Will AI Increase "Span of Control" and What Does That Mean for Middle Managers?

Most observers likely believe artificial intelligence will be used to increase span of control, at least to the extent that many supervisory functions can be handled by AI. That "should" create some opportunities to increase "span of control," which should also allow some moves to reduce middle management jobs.

“Span of control” refers to the maximum number of “direct report” humans that can be supervised by a single manager. That number tends to hover between five and eight, but can be wider or narrower depending on the industry and tasks to be supervised. 

But that assumption might not be universally applicable. If and where AI means job functions get more complex, or require even more exercise of "human" skills such as empathy, ethical judgment or complex coordination, then AI might actually decrease spans.

Still, that is likely the exception to the rule. Most observers likely believe that AI will make possible some increases in spans of control.

AI systems might  monitor performance metrics continuously, provide real-time feedback, coordinate schedules and resources and handle routine administrative tasks that previously required human managers. It remains to be seen. 

AI should be able to analyze patterns across larger groups of employees than any human manager could effectively track. So to that extent, middle manager value could decrease somewhat. 

Still, middle managers likely cannot be completely replaced. They translate strategic vision into operational tasks, provide coaching and development, facilitate cross-functional coordination, handle complex interpersonal dynamics and act as information conduits between levels. They also make nuanced decisions that require contextual understanding and emotional intelligence.

The issue is how AI might ultimately be able to become a substitute for some of those functions. 

Industry	Typical Span of Control (Manager:Direct Reports)	Characteristics & Rationale
Call Centers	Wide (1:10 - 1:20+, potentially even 1:100+)	High volume of standardized, repetitive tasks. Employees often follow scripts and procedures. Technology (CRM, call monitoring) assists with oversight, allowing managers to oversee many agents.
Manufacturing, Production	Medium to Wide (1:8 - 1:15+)	Tasks can be repetitive and process-driven, especially on assembly lines. Clear procedures and visual management tools facilitate supervision. Safety protocols often require close monitoring, but experienced workers may need less direct oversight.
Retail (Store Level)	Medium to Wide (1:5 - 1:15+)	Store managers often oversee a team of sales associates, cashiers, and stockers. Tasks are somewhat standardized, but customer interaction and merchandising can add complexity. Varies by store size and type.
Technology, Software Development	Narrow to Medium (1:4 - 1:7)	Work is often complex, requiring high levels of collaboration, problem-solving, and creativity. Teams are typically self-managing to a degree, but managers provide technical guidance, mentorship, and project oversight for highly specialized tasks.
Healthcare (Clinical/Nursing)	Narrow to Medium (1:3 - 1:10)	High complexity, critical decisions, and direct patient care. Requires close supervision to ensure patient safety, quality of care, and adherence to protocols. Nurse managers often have fewer direct reports due to the intensity and criticality of the work.
Executive Management, Leadership	Narrow (1:3 - 1:7)	Deals with strategic decision-making, complex problem-solving, and high-level collaboration. Requires significant individualized attention, coaching, and strategic alignment among direct reports (e.g., department heads, VPs).
Consulting, Professional Services	Narrow to Medium (1:3 - 1:8)	Projects are often unique and complex, requiring specialized expertise and close client interaction. Managers (project leaders) need to provide detailed guidance, quality control, and client relationship management for their team members.
Education (School Administration)	Narrow to Medium (1:5 - 1:10)	Principals oversee teachers, who in turn manage students. The complexity of managing various curricula, student needs, and administrative tasks often leads to a narrower span for administrators compared to highly standardized environments.
Financial Services ( Banking Branch)	Medium (1:6 - 1:10)	Branch managers oversee tellers, customer service representatives, and loan officers. While some tasks are standardized, customer interactions and regulatory compliance require a moderate level of oversight.

And though AI should tend to increase spans of control in most industries, AI could result in a decrease of spans in education and healthcare, two fields where productivity increases are hard to produce. AI might in many cases increase the span of control by reducing the managerial burden. 

But in some contexts it could decrease spans if roles become more complex or require closer oversight of human-AI collaboration. That might also tend to happen when empathy, ethical judgment or complex coordination is required, and that would tend to be true whether we are considering AI or simply span of control issues in general. 

Industry	Current Avg. Span of Control	AI Impact Direction	Projected Change	Reasoning
Manufacturing	5–10	Increase	10–15	AI enables predictive maintenance, process monitoring, reducing oversight needs.
Retail	8–12	Increase	12–18	AI-driven scheduling, inventory, and customer insights reduce manual supervision.
Healthcare	4–6	Mixed	3–7	Clinical AI reduces admin load, but patient safety and human empathy limit scalability.
Finance	6–10	Increase	10–15	AI automates reporting, fraud detection, and customer service functions.
Education	10–15 (academic), 5–8 (admin)	Mixed	8–20 (academic), 4–10 (admin)	AI grading and tutoring help faculty scale, but student engagement needs persist.
IT & Software	6–12	Increase	10–18	AI tools for code review, project tracking, and testing reduce managerial bottlenecks.
Construction	4–8	Slight Increase	6–10	AI in site planning and safety monitoring helps, but physical oversight still critical.
Legal Services	3–6	Increase	5–10	AI in discovery, contract analysis boosts support staff efficiency.
Public Sector, Government	5–9	Slow Increase	6–10	Bureaucracy limits rapid gains, but AI may help in records and citizen services.
Logistics, Transport	6–12	Increase	10–18	AI route optimization and fleet tracking allow for greater managerial reach.

Content Does Not Monetize Itself: Others in the Value Chain are Necessary

Businesses virtually never take positions that undermine or threaten their own interests. So how does Cloudflare benefit from its new emphasis on blocking language model scraping of web content?

By blocking unauthorized AI crawlers, Cloudflare helps its customers safeguard their original content from being used to train language models without consent or compensation. This is especially valuable for the publishers, media companies, and content-driven businesses that buy Cloudflare services. 

There also are new revenue opportunities. Cloudflare’s introduction of an AI scraping marketplace allows website owners to set terms and charge AI companies for access to their content. Cloudflare potentially earns fees or commissions, turning content protection into a business opportunity.

Cloudflare’s policies also provide more negotiating leverage for its content customers when negotiating licensing deals.

AI scraping can resemble denial-of-service attacks, overloading servers and degrading website performance. Cloudflare’s controls help prevent such problems.

The point is that the new policies are beneficial for Cloudflare, increasing its perceived value for its core clients; providing differentiation from competing alternatives and creating possible new revenue opportunities as well.

Lawsuits over copyright infringement and also technology solutions such as undertaken by Cloudflare to prevent web scraping are some content owner and content-supporting supplier responses to AI language model impact on their revenue models.

Still, content business models are going to have to change, as monetizable web traffic already is declining. Blocking of AI crawlers might slow down indexing activities, and raise the cost of doing so.

But content creators have no “right” to make money in the value chain. It always takes partners to monetize content or art. Language models might arguably pose an issue, just as controls on content scraping might be viewed as a help.

But content monetization always is dependent on others in the value chain.

Content Creators	Historical Value Chain Partners	Role in Monetization	Modern Value Chain Partners	Role in Monetization
Painters	Patrons (e.g., nobility, churches), art dealers, auction houses	Commissioned works, purchased paintings, or sold them to collectors; provided materials and exhibition spaces.	Galleries, online marketplaces (e.g., Saatchi Art), NFT platforms (e.g., OpenSea)	Display and sell artworks, authenticate pieces, or enable digital sales (e.g., NFTs); provide global exposure via online platforms.
Writers	Publishers, printers, booksellers	Printed and distributed books, serialized novels in magazines, paid advances or royalties.	Self-publishing platforms (e.g., Amazon KDP), literary agents, crowdfunding (e.g., Kickstarter)	Enable direct publishing and sales, connect writers to publishers, or fund projects via fan support.
Musicians	Record labels, concert promoters, sheet music publishers	Produced and distributed recordings, organized live performances, sold sheet music for home use.	Streaming platforms (e.g., Spotify, Apple Music), social media, Bandcamp	Distribute music globally, generate ad/subscription revenue, enable direct sales or fan-funded projects.
Sculptors	Patrons, city governments, workshops	Commissioned public or private sculptures, provided materials and studio space.	Art collectives, online galleries, 3D printing services	Facilitate sales through exhibitions, provide digital tools for creation, or connect with buyers online.
Actors	Theater companies, producers, patrons	Staged performances, paid actors for roles, attracted paying audiences.	Film/TV studios, streaming platforms (e.g., Netflix), talent agencies	Produce and distribute content, pay for performances, or monetize via subscriptions/ads; connect actors to opportunities.

Why 99.999 Percent Availability is Not Possible Anymore

Our user experience of applications, devices and networks is far from the “five nines” standards (99.999 percent availability) telcos used to tout. 

As a practical matter, today’s heterogenous, edge-powered, internet transport fabric, IP-based application environment absolutely means user experience cannot approach 99.999-percent availability for any applications. 

That might not apply to core systems in banking, financial trading or some security-critical use cases, but only to the core systems, not the end user access of those systems. 

The problem is that no matter what any single participant in the value chain might claim for its own availability, and even if that availability is between 99 percent and 99.99 percent, the entire end-to-end value chain depends on the sum total of availability across the whole value chain, and that math is challenging. 

Consider an example where contributor availabilities are:

• Device: 99%

• Home broadband access: 99.5%

• Internet backbone: 99.99%

• App server: 99.9%

• Local power: 99.5%

The end-to-end availability requires multiplying all those discrete availabilities. So the formula is 

0.99 × 0.995 × 0.9999 × 0.999 × 0.995 ≈ 97.4 percent. That means 229 hours of downtime per year, not the 5.26 minutes per year allowed by "five nines” standard.

The only reason end users seem unaware of the change is that much of the downtime happens when they are not actively using their connections (devices not present; devices in “do not disturb” mode; user is sleeping; apps not in immediate use). 

Value Chain Component	Typical Availability (%)	Major Downtime Factors
User Devices – Mobile	95%–99%	Battery loss, OS/software issues, dropped connections
User Devices – Fixed	96%–99.5%	Power outages, device crashes, local network (Wi-Fi) issues
Access Network – Mobile	97%–99.9%	Tower outages, congestion, interference, maintenance
Access Network – Fixed	98%–99.9%	Fiber/cable cuts, power issues, last-mile failures
Global Internet Backbone	99.99%+	Rare fiber cuts, DDoS attacks, routing errors
Application Servers (Cloud)	99.5%–99.99%	Cloud region outages, software bugs, maintenance, cyberattacks
Local Power Supply	99.0%–99.9% (urban)	Grid instability, storms, infrastructure failures
End-to-End Availability	Often < 95%–98%	Cumulative failures across components