The SaaS industry is entering a period of significant change.

For years, the dominant software model was relatively predictable: companies bought cloud applications, employees logged in, vendors charged recurring subscriptions, and businesses expanded by adding users, features, and customers.

That model is not disappearing overnight. But the economics, interfaces, product strategies, and competitive dynamics around SaaS are changing rapidly.

Artificial intelligence is moving from an optional feature toward a core product capability. AI agents are beginning to perform tasks rather than simply assist users. Pricing models are being reconsidered as software consumption becomes less tied to the number of human seats. Security and governance are becoming more important as autonomous systems gain access to business data and workflows.

Deloitte's 2026 software industry outlook describes this as a period of intensifying competition, AI-first product development, and growing agentic AI adoption.

For business leaders, the important question is not simply whether AI will affect SaaS.

It is how these changes will affect product strategy, customer expectations, pricing, operations, security, talent, and competitive advantage.

This guide explores the most important SaaS trends shaping the next phase of the industry and explains what leaders should consider when planning for the years ahead.

Why the Future of SaaS Is Changing

The original SaaS revolution moved software from locally installed applications to cloud-based subscriptions.

That transformation changed:

• How software was delivered
• How customers paid
• How products were updated
• How companies acquired users
• How businesses measured recurring revenue
• How software companies scaled internationally

The next transformation is broader.

AI can change not only how software is built but also how software is used.

Instead of asking:

“Which buttons should I click?”

users increasingly may ask:

“Complete this task for me.”

That shift sounds subtle, but it can fundamentally change the role of the application interface.

Deloitte expects SaaS applications to become more intelligent, personalized, adaptive, and autonomous as AI agents mature.

Business leaders therefore need to think beyond adding AI features.

They need to consider how the underlying SaaS business model may evolve.

1. AI-Native SaaS Will Become the New Competitive Baseline

Adding an AI assistant to an existing product is increasingly different from building an AI-native product.

An AI-native application is designed around what intelligent systems can accomplish from the beginning.

That can change:

• Product architecture
• User experience
• Workflow design
• Data requirements
• Automation
• Support
• Pricing
• Product development

Instead of simply adding a chatbot to a dashboard, an AI-native SaaS product might automatically analyze information, recommend actions, execute workflows, and continuously improve the process.

Deloitte's 2026 software outlook notes that software companies are increasingly moving from adding isolated AI functions toward AI-first engineering and product design.

What business leaders should do

Ask:

• Where can AI eliminate unnecessary manual work?
• Which workflows can become autonomous?
• What proprietary data improves the AI experience?
• Which product interactions can become conversational?
• Which customer outcomes can be automated?

The objective should be measurable customer value, not simply having an AI label.

2. Agentic AI Will Change How SaaS Products Work

Generative AI primarily changed how people interact with software.

Agentic AI has the potential to change who performs the work.

An AI agent can potentially:

1. Receive a goal
2. Access authorized information
3. Decide what steps are required
4. Execute actions across systems
5. Evaluate the result
6. Continue or escalate when necessary

This creates a fundamentally different software experience.

Instead of manually creating a report, a user might ask an agent to:

“Analyze this month's sales performance, identify unusual changes, summarize the causes, and prepare a management report.”

The software becomes an execution layer rather than simply a database and interface.

Gartner estimates that agentic AI could put a substantial portion of enterprise application software spending at risk through what it calls “agentic arbitrage,” as agents perform work across multiple applications.

Strategic implication

SaaS companies need to determine whether their product is:

• A destination users operate manually
• A system agents operate on behalf of users
• An orchestration layer connecting multiple systems
• A combination of all three

That question may become central to product strategy.

3. Seat-Based Pricing Will Face More Pressure

Traditional SaaS pricing often depends on seats.

The logic is simple:

More users → more seats → more revenue

But autonomous AI agents can change that relationship.

If one employee can use AI to perform work that previously required several people interacting with software, the number of human users may no longer reflect the amount of value generated.

Deloitte expects usage-based and outcome-oriented approaches to gain importance as agentic AI changes the relationship between software consumption and value.

This does not mean seat-based pricing will disappear.

For many collaborative applications, human users will remain central.

But SaaS vendors may increasingly experiment with combinations of:

• Base subscription
• Seats
• Usage
• Transactions
• Compute
• AI consumption
• Completed tasks
• Outcomes

What leaders should evaluate

Ask:

“Does our pricing metric increase when customer value increases?”

If not, the company may eventually need a different pricing architecture.

4. Usage-Based and Outcome-Based Pricing Will Expand

Usage-based pricing charges according to consumption.

Examples include:

• API calls
• Data processed
• AI tokens
• Automated tasks
• Transactions
• Storage
• Compute

Outcome-based pricing goes one step further.

Instead of charging for activity, the vendor attempts to charge according to business results.

For example:

• Resolved customer cases
• Completed transactions
• Qualified leads
• Automated workflows
• Revenue-generating actions

The challenge is measurement.

An outcome may depend on multiple systems and human decisions.

Deloitte notes that defining and measuring agents, tasks, processes, interactions, and outcomes will become increasingly important as these pricing models develop.

Practical recommendation

SaaS leaders should begin modeling several pricing scenarios now.

Compare:

Per-seat

Per-seat + usage

Base subscription + consumption

Usage only

Outcome-based

Then evaluate:

• Revenue predictability
• Customer understanding
• Gross margin
• Expansion potential
• Cost volatility
• Billing complexity

5. SaaS Will Become More Outcome-Oriented

Customers increasingly want software to produce measurable results.

They do not necessarily care that a platform has 150 features.

They care whether it helps them:

• Reduce costs
• Increase revenue
• Save time
• Reduce risk
• Improve productivity
• Automate repetitive work
• Make better decisions

AI makes this shift even more important.

If a product can execute work automatically, customers can evaluate it based on what it accomplishes rather than simply what functionality it provides.

Product strategy implication

Move your messaging from:

“Here are our features.”

toward:

“Here is the business outcome we help you achieve.”

That shift can influence product design, pricing, marketing, sales, and customer success simultaneously.

6. Software Interfaces Will Become More Conversational

Traditional SaaS interfaces are built around:

• Menus
• Dashboards
• Forms
• Filters
• Buttons
• Tables

AI creates another interaction model:

Intent → Conversation → Execution

Users can describe what they want in natural language.

For example:

“Show me customers whose usage dropped significantly this month and identify the likely reasons.”

The application can potentially generate the analysis without requiring the user to navigate several screens.

This does not mean dashboards will disappear.

Visual interfaces remain valuable for:

• Monitoring
• Reviewing
• Comparing
• Approving
• Auditing
• Exploring

The likely future is a combination of conversational and visual interaction.

7. AI Agents Will Create New Security Requirements

Traditional SaaS security primarily protects human users, applications, data, devices, and networks.

Agentic systems add another category:

Non-human actors capable of taking actions.

An agent may have permission to:

• Read customer records
• Send messages
• Modify databases
• Create transactions
• Access internal systems
• Trigger workflows

That creates a larger attack surface.

McKinsey notes that agentic AI can expand cyberattack surfaces because autonomous systems may independently decide what information and environments to access.

Security priorities will include

• Identity management
• Least-privilege access
• Agent authentication
• Permission boundaries
• Activity monitoring
• Audit trails
• Data governance
• Human approval
• Agent isolation
• Incident response

Security will increasingly need to answer:

“What is this AI system allowed to do?”

and:

“What exactly did it do?”

8. AI Governance Will Become a Core SaaS Requirement

AI adoption creates governance questions that traditional SaaS did not always need to address.

Organizations need to know:

• Which AI systems are being used?
• What data can they access?
• Which models process customer information?
• Who approved them?
• What actions can agents take?
• How are outputs monitored?
• What happens when an agent makes an error?

This is particularly important for regulated industries.

Governance should not be treated as a final compliance exercise.

It should be part of product architecture.

What leaders should establish

Create policies covering:

• AI approval
• Data access
• Model usage
• Vendor assessment
• Human oversight
• Logging
• Incident response
• Customer disclosure

Trust will become an increasingly important competitive factor.

9. Interoperability Will Matter More Than Ever

The future SaaS environment will likely contain more systems, agents, models, and specialized applications.

Customers will expect those systems to work together.

A product that works beautifully in isolation may be less useful if it cannot connect to:

• CRM systems
• ERP platforms
• Data warehouses
• Communication tools
• Identity providers
• AI platforms
• Workflow systems

Interoperability can therefore become a competitive advantage.

Deloitte highlights integration and interoperability as important considerations as organizations adopt increasingly agentic software environments.

Strategic question

Ask:

“Can our product become more valuable when connected to the customer's broader technology environment?”

If yes, integrations should be considered part of the core product strategy.

10. SaaS Platforms Will Replace More Point Solutions

AI can increase the value of platforms that coordinate multiple workflows.

Instead of buying separate applications for:

• Reporting
• Automation
• Research
• Content
• Analysis
• Customer communication

customers may increasingly prefer platforms that combine several capabilities.

But this does not mean every SaaS company should become a giant suite.

A specialized product can still win by becoming exceptionally valuable within a specific workflow.

The key distinction is:

Platform breadth vs. workflow depth

Companies should understand which one their market actually values.

11. Vertical SaaS Will Remain Important

Horizontal software serves many industries.

Vertical SaaS focuses deeply on a particular industry or profession.

Examples include software designed specifically for:

• Healthcare
• Construction
• Property management
• Legal services
• Financial services
• Manufacturing
• Restaurants

AI may actually strengthen vertical SaaS because domain-specific workflows can provide valuable context.

A generic AI system may understand language.

A vertical SaaS platform can understand:

• Industry workflows
• Regulatory requirements
• Specialized terminology
• Business rules
• Proprietary data
• Customer processes

Competitive advantage

Deep domain expertise can become a stronger moat when generic software capabilities become easier to replicate.

12. Proprietary Data Will Become More Valuable

AI capabilities can increasingly become commoditized.

Models improve quickly.

Features can be copied.

Interfaces can be replicated.

Data and workflow context can be harder to reproduce.

A SaaS company may develop valuable proprietary information through:

• Customer workflows
• Historical transactions
• Industry datasets
• Usage patterns
• Operational knowledge
• Domain-specific feedback

This does not mean companies should collect data indiscriminately.

Data must be collected, stored, and used responsibly.

But leaders should understand whether their data creates a legitimate product advantage.

13. AI Will Accelerate Software Development

AI-assisted development can help engineering teams:

• Write code
• Generate tests
• Debug problems
• Document systems
• Refactor code
• Explore prototypes
• Automate repetitive development tasks

Deloitte's 2026 software outlook argues that software creation is becoming faster and cheaper, increasing competitive pressure and enabling AI-native challengers to emerge.

This has an important consequence:

Code itself becomes less of a moat.

If more companies can build software quickly, differentiation needs to come from:

• Customer relationships
• Distribution
• Data
• Workflow integration
• Brand
• Domain expertise
• Trust
• Network effects
• Switching costs
• Execution

14. Product Development Will Become More Experimental

Lower development costs can change how companies build products.

Teams may be able to test:

• New interfaces
• Automated workflows
• Pricing concepts
• AI capabilities
• Personalization
• Internal tools

more quickly.

But faster development can also create more product complexity.

The answer is not to build everything AI makes possible.

The goal is to shorten the cycle between:

Hypothesis → Prototype → Customer feedback → Measurement → Decision

Speed is valuable when paired with disciplined product management.

15. Customer Expectations for Personalization Will Increase

AI can allow SaaS products to adapt more closely to individual users.

Personalization can include:

• Recommended workflows
• Customized dashboards
• Automated insights
• Role-specific experiences
• Adaptive onboarding
• Personalized notifications
• Context-aware assistance

Instead of every customer seeing the same product experience, software can increasingly respond to:

• Role
• Industry
• Usage
• Goals
• Behavior
• Account configuration

This can improve relevance, but personalization must remain understandable and controllable.

16. Customer Success Will Become More Proactive

Traditional customer success often reacts to problems.

AI can help teams identify risk earlier.

Signals may include:

• Declining usage
• Reduced login frequency
• Unused features
• Failed workflows
• Support activity
• Changes in account behavior

A system can potentially flag:

“This account's usage has declined substantially and two critical workflows have stopped running.”

The customer success team can then investigate before the customer reaches the cancellation stage.

The strategic shift

Move from:

Reactive support

to:

Predictive customer success

The objective remains human customer value.

AI simply helps teams recognize where intervention may matter most.

17. SaaS Marketing Will Become More Competitive

AI can reduce the cost of producing:

• Articles
• Ads
• Emails
• Videos
• Landing pages
• Research
• Sales materials

That creates an abundance problem.

If everyone can produce more content, content volume becomes less differentiating.

The competitive advantage shifts toward:

• Original insights
• Proprietary data
• Expert perspectives
• Customer evidence
• Strong brands
• Distribution
• Useful tools
• Community
• Product-led experiences

SaaS marketers should therefore focus less on producing maximum content and more on producing assets customers genuinely value.

18. Distribution May Become More Important Than Features

When software becomes easier to build, the ability to reach customers becomes increasingly valuable.

Distribution advantages can include:

• Strong SEO
• Existing communities
• Partnerships
• Brand recognition
• Enterprise relationships
• Integrations
• Marketplaces
• Referrals
• Product virality

A technically impressive product still needs a reliable path to customers.

Business leaders should therefore treat distribution as a strategic asset rather than merely a marketing function.

19. AI Will Increase Competitive Pressure

The cost of building software is falling in many areas.

That can enable:

• New startups
• Internal corporate tools
• Smaller competitors
• Industry-specific products
• New software categories

Deloitte expects AI-native challengers to create competitive pressure on established software companies and potentially address markets that were previously underserved.

Established SaaS companies should not assume that their existing market position guarantees future defensibility.

They need to continuously evaluate:

• New entrants
• Customer alternatives
• Internal build options
• AI-native competitors
• Platform changes

20. SaaS Buyers Will Demand Clearer ROI

As AI increases software capabilities, buyers may become more skeptical of vague claims.

A product that costs $50,000 annually needs to demonstrate meaningful value.

Business buyers will increasingly ask:

• What does this automate?
• How much time does it save?
• What revenue does it influence?
• What costs does it reduce?
• What risks does it mitigate?
• How quickly can we realize value?

This makes ROI measurement an increasingly important part of SaaS sales and customer success.

21. SaaS Procurement Will Become More Complex

The number of AI-enabled applications in organizations can increase technology complexity.

Procurement teams may need to evaluate:

• AI models
• Data processing
• Security
• Compliance
• Integrations
• Usage costs
• Vendor concentration
• Agent permissions

A product that makes these questions easy to answer can gain an advantage.

Provide clear:

• Security documentation
• Data policies
• AI documentation
• Pricing explanations
• Integration details
• Compliance information

Transparency can shorten enterprise evaluation cycles.

22. SaaS Cost Management Will Become More Important

Traditional SaaS economics often benefited from relatively predictable infrastructure costs.

AI workloads can introduce more variable expenses.

Costs may depend on:

• Model usage
• Tokens
• Compute
• Agent activity
• Data processing
• Inference volume

Deloitte notes that the economics of AI differ from traditional cloud software and that AI costs and hybrid pricing can create pressure on margins.

SaaS companies therefore need better visibility into unit economics.

Track:

Revenue per customer − infrastructure and AI costs = contribution economics

The exact accounting model will differ by business, but the principle is universal:

Know what each new unit of usage costs.

23. FinOps Will Expand Beyond Cloud Infrastructure

FinOps traditionally focused on managing cloud spending.

As AI becomes embedded in products, companies may need to understand the cost of:

• Model inference
• Tokens
• Agents
• API calls
• Data processing
• Storage
• Compute

This creates a new operational discipline.

Product teams need to understand cost implications when designing AI features.

A feature that increases engagement by 20% but increases inference costs by 200% requires careful evaluation.

24. Human Oversight Will Remain Important

The future of SaaS is not necessarily a world where humans disappear from software workflows.

For high-risk decisions, humans may still need to:

• Approve
• Review
• Escalate
• Correct
• Audit

Deloitte highlights transparency, explainability, reversibility, and auditability as important considerations for autonomous systems.

The best product design may therefore be:

Human intent + AI execution + human oversight

rather than unrestricted automation.

25. Trust Will Become a SaaS Differentiator

As software gains more autonomy, customers need confidence that it will behave appropriately.

Trust can come from:

• Security
• Reliability
• Transparency
• Explainability
• Audit logs
• Clear permissions
• Human controls
• Strong support

This is especially important when software can perform consequential actions.

A customer may tolerate a recommendation error.

They may not tolerate an autonomous system making an irreversible financial or operational decision without appropriate controls.

26. The SaaS Interface May Become Less Visible

One of the most interesting long-term possibilities is that users may interact less directly with individual applications.

Instead, AI agents could operate across several systems.

For example:

1. An employee asks an AI agent to prepare a customer renewal.
2. The agent retrieves CRM information.
3. It checks billing data.
4. It reviews product usage.
5. It generates a recommendation.
6. It prepares the renewal communication.
7. The employee approves it.

The employee may never manually open each underlying application.

Gartner describes this possibility as a shift in which software becomes less visible as agents complete tasks across systems.

That creates an important strategic question:

If customers interact with our software less, how do we remain valuable?

The answer may increasingly be through being the trusted system of record, execution layer, data source, workflow engine, or specialized intelligence provider.

27. SaaS Companies Will Need Stronger Ecosystem Strategies

As software becomes more interconnected, ecosystems can become powerful distribution and product advantages.

Opportunities include:

• Integration marketplaces
• Agent marketplaces
• Developer platforms
• APIs
• Partner programs
• Embedded software
• Data partnerships

A SaaS company does not necessarily need to own every capability.

It can become the central platform around which other capabilities operate.

28. Multi-Agent Systems Could Become a New Architecture

A single AI agent may eventually be insufficient for complex business processes.

Different specialized agents could perform different tasks.

For example:

Research agent → Analysis agent → Compliance agent → Execution agent → Audit agent

The system becomes an orchestrated network rather than a single application.

This creates new technical requirements around:

• Identity
• Permissions
• Communication
• Observability
• Error handling
• Governance
• Billing

SaaS leaders should therefore think about architecture beyond individual AI features.

29. SaaS Business Models Will Become More Flexible

The future may not belong to one pricing model.

Instead, companies may combine:

Subscription + seats + usage + outcomes

For example:

• Base platform subscription
• Per-user collaboration fee
• AI consumption charge
• Usage-based automation
• Premium outcome-based services

Hybrid pricing can align revenue with value, but it can also make bills harder to predict.

The winning model will need to balance:

• Customer simplicity
• Vendor economics
• Predictability
• Expansion
• Value alignment

30. Business Leaders Should Prepare for Multiple Futures

No one can know exactly how quickly every SaaS trend will develop.

Agentic AI may transform some categories rapidly and others much more slowly.

Some applications may become agent-driven.

Others may remain heavily human-operated.

Some pricing models may change quickly.

Others may remain seat-based for years.

Therefore, strategic planning should use scenarios.

Scenario A: AI augmentation

AI improves existing SaaS workflows.

Scenario B: Agentic transformation

Agents perform substantial portions of customer workflows.

Scenario C: Platform displacement

Some standalone applications become capabilities inside broader AI platforms.

Scenario D: Hybrid ecosystem

Humans, agents, SaaS applications, and specialized platforms work together.

Build strategies that remain viable across multiple scenarios.

31. How SaaS Leaders Should Respond Now

Business leaders do not need to predict every technology development.

They need to build organizational capabilities that remain useful across scenarios.

Focus on:

Customer value

Know exactly what customers pay you to accomplish.

Data

Understand which data assets improve your product.

AI capability

Experiment with AI where it creates measurable value.

Security

Build appropriate controls before autonomous systems gain broad permissions.

Interoperability

Make it easy for your product to connect with the broader ecosystem.

Pricing

Model alternatives to purely seat-based pricing.

Economics

Track AI and infrastructure costs at the feature and customer level.

Distribution

Strengthen channels competitors cannot easily copy.

Talent

Develop teams capable of combining domain knowledge with AI-enabled product development.

32. A Practical SaaS Future-Readiness Framework

Use the following framework to evaluate your organization.

Product

• AI roadmap exists
• Core workflows clearly defined
• Automation opportunities identified
• Customer outcomes measured

Data

• Data quality monitored
• Important proprietary data identified
• Data governance established
• AI data access controlled

Technology

• APIs available
• Integrations supported
• AI architecture documented
• Observability implemented

Security

• AI permissions controlled
• Agent actions logged
• Human approval defined for high-risk tasks
• Security testing updated for AI workflows

Commercial

• Pricing model reviewed
• Usage economics understood
• AI costs measured
• Customer ROI documented

Organization

• Employees trained on AI
• Product and engineering workflows updated
• Cross-functional AI governance established
• Leadership reviews AI opportunities regularly

33. The Most Important SaaS Trends to Watch

If you cannot track dozens of trends, focus on these:

1. Agentic AI

Watch how quickly customers allow AI systems to execute real work.

2. Pricing transformation

Monitor movement from seats toward usage and outcomes.

3. AI-native competitors

Track new companies built around AI rather than retrofitted onto traditional SaaS.

4. Security and governance

Watch how enterprise requirements evolve around autonomous systems.

5. Interoperability

Monitor whether customers increasingly expect applications and agents to work across platforms.

6. Software development economics

Watch how AI-assisted development changes competitive intensity.

7. Customer ROI expectations

Measure whether buyers increasingly demand quantified outcomes.

8. Data advantages

Determine whether proprietary data is becoming a meaningful source of differentiation.

34. The Future of SaaS Will Reward Adaptability

The most important trend may not be AI itself.

It may be adaptability.

Technology changes quickly.

Customer behavior changes.

Competitors change.

Pricing models change.

Distribution channels change.

A company that is structurally capable of learning and adapting can respond more effectively than one built around a rigid assumption about how SaaS should work.

That means leaders should create systems for:

• Rapid experimentation
• Customer research
• Product feedback
• Financial modeling
• Security review
• Competitive analysis
• Technology evaluation

The objective is not to chase every trend.

It is to recognize which trends matter to your customers before they become obvious.

Frequently Asked Questions About the Future of SaaS

What is the biggest trend shaping the future of SaaS?

AI—particularly agentic AI—is one of the most consequential forces affecting SaaS. It can change product interfaces, automation, pricing, software development, security, and customer expectations. The effect will vary by application category and business model.

Will SaaS disappear because of AI agents?

There is no basis for assuming SaaS will simply disappear. AI agents may reduce the amount of direct human interaction with some applications, while increasing the value of systems that provide trusted data, workflows, integrations, governance, and execution capabilities. Gartner and Deloitte both describe significant potential disruption while also emphasizing a gradual transition rather than an immediate replacement of enterprise SaaS.

Will seat-based SaaS pricing disappear?

Seat-based pricing is likely to remain relevant for many products, particularly collaborative applications. However, AI agents can weaken the relationship between the number of human users and the amount of work performed, creating incentives for vendors to experiment with usage-, consumption-, and outcome-based models.

What should SaaS companies do about AI right now?

Start with customer problems rather than technology. Identify workflows where AI can create measurable value, test those applications, evaluate their economics, and establish appropriate security and governance controls. Avoid adding AI features simply for marketing purposes.

How will AI affect SaaS margins?

AI can improve productivity and create new revenue opportunities, but inference, model, and infrastructure costs can also increase expenses. Deloitte specifically notes that AI costs and hybrid pricing can place pressure on software economics and margins.

Why will SaaS security become more important?

AI agents can access data and perform actions autonomously, creating new risks around identity, permissions, data exposure, and unintended actions. McKinsey identifies expanded attack surfaces and the need to govern autonomous systems as important consequences of agentic AI adoption.

What should business leaders prioritize when planning for the future of SaaS?

Focus on customer value, AI capabilities, data, security, interoperability, pricing flexibility, unit economics, distribution, and organizational adaptability. Scenario planning can help leaders prepare without assuming that one specific technology outcome is guaranteed.

Conclusion

The future of SaaS is likely to be more intelligent, automated, interconnected, and outcome-oriented than the software industry of the previous decade.

AI agents may change how people interact with applications. Pricing may evolve beyond simple seat-based subscriptions. Security and governance will become increasingly important as software gains more autonomy. Interoperability and proprietary data may become stronger sources of competitive advantage, while AI-assisted development may make software itself easier to produce.

But technology alone will not determine which SaaS companies succeed.

Customer value will remain fundamental.

The companies best positioned for the next phase will be those that understand their customers deeply, use AI where it creates measurable value, maintain strong economics, protect customer data, build adaptable products, and develop distribution advantages that are difficult to replicate.

Business leaders should therefore avoid treating the future of SaaS as a prediction exercise.

Instead, treat it as a strategic preparation exercise.

Identify the assumptions behind your current product, pricing, distribution, technology, and competitive advantage. Then test how those assumptions hold up as AI agents, new pricing models, autonomous workflows, and changing customer expectations become more important.

The future will not arrive in exactly the same way for every SaaS category.

The organizations prepared to learn, experiment, and adapt will be better equipped to navigate whatever comes next.