Do companies need AI for everything? No. #62
A critical examination of artificial intelligence adoption and the importance of a comprehensive strategy for emerging technologies governance.
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In boardrooms across the globe, a familiar scene unfolds daily: executives demanding immediate AI implementation while consultants promise miraculous transformations through AI platforms. Yet beneath this fervor lies a more complex reality that demands our careful consideration. The question “How can we use AI for this?” has become ubiquitous in business discourse, but perhaps we are asking the wrong question. Instead, we should be asking, “Do we really need AI for this?” We should seriously examine whether artificial intelligence is the optimal path to achieving our strategic goals, or whether our fascination with this particular technology has blinded us to more comprehensive solutions.
The seductive simplicity of AI-First thinking
The current business environment has created what can only be described as an artificial intelligence orthodoxy, where the mere mention of AI capabilities seems to validate any technology initiative. This phenomenon extends far beyond typical technology adoption cycles, representing instead a fundamental shift in how organizations conceptualize innovation itself. Companies that previously approached technology decisions through careful analysis of business requirements now find themselves retrofitting AI solutions into existing processes, often without clear understanding of whether these interventions address actual pain points or simply satisfy the psychological need to appear technologically advanced.
This AI-centric approach reveals itself in various manifestations throughout modern enterprise operations. Chief executives proudly announce AI initiatives in quarterly earnings calls, marketing departments emphasize AI-powered features in product descriptions, and procurement departments specifically seek vendors offering artificial intelligence capabilities regardless of their relevance to actual business challenges. The result is an ecosystem where AI adoption has become divorced from strategic thinking, creating what industry analysts have begun to recognize as a dangerous form of technology-centric myopia.
Consider the typical enterprise software procurement process in 2025: procurement teams now routinely include "AI capabilities" as mandatory requirements in their vendor evaluations, even when the underlying business processes would benefit more from basic automation, improved data integration, or enhanced user interfaces. This phenomenon suggests that organizations have begun treating artificial intelligence not as one tool among many, but as a prerequisite for any serious technology consideration. The implications of this shift extend beyond inefficient resource allocation, creating organizational structures and expectations that may prove fundamentally misaligned with actual business needs.
The hidden costs of AI tunnel vision
When organizations commit to AI-first strategies without comprehensive technology assessment, they inadvertently create several categories of risk that compound over time. The most immediate concern involves what some researchers have identified as hype dependency, where business strategies become tethered to market expectations rather than sustainable value creation. This dependency manifests most clearly in quarterly planning cycles, where executives feel compelled to announce new AI initiatives regardless of their strategic merit, simply to maintain investor confidence and competitive positioning.
The financial implications of this approach extend beyond obvious implementation costs to encompass what economists call “opportunity costs”, the strategic alternatives foregone when resources are concentrated in a single technological domain. Organizations pursuing aggressive AI adoption often discover that their substantial investments in AI infrastructure, specialized talent acquisition, and algorithm development leave limited resources for complementary technologies that might deliver superior returns. For instance, a retail organization investing heavily in AI-powered customer segmentation algorithms might simultaneously neglect critical improvements to their supply chain visibility, inventory management systems, or customer service platforms that would generate more immediate and measurable business value.
Perhaps more concerning is the emergence of what can be characterized as competitive blindness, where organizations become so focused on artificial intelligence capabilities that they fail to recognize how competitors are leveraging broader technology portfolios to create differentiated value propositions. While one company dedicates engineering resources to developing sophisticated recommendation engines, more strategically agile competitors might be combining IoT sensors, edge computing capabilities, and blockchain verification systems to create entirely new customer experiences that transcend the limitations of AI-only approaches.
The organizational consequences of AI-focused strategies often prove more disruptive than anticipated. Companies that restructure their technology teams around artificial intelligence competencies frequently discover that this specialization creates knowledge silos that inhibit broader digital transformation initiatives. Data scientists optimizing AI models may lack the systems integration expertise needed to ensure their algorithms work effectively within existing business processes, while traditional IT professionals may feel marginalized by the organization's emphasis on AI capabilities, leading to reduced engagement and knowledge transfer challenges.
The customer experience paradox
One of the most troubling aspects of contemporary AI adoption involves the growing disconnect between technological sophistication and customer value delivery. Organizations often become so enamored with the elegance of their AI implementations that they lose sight of whether these systems actually improve customer experiences or solve meaningful problems. This phenomenon appears most prominently in customer service applications, where companies deploy chatbots and automated response systems that demonstrate impressive natural language processing capabilities while simultaneously frustrating customers who require human judgment and empathy.
The paradox becomes more apparent when examining customer feedback data, which increasingly reveals a preference for reliable, straightforward interactions over technically impressive but unpredictable AI-powered experiences. Customers frequently express frustration with recommendation algorithms that seem to understand their technical preferences while completely missing their contextual needs, or with automated customer service systems that can parse complex linguistic expressions but cannot authorize the simple policy exceptions that would resolve their concerns.
This disconnect suggests that many organizations have inverted the traditional relationship between technology and customer value, allowing algorithmic capabilities to drive customer experience design rather than using customer insights to inform technology selection. The result is often a portfolio of AI implementations that optimize for metrics that matter to engineers, accuracy rates, processing speeds, algorithmic sophistication, while potentially degrading the experiential qualities that customers actually value: reliability, transparency, and human agency.
The ecosystem integration challenge
Modern business success increasingly depends on an organization's ability to participate effectively in complex technology ecosystems that span multiple industries, platforms, and stakeholder groups. This reality creates significant challenges for companies that have organized their technology strategies around artificial intelligence capabilities, since many of the most valuable ecosystem opportunities require integration across diverse technological domains that extend far beyond AI applications.
The challenge becomes more complex when examining supply chain integration requirements, where partners increasingly expect real-time data sharing, automated compliance verification, and predictive analytics capabilities that depend on much more than AI alone. Companies may possess world-class demand forecasting algorithms, but find themselves unable to participate in advanced supplier networks because they lack the data integration platforms, security frameworks, and collaborative tools that enable ecosystem-level optimization.
This ecosystem integration challenge reveals itself most clearly in merger and acquisition scenarios, where organizations with AI-centric technology portfolios often struggle to capture anticipated synergies because their specialized capabilities don't align well with the broader technology requirements needed for successful business integration. The result is often a complex and expensive process of retrofitting complementary technologies after the fact, rather than the seamless capability enhancement that strategic acquirers anticipated.
The professional guidance imperative
The complexity of contemporary technology landscapes makes it virtually impossible for even well-informed business leaders to evaluate technology alternatives effectively without specialized expertise that extends beyond vendor presentations and marketing materials. This reality has created what can only be described as a crisis of informed decision-making, where organizations make substantial technology investments based on incomplete information and vendor-driven narratives rather than comprehensive strategic analysis.
The distinction between genuine technology advisory and consultancy services and vendor-driven sales processes has become increasingly important as the stakes of technology decisions continue to escalate. True technology strategists bring several critical capabilities that transcend vendor relationships and product-specific expertise. They possess deep understanding of how different technological domains interact and complement each other, enabling them to design integrated solutions that leverage AI where appropriate while incorporating other enabling technologies where they provide superior value.
Professional technology advisors also bring crucial industry perspective that allows them to contextualize AI capabilities within broader competitive and regulatory frameworks. They understand how similar organizations have successfully implemented various technology combinations, where AI initiatives have generated measurable returns, and equally importantly, where AI investments have failed to deliver anticipated value despite technical success. This institutional knowledge proves invaluable for organizations trying to separate genuine AI opportunities from implementations that merely satisfy psychological or political requirements.
Perhaps most importantly, experienced technology strategists possess the analytical frameworks needed to evaluate technology investments based on business and impact outcomes rather than technical specifications. They can help organizations develop measurement criteria that capture the full value of technology initiatives, including indirect benefits like improved employee engagement, enhanced customer loyalty, and increased strategic flexibility that might not be immediately apparent in traditional ROI calculations.
The strategic alternative of technology portfolio thinking
Rather than pursuing AI-first strategies, leading organizations are increasingly adopting what technology strategists call portfolio approaches to emerging technology adoption. This methodology recognizes that sustainable competitive advantage typically emerges from the strategic combination of multiple enabling technologies rather than from excellence in any single domain, regardless of how sophisticated that domain might be.
Portfolio thinking begins with comprehensive assessment of business objectives and customer requirements, followed by systematic evaluation of how different technology combinations might address these priorities. This approach naturally leads to more nuanced technology strategies that incorporate AI where its capabilities provide clear advantages, while simultaneously leveraging complementary technologies like edge computing, quantum sensing, distributed ledger systems, and advanced materials science where they offer superior solutions to specific challenges.
The portfolio approach also provides natural risk mitigation benefits that AI-centric strategies cannot match. Organizations that distribute their technology investments across multiple domains are inherently less vulnerable to the boom-and-bust cycles that characterize individual technology markets, while simultaneously positioning themselves to capitalize on unexpected convergences and synergies that emerge as different technological domains mature.
More importantly, portfolio strategies enable organizations to maintain strategic flexibility as business requirements evolve and new technological possibilities emerge. Rather than being committed to artificial intelligence solutions that may become obsolete or less relevant over time, organizations can continuously rebalance their technology portfolios to reflect changing market conditions, regulatory requirements, and customer expectations.
Building future-resilient technology strategies
The most successful technology strategies in the current environment share several characteristics that distinguish them from both AI-centric approaches and traditional technology planning methodologies. They begin with deep understanding of business model evolution, recognizing that technology decisions must support not just current operational requirements but also anticipated future business configurations that may be substantially different from present-day realities.
These strategies also incorporate systematic analysis of ecosystem requirements, acknowledging that most valuable business opportunities now depend on effective collaboration across multiple organizations, platforms, and technological domains. This ecosystem perspective naturally leads to technology selections that prioritize interoperability, scalability, and adaptability over pure performance optimization in narrow functional areas.
Future-resilient strategies also emphasize the critical importance of human-technology collaboration, recognizing that sustainable competitive advantage typically emerges from augmenting human capabilities rather than from replacing human judgment with automated systems. This perspective leads to technology implementations that enhance employee effectiveness and customer experiences rather than simply automating existing processes or demonstrating technical sophistication.
Perhaps most importantly, these strategies incorporate robust feedback mechanisms that enable continuous learning and adaptation as both business requirements and technological capabilities evolve. Rather than treating technology decisions as permanent commitments, organizations develop systematic approaches to monitoring, evaluating, and modifying their technology portfolios based on emerging evidence and changing circumstances.
Strategic technology adoption
The question "Do we really need AI?" ultimately serves as a gateway to more fundamental questions about organizational strategy, competitive positioning, and value creation in increasingly complex business environments. The answer rarely involves simple yes-or-no decisions about AI adoption, but rather requires sophisticated analysis of how different technology combinations can support specific business goals within particular competitive and regulatory contexts.
Organizations that approach these decisions most effectively typically engage qualified technology strategists who can provide independent analysis that transcends vendor interests and hype cycles. These professionals bring the analytical frameworks, industry experience, and technical expertise needed to develop technology strategies that genuinely support business success rather than satisfying psychological or political requirements for AI adoption.
The future belongs to organizations that can navigate the complex landscape of emerging technologies with strategic sophistication, leveraging artificial intelligence where appropriate while simultaneously capitalizing on the broader universe of technological possibilities that define contemporary business environments. Success in this landscape requires moving beyond simplistic AI-first thinking toward more nuanced approaches that recognize technology strategy as a critical business capability that demands the same level of professional expertise and strategic attention as finance, marketing, and operations.
Rather than asking whether you need AI, the more productive question becomes: what combination of emerging technologies, implemented with appropriate professional guidance, will best position your organization to create sustainable value in an increasingly complex and competitive business environment? The answer to this question will determine not just your technology roadmap, but your organization's capacity to thrive in the decades ahead.
Even in this field, we are only at the beginning.
(Service Announcement)
This newsletter (which now has over 5,000 subscribers and many more readers, as it’s also published online) is free and entirely independent.
It has never accepted sponsors or advertisements, and is made in my spare time.
If you like it, you can contribute by forwarding it to anyone who might be interested, or promoting it on social media.
Many readers, whom I sincerely thank, have become supporters by making a donation.
Thank you so much for your support!