AI ROI: Efficiency First, Effectiveness Longer Term

The distinction between efficiency and effectiveness is a foundational dichotomy in organizational life, and adoption of artificial intelligence will exhibit both tendencies, as most prior computing technologies have tended to do. 

As with earlier waves of information technology adoption, organizations initially justified investments in computers, enterprise software, and the internet through labor savings and operational efficiencies.

Over time, however, the greatest economic gains came from effectiveness:

• creating entirely new products and services (such as online banking, e-commerce, cloud software, and digital media)

• entering new geographic markets

• serving customers in more personalized ways

• accelerating research and product development

• enabling new business models that were previously impractical.

AI appears to be following the same trajectory. 

While efficiency is often the primary driver for early-stage AI adoption: 

• automating manual tasks

• accelerating workflows

• reducing overhead.

But most might agree that greater long-term outcomes are possible by focusing on AI effectiveness, or "doing the right things” (perhaps different things)  with AI. 

Dimension	Operating More Efficiently	Operating More Effectively
Goal	Reduce costs	Increase value created
Focus	Existing work	New opportunities
Typical metric	Cost, hours, headcount, error rate	Revenue, market share, innovation, customer satisfaction
Economic effect	Improve margins	Increase growth
Example	Automate invoice processing	Launch products previously impossible to build

Efficiency normally gets the early attention, as AI excels at removing human friction from repetitive tasks such as data entry, scheduling, routine coding, or basic customer service. 

Business Function	AI Application	Efficiency Gain	Example
Customer service	Chatbots and AI agents	Lower support costs, faster response	Klarna automates a large share of customer inquiries
Software development	Code generation	Faster coding and testing	GitHub Copilot, Microsoft
Manufacturing	Predictive maintenance	Less downtime	Siemens, GE
Finance	Invoice processing	Reduced manual accounting	SAP, Oracle AI
HR	Resume screening	Faster hiring workflows	Workday AI
Legal	Contract review	Reduced attorney review time	Harvey AI
Marketing	Content generation	Lower content production costs	Adobe Firefly
Supply chain	Demand forecasting	Lower inventory costs	PepsiCo, Walmart
Healthcare	Medical documentation	Reduced physician administrative burden	Microsoft Dragon Copilot
IT operations	AI help desks	Faster ticket resolution	ServiceNow

Executing established processes faster, cheaper and with fewer errors improves the throughput of a business. It can help lower operating costs. But does not necessarily change the nature of the business.

Effectiveness is about strategy and outcome; the pursuit of higher-value results. 

As organizations mature in their AI journey, the focus often shifts from "doing more with less" (efficiency) to "doing more of what matters" (effectiveness). 

Category	Definition	AI Implementation Example	Key Benefit
Efficiency (Doing things right)	Focuses on optimizing processes, speed, cost-reduction, and resource utilization.	Automating invoice processing; using predictive maintenance to reduce machine downtime.	Operational excellence, cost savings, time-to-market speed.
Effectiveness Doing the right things)	Focuses on achieving high-value outcomes, strategic goals, and solving the correct problems.	Using generative AI for creative brainstorming; AI-driven drug discovery; personalized patient diagnostics.	Competitive advantage, innovation, improved quality of output, solving complex challenges.

Long-term AI gains are likely to come from greater organizational effectiveness, as that has been the pattern for earlier computing innovations as well.

Business Function	AI Application	Effectiveness Gain	Business Outcome
Sales	Personalized prospecting	More customers reached	Higher revenue
Marketing	Hyper-personalization	Better conversion	Larger customer acquisition
Product development	AI-assisted design	More products launched	Faster innovation
R&D	Scientific discovery	More candidate molecules/materials	New products
Customer success	Predictive recommendations	Higher retention	Greater customer lifetime value
Consulting	Faster research	More client engagements	Revenue growth
Software	AI-native applications	Entirely new product categories	New businesses
Manufacturing	AI-assisted engineering	Better product quality	Competitive advantage
Education	Personalized tutoring	Better learning outcomes	New educational services
Financial services	AI investment analysis	Better decision quality	Higher returns

Analysts at McKinsey tend to find such returns are possible when enterprises implement AI, though both efficiency gains as well as effectiveness advantages can be seen. 

Finding	Interpretation	Source
Companies report both cost savings and revenue increases from AI	AI affects both efficiency and effectiveness	McKinsey Global AI Survey (McKinsey & Company)
Revenue gains are most common in marketing, sales, and product development	AI enables growth, not just automation	McKinsey (McKinsey & Company)
Enterprise-wide AI adoption correlates with substantially higher profit margins and returns on invested capital	Value comes from integrating AI into business strategy	McKinsey Operations Survey 2026 (McKinsey & Company)
AI-supported sales tools increased incoming orders by roughly 40% in one automotive transformation	AI improves commercial effectiveness	McKinsey Operations Survey (McKinsey & Company)
Software engineering productivity improved by up to 44% while enabling faster product delivery	AI boosts both efficiency and innovation	McKinsey Operations Survey (McKinsey & Company)

Sora's Lesson: Today's Headline, Tomorrow's Footnote

When OpenAI first previewed Sora, its image creation tool, in February 2024, it briefly became the symbol of an existential threat to professional filmmaking. 

Sora suggested that anyone could create cinematic-quality footage from a text prompt. 

Just six months later, OpenAI killed Sora to focus on enterprise AI. 

But Sora was just one tool in a rapidly expanding field of AI video models. The perceived threat did not disappear, but diffused.

Sora arguably illustrates one element of AI:  the competitive frontier moves faster than the products.

Phase	Perception	Reality
February 2024	"Sora changes filmmaking overnight."	Research preview with impressive demonstrations but limited public access. (OpenAI)
Late 2024	"Studios may replace production with AI."	Public release revealed practical limitations involving shot consistency, editing and controllability. (OpenAI)
2025-2026	"One video model dominates."	Numerous competitors (Runway, Google Veo, Kling, Seedance, MiniMax, Pika and others) rapidly closed capability gaps. The conversation shifted from one model to an entire ecosystem. (arXiv)

Sora’s demise also showed that professional AI bottlenecks have shifted. The scarce resource stopped being video generation and became:

• creative direction

• prompting

• editing

• narrative structure

• intellectual property

• production workflow integration. 

That is a different competitive landscape than many feared in early 2024.

Instead of replacing filmmaking, AI increasingly entered as another production tool for:

• previsualization

• concept art

• storyboard generation

• pitch videos

• background plates

• visual effects assistance

• advertising

• social media production

• low-budget content creation. 

The broader lesson might be that sustainable competitive advantage in AI is not yet achievable, in part because the next frontier moves fast. Today’s headline news is tomorrow’s footnote.

Is Mark Cuban Right About Employee Stock Ownership?

Mark Cuban is a creative guy. To help reduce wealth inequality, he advocates that private firms give every employee stock, for example. 

To be sure, it is not a panacea. Employee ownership in retail, hospitality, and gig work would be difficult, for example. 

Still, the evidence suggests employee equity ownership plans have had measurable but generally modest success at reducing wealth inequality, particularly within participating firms and among middle-income workers. 

Research demonstrates that broad-based equity distribution serves as a powerful driver of wealth accumulation for low- and middle-income workers:

Capital Asset Accumulation: Studies from the National Center for Employee Ownership suggests that workers participating in employee stock ownership plans accumulate substantially higher median net worth (often 90 percent or higher) compared to non-employee-owners in similar industries.

IZA World of Labor (Kruse, 2016) suggests that employee ownership disproportionately benefits female and minority workers as well.

They have not, by themselves, substantially reduced wealth inequality across entire societies, because participation is often limited to certain employers, ownership stakes are typically modest, and broader drivers of wealth concentration (housing, inheritances, business ownership, and financial assets) remain dominant.

Still, equity participation likely would help reduce wealth inequality.  

Observers might argue that relatively low wealth inequality in Nordic nations, compared with many developed nations, is not primarily due to employee ownership, however. 

The outcomes are shaped by:

• strong labor unions

• progressive taxation

• universal public services

• pension systems

• high employment

• capital taxation (historically)

Employee ownership exists but is not the principal equalizing mechanism.

And there are practical issues beyond the limited number of firms that might reasonably be expected to support such policies:

• Employees may have too much wealth tied to one company. If the firm fails, workers can lose both jobs and retirement savings.

• Stock compensation programs need to be broad-based.

• Even generous employee ownership usually represents a modest fraction of total national wealth compared with:

• inherited wealth

• real estate

• privately owned businesses

• financial portfolios

So measures to broaden ownership potential in those areas also matters greatly. 

In practice, countries with relatively low inequality often combine multiple policies:

• broad-based employee ownership

• progressive taxation

• education equity

• pensions

• social insurance. 

Evidence suggests employee ownership is a useful complement to these policies rather than a standalone solution. 

But Cuban is on to something. Broad employee participation in equity ownership can help reduce some amount of wealth inequality.

AI Circular Investment, Like Customer Concentration, is Hard to Avoid

Nvidia's 9.3-percent ownership of Nebius is an example of the AI "circular strategy,” where participants in the AI value chain invest in each other while committing to large-scale purchases of one another's products and services.

Other examples include:

• Microsoft-OpenAI: Microsoft invested over $13 billion (major tranche in 2023). OpenAI became a major Azure customer, with commitments like $250 billion in cloud services.

• Nvidia’s role: Nvidia has invested heavily in OpenAI (up to $100 billion cited), xAI, Mistral, and others, with recipients committing to buy its GPUs. Nvidia also invested in neocloud providers like CoreWeave.

• Amazon/Anthropic/Google: Investments in Anthropic paired with commitments to use AWS, Google Cloud/chips.

• Deals involving Oracle, AMD, BlackRock, etc., including data center acquisitions and massive purchase commitments (Oracle buying Nvidia chips for OpenAI facilities). These form a "tangled web" or " virtuous circle" of financing and buying, depending on one’s perspective. 

The practice is not as unprecedented as it might seem.

Similar strategies can be seen in  prior computing and tech infrastructure investment periods. 

One example is vendor financing. where suppliers extend credit, loans, or equity-like support to customers to enable purchases.

And though the precedent will worry some, who see potential for excess investment, such arrangements were common during the optical fiber investment boom around the turn of the century. 

Equipment vendors helped were active investment partners: 

• Lucent, Nortel, Cisco, Alcatel provided vendor financing (loans, credit, sometimes over 100 percent of purchase value) to buyers building fiber networks

• "Capacity swaps,” where service providers purchased capacity from each other and each partner booked the revenue, also occurred. 

But the computing industry has often seen such deals:

• Enterprise computing suppliers (IBM, later Dell, HP, Cisco) have long used financing, leasing, and channel programs to help customers acquire servers, networking gear, and software

• In the personal computing era, vendor financing and trade-ins helped drive volume

• In semiconductors and enterprise IT, suppliers often finance customers to secure market share, especially during technology transitions.

It’s one sort of risk, to be sure. 

But customer concentration sometimes cannot be avoided. 

In fact, capital-intensive industries tend to produce a rule of three structure, where just a few market leaders exist. In the AI market, such concentration might be unavoidable.

The "Big Three" cloud providers (AWS, Azure, Google) represent more than 60 percent of the global cloud infrastructure market. 

For a major supplier such as Nvidia, that translates to a concentrated customer base. In recent quarters, just four customers (hyperscalers) accounted for 61 percent of revenue. 

The biggest customers have represented 50 percent or more of business in some periods. Just six customers drove 85 percent of revenue in one reported quarter.

Suppliers and investors ideally want broad bases to mitigate risk—if one customer cuts spending, others can offset it. Here, the top buyers are interdependent (via partnerships, investments, and shared ecosystems) and move in similar cycles driven by AI progress and monetization.

Customer concentration is always considered a risk. In the case of AI, it is almost unavoidable. Nor should that be surprising in any capital-intensive business in an early stage of development. 

But even long term, a market led by just a few firms  is almost certain to develop.