/07/31/the-real-reason-ai-investments-fail-to-transform-the-business/","quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 8361 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":8361,"summary_length":203,"usable_text_length":8361,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":8361,"summary_length":203}}},"quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 8361 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":8361,"summary_length":203,"usable_text_length":8361,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":8361,"summary_length":203}},"actions":{"read":"/item/49761","export_markdown":"/api/items/49761/export?format=markdown","export_json":"/api/items/49761/export?format=json","diagnose":"/api/diagnose?url=https%3A//www.forbes.com/sites/briansolis/2026/07/31/the-real-reason-ai-investments-fail-to-transform-the-business/"}},"digest":{"id":49761,"title":"The Real Reason AI Investments Fail To Transform The Business - Forbes","url":"https://www.forbes.com/sites/briansolis/2026/07/31/the-real-reason-ai-investments-fail-to-transform-the-business/","source":"Forbes","topic":"ai","published_at":"2026-07-30T12:30:00+00:00","excerpt":"New research reveals what leaders must align to move from incremental automation to business reinvention. 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During the early space age, many rockets successfully left the launchpad but never reached orbit.","source":"Forbes","date":"2026-07-30T12:30:00+00:00","content":"Most AI efforts are generating motion without momentum. New research reveals what leaders must align to move from incremental automation to business reinvention.\nDuring the early space age, many rockets successfully left the launchpad but never reached orbit. They climbed, consumed enormous amounts of fuel, lost velocity, and eventually fell back toward Earth. Their problem was not a lack of effort, it was insufficient thrust to overcome gravity. And today, many enterprises now find themselves in a similar position with AI. Budgets are rising. Pilots are expanding. Copilots are appearing across the enterprise, and agents are beginning to move from demos into workflows. Yet the business results often remain stubbornly earthbound, and siloed.\nBain & Company found that nearly 40% of companies measuring AI cost savings achieved less than 10%, despite targeting savings between 11% and 20%. Still, 90% planned to increase their budgets.\nSAP Concur research found that 38% of finance leaders and 39% of CEOs believe it is still too early to determine whether AI is delivering value. Half of finance leaders said evaluating ROI is their biggest AI deployment challenge.\nThe investment and activity real, but activity is not escape velocity.\nThat tension inspired my latest ServiceNow Futures report, Reaching Escape Velocity: From Automation to AI Business Reinvention. It’s available now, as a free download.\nAs part of the research, I reviewed 29 top reports from technology companies, consulting firms, academic institutions and media organizations, then combined that research with direct executive engagement.\nA consistent pattern emerged, most companies do not have an AI technology problem. They have a gravity problem.\nThe Gravity of Business As Usual\nFor an enterprise, gravity is the accumulated weight of legacy systems, outdated processes, functional silos, familiar metrics and legacy thinking. It is the invisible force pulling every ambitious AI initiative back toward the operating model that produced it.\nThis is not our first encounter with organizational gravity.\nDuring the digital transformation era, many companies modernized analog processes but stopped short of transforming them. Paper forms became digital forms. Old workflows moved to the cloud. Existing business models gained newer interfaces.\nMuch of what passed for transformation was digitization.\nA KPMG survey found that 51% of U.S. businesses had not seen increased performance or profitability from their digital transformation investments.\nAI adoption risks repeating that history, only faster and with substantially higher stakes. Automating yesterday’s work may make it faster and less expensive, but it can also produce a more efficient version of a business designed for a world that no longer exists.\nThe market will always prove over time that you cannot run tomorrow’s business with yesterday’s operating model.\nUnprecedented Technology, Familiar Behavior\nThe technology itself is not moving incrementally.\nResearch from the National Bureau of Economic Research found that nearly 40% of Americans adopted generative AI within two years of ChatGPT’s launch, roughly twice the internet’s adoption rate at the same stage. By late 2025, a Brookings Institution survey estimated that 57% of U.S. adults, approximately 149 million people, were using generative AI regularly.\nThe speed is extraordinary. The breadth is even more consequential.\nGenerative AI is not merely another application category. It can reshape how organizations understand customers, make decisions, develop products, coordinate operations, manage risk and create value. Agentic AI expands the shift by allowing systems to pursue goals, coordinate across applications and take action with varying degrees of autonomy.\nYet while AI capabilities advance exponentially, the typical organizational response remains incremental.\nCompanies add AI to individual tasks, distribute horizontal tools, celebrate adoption rates and count hours saved. Those gains can be useful, but they rarely alter the economics, experience, or trajectory of the business.\nThe most important question is no longer, “Where can we use AI?”\nIt needs to be, “What would we design differently if intelligence were available across the enterprise from the beginning?”\nOne Atmosphere, Three Trajectories\nAcross the research, AI adoption clustered into three distinct modes.\nThe first is incremental optimization: bolt-on tools, isolated pilots and efficiency-oriented use cases layered onto existing systems. This approach produces localized gains but leaves the underlying business largely untouched.\nThe second is AI-forward reinvention: AI becomes embedded in selected end-to-end workflows, with progress measured against business outcomes rather than tool adoption.\nThe third is AI-first reinvention: intelligence becomes a foundational layer of strategy, technology and operations. Workflows, applications, roles, governance and performance measures are redesigned as a connected system.\nThese trajectories may begin with similar technology. They do not arrive at the same destination.\nBCG estimates that 70% of potential AI value is concentrated in core functions such as sales and marketing, manufacturing, supply chain and pricing. Unlocking that value requires more than deploying a copilot. It demands systemic workflow redesign.\nMcKinsey’s Quantum Black research across 25 organizational attributes found that workflow redesign had the greatest effect on EBIT impact of any factor studied.\nPilots prove that AI works toward efficiency. Redesigning the workflows that drive revenue, cost and customer value proves consequential to business transformation.\nReaching Escape Velocity Requires Alignment\nThe report organizes the reinvention challenge around three interconnected disciplines: Think, Build and Operate.\nThink is the leadership trajectory. Reinventors do not begin with a catalogue of use cases. They begin with first principles. They ask how the company would operate, compete and create value if AI were built into its foundation rather than added afterward.\nBuild is the technological thrust. Reinventors move beyond an accumulation of disconnected models and tools. They develop an intentional stack spanning infrastructure, proprietary and consented data, models, agents, applications, orchestration, lifecycle tooling, governance, security and observability.\nOperate sustains the climb. People, processes, organizational design, governance and measurement must evolve with the technology. A BCG analysis attributes only 10% of transformation success to algorithms and 20% to technology, with the remaining 70% tied to people and processes.\nThis is where many strategies lose altitude. Leaders articulate transformation, technologists build automation, and the organization continues operating as it always has.\nEach component may be advancing. They are simply not advancing together.\nThe Executive Mandate\nReaching escape velocity is not the responsibility of the CIO alone. The CEO must define the ambition. The COO must redesign work across functions. The CHRO must prepare people for human-agent collaboration and entirely new roles. The CFO must move measurement from adoption and speculative productivity toward realized business outcomes. Risk, security, legal and compliance leaders must build trust into the architecture before autonomous systems begin acting at scale.\nThe goal is not more AI or AI fluency. The goal is a better business and experiences and outcomes because of AI.\nOnce these elements align, transformation begins to compound. Every redesigned workflow generates better data. Better data improves decisions. Autonomous processes release capacity. New capacity funds the next redesign. Learning from one workflow accelerates the next.\nThat is the moment AI stops behaving like a portfolio of projects and starts operating as a system of reinvention.\nReaching Escape Velocity was written to help leadership teams identify their current trajectory, understand the forces holding them back and build a practical path forward. It is a blueprint for changing the business while leaders still have the agency to shape what comes next.\nYour organization is already in motion.\nThe question is whether it is generating enough momentum to break free legacy debt to achieve escape velcoity.","confidence":0.9,"diagnostics_url":"/api/diagnose?url=https%3A//www.forbes.com/sites/briansolis/2026/07/31/the-real-reason-ai-investments-fail-to-transform-the-business/","quality_bucket":"high","failure_kind":"none","retryable":false,"quality_reason":"High confidence: full text extraction produced 8361 characters.","quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 8361 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":8361,"summary_length":203,"usable_text_length":8361,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":8361,"summary_length":203}},"tags":[],"format_contract_version":"news_item_formats.v1"}}}