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Artificial intelligence has been the subject of unprecedented levels of investment and enthusiasm over the past three years, driven by a tide of hype that promises revolutionary transformation across every business function. Yet the gap between this technologys promise and the delivery of real business value remains stubbornly wide. A recent study by BCG found that while 98% of companies are exploring AI, only 26% have developed working products and a mere 4% have achieved significant returns on their investments. This striking implementation gap raises a critical question: Why do so many AI initiatives fail to deliver meaningful value? Knowledge gap A big part of the answer lies in a fundamental disconnect at the leadership level: to put it bluntly, many senior executives just dont understand how AI works. One recent survey found that 94% of C-suite executives describe themselves as having an intermediate, advanced, or expert knowledge of AI, while 90% say they are confident in making decisions around the technology. Yet a large study of thousands of U.S. board-level executives reported in MIT Sloan Management Review in 2024 found that just 8% actually have substantial levels of conceptual knowledge regarding AI technologies. The only way AI initiatives can deliver significant value is when they are aligned with the organizations broader enterprise architecture. When I introduced the terminology of strategic enterprise architecture back in 2000 (e-Enterprise, Cambridge University Press), I wanted to emphasize the importance of aligning technical architecture with the broader structure of the business as a wholeits purpose, strategies, processes, and operating models. With AI, this alignment is more important than ever. But it relies on the ability of senior leaders to understand both parts of the enterprise equation. Opportunity costs The current gap between confidence and competence creates a dangerous decision-making environment. Without foundational AI literacy, leaders simply cant make informed decisions about how any given AI implementation fits with strategic priorities and the processes and existing tech infrastructure of the business. Ultimately, they end up delegating critical strategic choices to technical teams that often lack the business context necessary for value-driven implementation. The result? Millions of dollars invested in AI initiatives that fail to deliver on their promises. In addition to project failure, a lack of AI literacy leads to strategic opportunity costs. When CEOs cant distinguish between truly transformative AI applications and incremental improvements, they risk either underinvesting in game-changing capabilities or overspending on fashionable but low-impact technologies. What CEOs need to know Becoming AI-literate doesn’t mean that CEOs need to be able to build neural networks or understand the mathematical intricacies of deep learning algorithms. Rather, leaders need the kind of foundational practical knowledge that lets them align AI initiatives with core business operations and strategic direction. At minimum, CEOs should develop a working understanding of AI in three broad areas. 1. The Types of AI CEOs should understand the differences between the four major types of AI, the business applications of each, and their current maturity level. Analytical/Predictive AI focuses on pattern recognition and forecasting. This technology has been maturing for decades and forms the backbone of data-driven decision making in domains from finance to manufacturing. Deterministic AI systems apply predefined rules and logic to automate processes and decision-making, creating efficiency but requiring careful governance. Generative AIthe current hype kingcreates new content that resembles human work, offering unprecedented creative capabilities alongside significant ethical challenges. Agentic AI is the new kid on the block. It not only analyzes or produces outputs but takes bounded actions toward defined goals. Agentic AI offers the greatest opportunity and the largest risks for enterprise transformation, but is largely untested at scale. 2. Technical Infrastructure Considerations The infrastructure underpinning AI implementations shapes what is possible and practical for specific organizations. Deployment Models determine where and how AI systems operate. On-premises deployments maximize control over data, systems, and compliance but require significant capital investment and specialized personnel. Cloud-based deployments offer scalability and access to cutting-edge hardware but increase exposure to data security and vendor lock-in risks. Hybrid models retain sensitive processes in-house while outsourcing other workloads. Open and Closed Systems. Closed AI systemsproprietary systems created by commercial vendorssimplify deployment and provide enterprise-grade support but normally offer limited transparency and customization. Open (or open source) systems provide greater control and flexibility, particularly for specialized applications, but require more internal capacity and ongoing maintenance. Computing Resource Needs vary dramatically based on how AI is deployed. Most organizations primarily use AI for inference (using the reasoning capabilities of trained models) rather than training their own models. This approach significantly reduces hardware requirements but limits customization and mission-specific capabilities. Data Infrastructure is the foundation for successful AI implementations. This includes data pipelines for collecting and transforming information, storage systems for managing structured and unstructured data, processing frameworks for maintaining data quality, and governance mechanisms for ensuring compliance and security. Organizations with mature data infrastructure can implement AI more rapidly and effectively than those still struggling with data silos or quality issues. 3. The AI Tech Stack The contemporary AI stack comprises five interconnected layers that transform raw data into outputs designed to create value for the enterprise. The Foundation: Data & Storage This foundation captures, cleans, and catalogs both structured and unstructured information. The Engine: Compute & Acceleration High-density Graphics Processing Units (GPUs), AI-optimized chips, and elastic cloud clusters provide the parallel processing that deep-learning workloads require. Container orchestration tools abstract these resources, allowing cost-effective experimentation and deployment. The Brain: Model & Algorithm This is where foundation models, domain-specific small laguage models, and classical machine-learning libraries coexist. Organizations must decide whether to consume models “as-a-service,” fine-tune open-source checkpoints, or build custom networksdecisions that involve trade-offs between control, cost, and compliance. The Connectors: Orchestration & Tooling Retrieval-augmented generation (RAG), prompt pipelines, automated evaluation harnesses, and agent frameworks sequence models into end-to-end capabilities. User Access and Control: Applications & Governance This top layer exposes AI to users through APIs and low-code builders that embed intelligence in user-facing systems. For further foundational information on AI tech stacks, see IBMs introductory guide. Developing AI literacy in the C-Suite How can busy executives develop the AI literacy they need to lead effectively? Here are some practical approaches to closing the knowledge gap. Establish a personal learning curriculum. Set aside time for structured learning about AI fundamentals through executive education programs, books, or online courses specifically designed for business leaders. Build a balanced advisory network. Surround yourself with advisors who bridge technical expertise and business acumen. This might include both internal experts and external consultants who can translate complex concepts into business terms without oversimplifying. Institute regular technology briefings. Create a structured process where technical teams provide regular updates on AI capabilities, limitations, and potential applications in your industry. The key is ensuring these briefings focus on business implications rather than technical specifications. Experience AI directly. Hands-on experience with AI tools provides an essential perspective. Work directly with your company’s AI applications to develop an intuitive understanding of capabilities and limitations. Foster organization-wide literacy. Support AI education across all business functions, not just technical departments. When marketing, finance, operations, and other leaders share a common understanding of AI capabilities, cross-functional collaboration improves dramatically. True leadership in the age of AI begins with curiosity and the courage to learn.When CEOs become tech literate, they dont just adapt to the futurethey help shape it.
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E-Commerce
Miscommunication, missed messages, forgotten requeststhese are the hidden costs of doing business in real estate. In the real estate industry, manual data entry mistakes, such as misallocated expenses or incorrect financial reporting, can cost companies millions annually. Research indicates that manual data entry has an error rate ranging from 1% to 4% and each error can cost up to $25 to rectify. These costs can manifest as missed investment opportunities, unresolved tenant issues, and lost client trust. Real estate professionals cannot afford to ignore these inefficiencies. But it doesnt have to be this way. Automation is transforming real estate by eliminating these friction points, not just for large firms but for small businesses and independent professionals as well. When used correctly, automation doesnt replace the human touchit enhances itbringing clarity, consistency, and professionalism to every interaction. Clarity and consistency through automation In any business, consistency is key to maintaining professionalism. Automation allows companies to standardize processes, ensuring that terms, policies, and interactions are clear and uniform. This not only minimizes misunderstandings but also builds trust with clients and partners. Everyone knowing what to expect reduces disputes and enhances professionalism. In real estate, this is especially true in lease management. Automated enforcement of lease agreements ensures that terms such as late fees, payment deadlines, and maintenance responsibilities are clearly outlined and consistently enforced. Clear, legally compliant lease terms reduce ambiguity and prevent disputes. For real estate agents, investors, and landlords managing their own properties, automation makes it easier to comply with local laws by applying consistent terms across all leases or transactions, protecting interests, and building client trust. Additionally, automated in-app messaging leads to stronger relationships and fewer misunderstandings by providing a centralized platform where landlords and tenants can communicate. Real-time, two-way communication directly within a secure system eliminates the need for scattered text threads, emails, or missed calls. Landlords can track and manage conversations efficiently, while tenants gain a clear channel for addressing concerns, receiving updates, or asking questions. Predictable and respectful communication Automation can also elevate customer interactions by maintaining consistent, respectful communication. In business, this means automated reminders for appointments, follow-ups, or deadlines, ensuring clients are kept informed without feeling overwhelmed or neglected. For property managers and landlords, this is exemplified by automated rent payment reminders and notifications. Rather than sending ad-hoc texts or emailssometimes at inconvenient hours such as early morning or late nightlandlords can set up reminders delivered consistently at the same time of the day and on the same days of the month. This reduces late payments, maintains professionalism, and respects tenants’ personal time. Automation also supports multiple payment methods, offering tenants convenience while providing landlords with a clear, trackable payment history. Streamline management Effective management requires keeping track of tasks, requests, and communications without anything slipping through the cracks. Automation excels at this, offering centralized systems where tasks are logged, prioritized, and tracked. In real estate, this is best seen in maintenance management. Automated systems allow tenants to submit maintenance requests, which are then logged, categorized, and tracked. Tenants can see the status of their requests, reducing repetitive follow-ups, while landlords have a clear record of completed work, costs, and vendor interactions. Enhance client experiences Businesses across industries are increasingly focused on the client journey, ensuring that every touchpoint is smooth and satisfying. In real estate, automation can transform this experience by streamlining scheduling, property showings, and follow-ups. For real estate agents, this might mean automated property match notifications or self-service scheduling tools that allow prospective buyers or renters to book showings without waiting and prevent double bookings. Beyond convenience, automation elevates professionalism. Agents and landlords can maintain consistent follow-ups, ensuring that clients receive timely responses and critical information without delays. This reduces client anxiety, builds trust, and helps real estate professionals create a reputation for reliability and responsiveness. Furthermore, automation can help investors offer value-added services that improve tenants’ financial well-being. For example, credit-boosting features allow tenants to report on-time rent payments to major credit bureaus, helping them build their credit scores over time. This benefits both tenants, who see improved credit, and landlords, who often experience a noticeable increase in on-time payments. Such features make rental properties more attractive to prospective tenants and foster long-term loyalty. Data-Driven Decisions for Smarter Investments Automation is a game-changer for investors who rely on data to drive decision-making. Automated tools can collect and analyze market trends, rental yield data, property valuations, and investment forecasts in real-time. This gives investors immediate access to insights that can guide strategic decisions. Beyond basic data access, automation also allows for customized dashboards where investors can visualize performance metrics across their portfolio. This helps them quickly identify high-performing properties, spot emerging opportunities, and make informed decisions faster than competitors relying on manual research. For instance, RentRedi runs surveys that provide critical insights into landlord behaviors, from how they prepare for tax season to how they screen tenants. Understanding these patterns helps professionals benchmark their practices, anticipate challenges, and make more informed decisions. Additionally, we partner with Chandan Economics to develop data reports that offer broader market insights by tracking trends in rental demand, property values, and landlord investment plans. Access to this kind of data ensures that professionals are not making decisions based on guesswork but on solid, actionable intelligence. Elevating Industry Standards In an industry where professionalism can make or break a deal, automation allows real estate professionals to minimize miscommunication and hidden costs. Businesses that embrace automation can reduce costly errors and deliver consistent, high-quality service that sets them apart. As they raise the bar for professionalism, they gain a competitive edge, build stronger client relationships, and operate more efficiently. This means that even the smallest landlords can adopt best practices once reserved for large corporate investors. Automation allows real estate professionals to focu on higher-value tasks like client relationships and portfolio growth. As automation becomes the norm, professionalism in real estate is becoming the standard, rather than the exception. Ryan Barone is cofounder and CEO of RentRedi.
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E-Commerce
If you break your arm, you get a cast. If your cholesterol is high, you get a prescription. But what happens when what ails you is mental, behavioral, or emotional in nature? Too often, the answer is: nothing. For far too long, our healthcare system has treated the brain as somehow separate from the body. Fact is, mental health is health. One in five U.S. adults are estimated to be living with mental illness, and research suggests that 55% of adults with a mental illness have not received any treatment. The workplace is where many experienced and navigated the COVID pandemic as a collective trauma. Employees have come to expect mental health resources, and in todays high-stress business climate alongside lifes everyday challenges, they are needed now more than ever. Left unchecked, mental and behavioral health conditions (which includes substance use disorders) cost U.S. employers approximately $282 billion each year in absenteeism, productivity declines, and associated healthcare expenses. As business leaders, we can no longer afford to treat mental health as someone elses problem or an after-hours issue. The case for whole health Evidence (and intuition) proves the body and mind are inextricably connected. Mental health conditions like depression can double the risk of developing diabetesand vice versa. Those living with chronic illnesses are far more likely to experience anxiety, depression, or other mental health struggles. Social determinants of health (including loneliness, housing, food security, and transportation) are additional factors. And yet, our systems continue to silo these areas of care. Its time to bridge that divide because all of these issues impact whole health. Treating mental health alongside physical health is the right thing to do for employeesbecause it improves their healthand for employers, as it helps stabilize costs, reduces employee absences, and improves productivity. More importantly, it builds healthier people, at work and in life. Whether its expanding access with digital therapy or integrating behavioral care with primary care, the industry is finally beginning to focus more on the whole personand not just their conditions. Invest in what (and who) matters Wellness apps and lunch-and-learns are a start, but effectively addressing mental health must go further. Serious mental illnesses (SMI), which include conditions like bipolar disorder and severe anxiety, and substance use disorder (SUD) are highly complex and require serious attention, and investment. For example, individuals with SMI face a 53% higher risk of developing cardiovascular diseaseand are 85% more likely to die from it compared to those without SMI. Integrated care for these complex conditions has been shown to improve quality of life and significantly reduce overall healthcare costs. Making programs and resources available for employees with such conditions is more than good medicineits good business. Nearly two million employees receive treatment for SUDs annually, and more than 13 million workers are in some form of recovery, representing 9% of all adults. This population represents a sizable portion of our nations workforce, and employees in recovery often show increased energy, focus, and performance. What employers can do right now As stewards of workforce health and productivity, employers have a unique opportunity to lead in this space. Heres where to start: Enhance benefits. Modern Employee Assistance Programs (EAPs) offer far more than they once did. If you do provide an EAP, but havent scrutinized its options lately, you should. The latest premium models go beyond counseling, digital self-help tools, and expert referrals tailored to employees needs. Some now address social determinants of health and share wellbeing/mindfulness resources, concierge-level support, coaching, and on-site resources. Support all levels of need. Not every employee needs therapy, medication, or more intense carebut every employee needs support for themselves and their families. From digital wellness tools to specialized autism care access, a range of solutions helps meet people where they are. Invest in prevention. Just as we promote physical well-being through wellness incentives, the same must apply to mental and emotional health. For every dollar spent on mental health initiatives, companies can expect a return of $4 due to reduced absenteeism, lower overall healthcare costs, and increased productivity. Partners in progress: Were in this together Ultimately, mental and behavioral health conditions are commonand treatable. By investing time, expertise, funding, and a spirit of partnership, employers can transform lives and workplaces. Thats not just good medicineits good business. At Carelon Behavioral Health, were committed to changing how the system works. With 160 million U.S. adults spending much of their waking hours at work, we know employers are an important key to destigmatizing mental health and unlocking whole health. Its time we accept the fact that mental health is health. It always has been. Bryony Winn is president of Carelon Health. Corbin Petro is president of Carelon Behavioral Health.
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E-Commerce
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