Latest news with #JasonMissildine
Forbes
7 hours ago
- Business
- Forbes
The Evolution Of AI In Business
Jason Missildine is an executive technology and strategy advisor that helps organizations drive business growth and market expansion. Artificial intelligence has long been part of enterprise technology, evolving from early data-driven models to today's sophisticated systems capable of natural language processing and contextual awareness. These advancements are dramatically improving the way organizations interact with software, automate processes and make strategic decisions. While early AI was limited to predictive analytics and rule-based automation, modern AI systems now engage in real-time learning, adapting dynamically to changing environments. This progress enables businesses to leverage AI not just as a tool for efficiency but as an integral component of decision-making and operational strategy. AI Adoption Across Organizations AI's presence in business is no longer a question of "if"—it's "how soon" and "how deeply." Many organizations utilizing technology today will inevitably integrate AI in some form, but adoption varies based on scale, security needs and industry demands. Small Organizations: Many startups and small businesses turn to low-cost or free AI solutions for automation, customer service and marketing assistance. While these tools offer accessibility, they may lack the necessary security controls and governance frameworks, potentially exposing businesses to vulnerabilities. Larger Enterprises: Medium and large businesses often implement AI through commercial platforms, prioritizing security, compliance and interoperability with core business systems. AI-powered ERP and CRM solutions are transforming workflows, enhancing customer interactions and optimizing operations at scale. However, larger enterprises with unique operational needs frequently require custom-developed AI models or bespoke neural networks tailored to specific business challenges. • Bespoke AI Solutions: Organizations operating in regulated industries, such as finance, healthcare and defense, require AI systems with custom security layers, proprietary algorithms and compliance-driven governance models that off-the-shelf solutions cannot provide. • Industry-Specific AI Optimization: Companies in sectors like manufacturing, supply chain logistics and energy increasingly adopt AI-driven predictive analytics, anomaly detection and autonomous process optimization—often necessitating tailor-made solutions developed in-house or through specialized AI vendors. • Enterprise AI Ecosystems: Leading global enterprises are moving beyond isolated AI implementations and investing in comprehensive AI ecosystems that integrate across departments, self-optimize and continuously evolve based on real-time operational insights. Highly-Niche Or Large Enterprises: Some industries demand highly customized AI solutions tailored to specific challenges, whether it's financial modeling, healthcare diagnostics or predictive maintenance in manufacturing. These organizations often invest in proprietary AI models or develop dedicated neural networks to achieve superior accuracy and reliability. The Future Of AI-Native Systems Today, AI integration often requires substantial effort—analyzing data, securing systems and fine-tuning models to produce highly accurate results. But the next phase of enterprise AI will not be about adaptation; it will be about AI-native architecture. Future systems will be built with AI at their core, meaning software will be inherently intelligent, secure and adaptive. Businesses will no longer need to retrofit AI into existing infrastructures. Instead, AI-first platforms will be automatically optimized, capable of real-time data analysis and built with embedded security features that meet evolving compliance standards. One key advancement will be self-improving AI, where systems autonomously refine their algorithms based on operational data. Instead of executives manually analyzing reports, AI will predict trends, detect inefficiencies and recommend adjustments without human intervention. AI As A New Decision-Maker Imagine an enterprise where AI functions as an embedded strategist—analyzing performance metrics across all departments, identifying bottlenecks and executing improvements. AI-powered business intelligence tools will redefine leadership, enabling executives to make informed decisions backed by real-time insights. The future of AI is not just automation—it's augmentation. Business leaders will rely on AI to enhance creativity, streamline collaboration and eliminate redundant tasks, freeing human talent to focus on innovation rather than day-to-day operational burdens. Risks To Avoid While the promise of AI is vast, organizations must tread carefully to avoid critical missteps. Over-eager implementation without proper planning can lead to data privacy breaches, algorithmic bias, compliance failures and operational disruption. Many AI systems, if poorly trained or inadequately governed, can amplify existing inequities or make decisions based on flawed data. Additionally, excessive reliance on AI without maintaining human oversight may weaken organizational judgment and accountability. To mitigate these risks, leaders must invest in data quality, ensure transparency in AI decision-making and establish rigorous governance frameworks from the outset. An intentional, measured approach—coupled with cross-functional collaboration—ensures AI adoption enhances rather than hinders the business. The End Of Complexity The best technology makes complexity invisible. AI will be no different. As enterprises evolve, and AI is incorporated correctly, automation will feel seamless, intuitive and indispensable. Organizations that embrace this revolution will lead industries, mastering efficiency and agility in ways unimaginable just a decade ago. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
6 days ago
- Business
- Forbes
Measuring AI's Impact And Value: 20 Essential Factors To Consider
getty As AI systems become more embedded in core business functions, traditional metrics like precision and recall capture only part of the picture. Measuring ROI now requires a holistic lens—one that accounts for AI's impact on workflows, decision-making speed and long-term adaptability. Whether a business is assessing its internal AI tools or the AI-powered features included in its products, relying solely on technical benchmarks can result in missing or misinterpreting the broader value—or potential risk—AI systems introduce. Below, members of Forbes Technology Council highlight key factors worth considering when assessing AI success and ROI, explaining why each one offers a more complete view of performance. 1. Hours Reclaimed A practical metric I use to measure AI's ROI is hours reclaimed. I recently rebuilt our GTM messaging across three segments—what previously took 20 hours to do manually, I completed in two, and then in 45 minutes using AI. That time saved is measurable, repeatable and directly tied to productivity gains, reduced errors and faster execution across teams. - Farrukh Mahboob, PackageX 2. Decision Latency Reduction Decision latency reduction is a powerful AI success metric. It measures how quickly AI enables smart, confident decisions, compressing the time between insight and action. Unlike cost savings, this reflects real strategic agility. When decisions speed up, it shows AI is truly embedded in how the business moves. - Jason Missildine, Intentional Intensity Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify? 3. CO2 Usage A metric recently brought into the measurement equation is CO2 usage. Along with tracking more traditional efficiency metrics that showcase faster or cheaper results thanks to an AI system, calculating how much energy it uses provides an offset figure that can be incorporated into evaluations and influence longer-term strategy. - Mark Thirlwell, BSI Group 4. Ethical Outcomes One powerful metric is how well AI systems translate human values into safe, bias-free outcomes that benefit society and stakeholders. More than delivering correct answers, AI systems need to model responsible behaviors, which in turn leads to growth, innovation and a better customer experience. - Vishal Talwar, Wipro Ltd. 5. Contextual Adaptation Quotient Contextual adaptation quotient is a powerful new metric that measures how well AI systems sustain performance across varying domains, users or conditions without retraining. Unlike static accuracy scores, CAQ captures real-world adaptability, highlighting robustness, transferability and long-term ROI in dynamic environments. - Nikhil Jain, SmartThings, Inc. 6. 'Trust Delta' One insightful metric is the 'trust delta,' or how much more (or less) people trust your system after you add AI. You can measure this through user feedback and behavior changes. The smartest AI is useless if people won't use it. If your AI makes people second-guess themselves or feel uneasy, it's actually slowing them down. The trust delta shows whether you're building something people want to work with or work around. - Kehinde Fawumi, Amazon 7. Time To Confidence A genuinely insightful ROI metric for AI systems is time to confidence—how quickly a user reaches a decision they trust. In high-stakes fields like investing, speed alone isn't enough; decisions must also be defensible. - Mike Conover, Brightwave 8. Innovation Rate In my view, the innovation rate metric stands out above all. This tracks the number of new products, services or process improvements directly enabled by AI-driven insights. While ROI focuses on optimizing the present, this metric reveals how effectively AI is building a company's future. A high innovation rate proves AI is not just a cost center, but a strategic engine for growth and market leadership. - Mohan Mannava, Texas Health 9. Autonomy-To-Intervention Ratio A cutting-edge metric is the autonomy-to-intervention ratio, which tracks how long an AI system can operate before needing human correction. It moves beyond traditional KPIs like precision to reveal trust, scalability and operational ROI in real terms. A high AIR means AI isn't just working; it's learning, adapting and truly offloading cognitive burden. - Nicola Sfondrini, PWC 10. Time To Insight Reduction One emerging and insightful metric is time to insight reduction, which is how much more quickly actionable intelligence is derived from data. It reflects the AI system's real-world impact on decision velocity, efficiency and responsiveness, making it a powerful indicator of true ROI beyond cost savings or accuracy alone. - Hrushikesh Deshmukh, Fannie Mae 11. Decision Outcome Improvement The true measure of AI isn't just technical performance, but its real-world impact. Decision outcome improvement quantifies the tangible uplift in valuable results achieved when AI influences a decision, versus the baseline without it. This metric is crucial because it cuts through tech specs to show the practical, profitable difference AI makes, revealing its true ROI where it matters most. - Raghu Para, Ford Motor Company 12. Revenue Per AI Decision Revenue per AI decision is a metric that I find myself looking at quite often. It shows how well an AI system drives actual business outcomes. At our company, if an AI model suggests a payment plan and it closes faster or with higher value, that's measurable success. It ties AI performance directly to bottom-line impact, which matters more than model accuracy or usage stats alone. - Ashish Srimal, Ratio 13. Time To Value Realization One insightful metric is time to value realization, which measures how quickly a company can start deriving business value from an AI implementation. A shorter TTVR indicates efficient deployment, effective user adoption and that the AI is solving a real problem quickly, directly correlating to faster benefits and competitive advantage. - Ambika Saklani Bhardwaj, Walmart Inc. 14. Adaptive Learning Rate One unique metric for measuring AI success is adaptive learning rate, which helps quantify the speed at which an AI system can learn from new data. For instance, in audio processing, a high ALR means an AI can quickly adapt to new accents or background noises, continuously improving without constant retraining. This shows an AI's true long-term value, beyond initial deployment. - Harshal Shah 15. Autonomous Resolution Rate A powerful new metric is autonomous resolution rate, which is the percentage of tasks completed end-to-end by AI agents without human intervention. In ERP/CRM, ARR reflects true ROI by measuring how effectively AI agents handle processes like order creation, invoice matching or case resolution. High ARR signals reduced operational costs, improved efficiency and successful agent adoption at scale. - Giridhar Raj Singh Chowhan, Microsoft 16. Model Utilization Rate One enlightening measure is the model utilization rate—the percentage of the output of an AI model that gets used for decision-making or operations. It's instructive because accuracy is of no consequence if the truths are not acted on. It's a measure of real-world application and trust in AI that demonstrates the relevance and value it has in business. - Saket Chaudhari, TriNet Inc. 17. Feature Abandonment Recovery Feature abandonment recovery is the percentage of users who return to an AI feature after experiencing initial frustration. Most metrics show first-touch success, but this shows resilience. If users give your AI a second chance after it fails them, you've built something valuable. It indicates your AI provides enough value that users forgive mistakes—the ultimate sign of product-market fit. - Marc Fischer, Dogtown Media LLC 18. Resource Efficiency Index The resource efficiency index measures how well AI saves time, effort and resources by reducing manual work and enhancing productivity. Unlike traditional ROI, REI captures indirect benefits such as innovation and agility, providing a holistic view of AI's impact on workforce efficiency and strategic value in modern business operations. - Dileep Rai, Hachette Book Group 19. Access Management Data Access management data provides powerful, real-time metrics that analyze the impact and adoption of technologies and digital systems, such as those using AI. This data offers actionable insights into how tools are being used and their effect on productivity. By mapping usage trends to business outcomes, organizations can identify gaps, optimize training and prove ROI. - Fran Rosch, Imprivata 20. Return On Disruption One novel metric is return on disruption, which measures how AI redefines workflows or business models, not just cost or revenue gains. ROD captures transformative impact, signaling true innovation and long-term competitive advantage rather than incremental efficiency. - Lori Schafer, Digital Wave Technology
