AI is a challenge of leadership instead of innovation

 

AI doesn’t fail primarily due to lack of ideas or technology. It fails because leaders don’t make the hard decisions AI forces into the open.

Innovation problems are about can we build it?
AI problems are about should we, where, and under what constraints?

That’s a leadership problem.

1. AI collapses the gap between decision and consequence

Traditional innovation lets leaders delegate:

• Engineers build
• Product experiments
• Leaders review outcomes later

AI doesn’t allow that comfort.

• AI executes decisions at scale
• Errors propagate instantly
• “Small” choices become policy

Leadership challenge

• You must decide in advance what decisions are allowed to scale.
• You own failures you didn’t personally approve line-by-line.

 

2. AI exposes organizational contradictions

AI systems force answers to questions leaders often avoid:

• Do we value speed or safety?
• Growth or trust?
• Consistency or discretion?
• Efficiency or employment?

Humans can navigate contradictions informally.
AI cannot.

Result

• Leadership indecision becomes model ambiguity.
• Political compromises turn into technical debt.

 

3. Innovation tolerates ambiguity. AI amplifies it.

Innovation thrives on exploration.
AI systems:

• Act even when uncertain
• Sound confident when wrong
• Hide edge cases until damage occurs

Leadership failure mode

• Treating AI like a prototype instead of an operational actor.
• Confusing model accuracy with decision readiness.

 

4. AI shifts accountability upward, not downward

In classic innovation:

• Failure belongs to the team.
• Leaders sponsor and shield.

In AI:

• Failures trigger legal, ethical, and reputational consequences.
• “The model did it” is not a defense.

Hard truth

You cannot delegate moral agency to software.

That accountability sits with leadership whether acknowledged or not.

 

5. The real bottleneck is not data or models; it’s permission

Most AI programs stall because leaders won’t decide:

• Which workflows can be automated
• Which roles change
• Which risks are acceptable
• When humans must override the system

Teams can build models faster than leaders can grant authority.

 

6. AI forces explicit value tradeoffs

 

Innovation asks: What’s possible?
AI asks: What is acceptable?

Examples:

• Fairness vs profitability
• Transparency vs performance
• Personalization vs privacy

These are normative decisions, not technical ones.

Only leaders can make them and be accountable.

 

7. AI success looks boring, not innovative

Well-led AI:

• Quietly prevents bad decisions
• Stops scaling the wrong things
• Reduces variance, not creativity

Poorly led AI:

• Demos well
• Fails publicly
• Surprises leadership

Innovation celebrates novelty.
Leadership values reliability.

AI rewards the second.

 

The core insight

AI is a mirror. It reflects leadership clarity or the lack of it - at machine speed.

If values, ownership, escalation paths, and risk tolerance are unclear, AI will surface that confusion faster than any other technology.

That’s why organizations with strong leadership but mediocre tech outperform those with brilliant models and weak governance.

 

A simple litmus test for leaders

If a leader cannot clearly answer:

1. What decisions this AI is allowed to make
2. What data it is allowed to use
3. What failure looks like
4. Who shuts it off
5. Who apologizes publicly

They are not leading AI.
They are experimenting with it.

Is the era of the 'Best-of-Breed' giant over? Why the hyperscale oligarchy is coming for Salesforce, SAP, and the rest of the independent enterprises?

 

The answer is messier than either or. We’re heading toward a stratified market where both thrive, but in fundamentally different ways

Short answer: hyperscalers will dominate infrastructure and primitives, but they will not own the entire enterprise stack. There is still room for independent players, but only if they operate in very specific lanes and avoid pretending the old model still works.

Here’s an unsentimental breakdown:

1. What hyperscalers actually win (and why)

Hyperscalers (AWS, Azure, GCP) have structural advantages that compound:

A. Capital + utilization flywheel

• Massive capex amortized across millions of customers
• Better utilization → lower marginal cost → more investment
• No independent vendor can match this curve

Result:
Compute, storage, networking, and AI infra become utilities.

B. Control of primitives

Hyperscalers own:

• Cloud Infrastructure
• Compute
• Storage
• Observability & Monitoring
• Identity, Access, and Security Primitives
• Data Warehousing & Core Analytics
• Developer Platform & Runtime
• AI runtimes

These are horizontal, non-negotiable layers.

Once primitives are owned, everything above is pressured.

D. Developer Platforms

Developers follow gravity and hyperscalers are a new shiny thing on the horizon

• IDE and allied tools
• Container orchestration
• API Gateway
• Low-code development will entice non-programmers into programming
• Workflow orchestration
• Ad-hoc Platform integration

E. Distribution power

• One-click procurement
• Integrated security and compliance
• Enterprise trust at the CIO level

Result:
Anything that looks like “undifferentiated plumbing” gets absorbed.

2. Where hyperscalers fail (systemically)

Hyperscalers struggle with deep, opinionated domain-specific software – producing polished products.

Not accidentally but structurally.

Why:

• They optimize for breadth, not depth
• Products must serve incompatible customer needs
• Internal incentives reward infra leverage, not domain mastery
• Regulatory risk pushes them toward neutrality

This creates a ceiling on:

• ERP nuance
• Industry-specific workflows
• Mission-critical business logic
• High-stakes compliance interpretation

Hyperscalers ship platforms. Enterprises run businesses.

Example: AWS has likely launched over 10 database services, but enterprises still pay Snowflake billions because Snowflake understood data warehouse users’ workflows in ways that AWS didn’t bother to. The hyperscalers ship features; independent vendors ship solutions.

3. The survivable lanes for independent giants (Oracle, SAP, Salesforce, etc.)

Independent enterprise giants survive only where all three conditions hold:

1. Domain lock-in is real, not contractual

• Understanding of local tax laws and continuous updation
• Own the workflows that run actual businesses - payroll, financial close, procurement, HR processes, Sales cycle, etc.
• Industry regulations (healthcare, utilities, banking, insurance, defense, etc.)

If the cost of being wrong is existential, not inconvenient, hyperscalers back off.

2. The product encodes institutional knowledge

Software that embodies:

• Decades of edge cases
• Legal interpretations
• Audit logic
• Process memory
• Internal politics embodied as organizational structure

This is representation learning, not CRUD.

Enterprise software is deeply embedded in work culture and politics.

3. Switching costs are cognitive, not technical and/or financial

APIs are easy to rewrite.
Mental models are not.

If users think in your system, you’re defensible.

4. Enterprise Software vendors: obsolete or underestimated?

Enterprise Software vendors are not dead but narrowing.

Where Enterprise Software vendors still win

• Regulated enterprise workloads
• High-scale transactional systems
• Enterprises that value predictability over innovation

Enterprise Software vendors’ strength is not agility; their invariant-ability.

Where Enterprise Software Vendors lose

• Developer mindshare
• AI-native workflows
• Anything that smells like commodity infra

5. The new equilibrium (2025–2035)

The enterprise stack is splitting into three layers:

Layer 1: Utilities (hyperscalers)

• Compute
• Storage
• Networking
• AI runtimes
• Security primitives

Winner-take-most.

Layer 2: Platforms (contested)

• Data platforms
• Integration
• Analytics
• Workflow engines

Hyperscalers pressure here but don’t fully own it.

Layer 3: Systems of Record & Judgment (independent giants)

• ERP
• Financials
• HR
• Industry-specific cores

This layer cannot move fast without breaking reality.

That’s Enterprise Software vendors’ natural habitat.

6. The real threat is not hyperscalers - it’s collapse via false grokking

Independent giants don’t die because hyperscalers kill them.

They die because they:

• Mistake contracts for moats
• Optimize sales over learning
• Ship abstractions divorced from real workflows
• Stop encoding new reality

Hyperscalers apply pressure.
False grokking pulls the trigger.

7. The absorption heuristic (use this yourself)

Ask four questions:

1. Is correctness universal or contextual?
   Universal → hyperscaler
   Contextual → independent
2. Does value increase with scale or judgment?
   Scale → hyperscaler
   Judgment → independent
3. Is the buyer optimizing cost or risk?
   Cost → hyperscaler
   Risk → independent
4. Can failure be rolled back safely?
   Yes → hyperscaler
   No → independent

If you answer “hyperscaler” to 3+ of these, absorption is inevitable.

8. Final verdict

The future of enterprise software is not owned by hyperscalers, but it is bound by them.
The independent giants that survive will be those with genuine moats the hyperscalers can’t easily replicate: deep vertical expertise (Veeva in pharma), workflow lock-in (ServiceNow for ITSM), or network effects (Salesforce’s AppExchange ecosystem). They’ll increasingly run on hyperscaler infrastructure while providing the opinionated layer on top.

What’s genuinely threatened is the middle - companies selling undifferentiated infrastructure or horizontal tools without strong moats. Why buy a standalone monitoring tool when each hyperscaler offers something 80% as good that’s deeply integrated?

The future probably looks like: hyperscalers own the infrastructure and broad horizontal services, independent giants own the high-value vertical workflows with real lock-in, and a healthy ecosystem of specialized vendors serves niches too small for hyperscalers to care about.

Stop Generating Insights, Start Engineering Action: 8 Ways to Make AI Useful for Executives

 

Beyond the Dashboard: Engineering AI for Actionable Leadership

To ensure AI recommendations are truly actionable for decision-makers rather than merely insightful, organizations must force a fundamental shift from "analysis" to "action architecture". Insights alone merely highlight patterns or trends, but actionable recommendations translate those patterns into concrete, feasible steps aligned with specific goals and resources.

1. Start with the Decision, Not the Data

Actionability is engineered by reverse-engineering from the specific decision a leader must make. Before building any model, it is vital to clarify what decision is being made, who makes it, and what constraints, such as budget, timeline, and risk tolerance, matter. If the AI cannot trace its output to a real-world choice, such as a pricing change or a portfolio shift, it remains "noise" or "decorative PowerPoint" material.

2. Convert Insights into Controlled Choices

Decision-makers generally do not want a single "answer" from a black box; they want controlled choices that mirror how leadership already operates. A strong pattern for AI output is to provide options:

• Option A: Fast, low-risk, moderate upside.

• Option B: Slower, higher risk, high upside.

• Option C: A defensive move that preserves resources.

Each recommendation should include confidence and uncertainty bands that are framed as risk postures (e.g., "worst case we lose $20k") rather than technical statistics.

3. Contextualize and Quantify Trade-offs

Actionability dies when outputs are too abstract. AI must have access to organizational constraints, such as resource limits, timing windows, and local rules, to avoid producing vague advice. Furthermore, every recommendation must quantify the implementation path, specify what must be given up (opportunity costs), and suggest whether the move is reversible or testable via a pilot.

4. The "24-Hour Rule" for Implementation

Every AI output should include a "next-step" trigger to prevent analysis paralysis. This includes:

• Defining exactly what needs to happen in the next 24 hours.

• Identifying who needs to be involved and what approvals are required.

• Naming the next irreversible action.

• Specifying the "what, who, and when" of the recommendation.

5. Speak the Language of "Decision Economics"

Executives act on money, risk, timing, and competitive position, not on technical metrics like R², embeddings, or anomaly clusters. AI must translate its findings into business levers, such as revenue growth, cost reduction, or margin improvement. Using a semantic layer can help translate complex data into this "business-friendly" format.

6. Deliver Insights in the "Flow of Work"

Actionable AI should appear where the decision is actually made, rather than in a detached tool or a static PDF. This means integrating recommendations directly into:

• CRMs for sales teams.

• Slack or Teams for operations.

• ERP systems for finance.

• Operational triggers (e.g., "If sentiment shifts by 5% → notify manager → recommend mitigation").

7. Build Feedback Loops and Accountability

To build trust, the system must allow for human overrides and capture the reasons why a recommendation was ignored. Organizations should measure more than just model accuracy; they must track behavioral adoption, including:

• Recommendation adoption rate.

• Time-to-action.

• Impact tracking (e.g., "We made 32 decisions with AI guidance and saved $1.7M").

• Assigning an accountable owner and a review cadence for every recommendation to ensure it does not "die in a slide deck".

Summary: The Actionability Test

The ultimate test for any AI recommendation is whether a leader can read it and immediately know which meeting to schedule, who to involve, and exactly what question they are answering. If the recommendation is not crisp and executable, it remains "interesting trivia" rather than a tool for real-world impact