How AI Is Reshaping the Martech Landscape — and What It Means for Marketing Teams

AI is not simply adding more tools to the marketing technology landscape. It is changing how martech is built, bought, connected and used.

For most of the past 15 years, one trend defined the marketing technology market:

More.

More software companies. More specialist categories. More platforms promising to solve increasingly narrow parts of the customer journey.

The annual Marketing Technology Landscape grew from around 150 products in 2011 to 15,505 in 2026.

The headline number has now almost stopped increasing. The 2026 landscape grew by only 0.79%.

But that apparent stability is misleading.

During the year:

• 1,488 products were added.

• 1,367 products were removed.

• Established SaaS companies disappeared.

• New AI-native platforms continued to emerge.

• Existing platforms absorbed capabilities that previously required separate tools.

The martech landscape is not standing still. It is experiencing a structural reorganisation.

AI is simultaneously:

• Creating new marketing technologies

• Expanding the capabilities of established platforms

• Making some standalone tools less necessary

• Allowing businesses to build their own applications and workflows

• Changing how customers discover and interact with brands

• Moving martech from fixed interfaces towards agent-accessible infrastructure

The result is not simply a larger or more intelligent martech stack.

It is a different model for how marketing technology operates.

This article explores what is changing, what is likely to remain important and what marketing teams should do differently as a result.

For specific platform recommendations, costs and example stacks, see our companion guide to the best marketing tech stack for every stage of growth.

What did the traditional martech landscape look like?

Before generative AI, most marketing technology followed a recognisable software-as-a-service model.

A business identified a requirement and purchased a platform designed to perform it.

For example:

• A CRM stored contact and sales information.

• An email platform sent campaigns and automated sequences.

• An analytics platform measured website activity.

• An SEO platform monitored keywords and competitors.

• A social-media platform scheduled posts.

• A landing-page builder created campaign pages.

• An experimentation platform ran A/B tests.

• An integration platform moved information between systems.

Marketers operated these tools through their user interfaces.

They logged in, reviewed dashboards, configured campaigns, created rules and moved information between platforms.

Automation was usually deterministic:

When this happens, do that.

For example:

When someone submits this form, create a CRM record, add the contact to a list and send a confirmation email.

This model could be very effective, but it created several recurring problems.

Tools accumulated faster than they were removed

New requirements frequently produced new subscriptions.

Over time, businesses acquired:

• Overlapping features

• Duplicate customer records

• Disconnected reports

• Underused software

• Unmaintained automations

• Conflicting definitions

• Increasingly complex integrations

Software features determined processes

Businesses often adapted their processes to match what a platform could do, rather than designing the best process and using technology to support it.

Data remained trapped within applications

Each platform became a partial version of the truth.

Advertising platforms knew about clicks and conversions. The CRM knew about leads and opportunities. The ecommerce platform knew about orders. The email system knew about engagement.

Connecting these views required integrations, exports, dashboards and considerable operational effort.

The user interface was the product

The value of a martech platform was largely experienced through the interface a marketer used.

That assumption is now beginning to change.

AI is becoming a capability across the landscape

It is tempting to think of AI as another category of marketing technology.

That was a reasonable way to view the first wave of AI marketing products:

• AI writing tools

• AI image generators

• AI chatbots

• AI subject-line optimisers

• AI research assistants

• AI analytics tools

But AI is no longer confined to a category.

It is becoming a horizontal capability embedded across almost every part of martech.

Martech area	How AI is changing it
Content	Research, ideation, drafting, editing, repurposing and localisation
Paid media	Creative generation, audience modelling, bidding and campaign analysis
CRM	Lead summaries, scoring, enrichment and recommended next actions
Email and lifecycle	Content generation, segmentation, timing and journey optimisation
Analytics	Natural-language queries, anomaly detection and interpretation
SEO and AI search	Content analysis, prompt monitoring and citation visibility
CRO	Research synthesis, hypothesis generation and experience personalisation
Customer service	Conversational support, classification and resolution
Research	Interview synthesis, competitor monitoring and insight extraction
Marketing operations	Workflow creation, data transformation and agent orchestration

This means businesses increasingly have three ways to access the same underlying capability.

They can:

1. Use AI embedded within software they already own.

2. Purchase a specialist AI-native product.

3. Build a custom assistant, workflow or agent.

The 2026 State of Martech report describes the emerging model as build, buy and borrow rather than a simple choice between building or buying.

A business may use AI inside an established CRM, add a specialist AI tool for one important task and create its own agent for a process unique to the organisation.

The three forces reshaping the martech market

1. Established platforms are absorbing AI capabilities

Large martech platforms possess several important advantages:

• Existing customer data

• Established integrations

• User permissions

• Familiar interfaces

• Procurement approval

• Operational history

• Customer trust

• Access to active workflows

They can therefore add AI directly to processes marketers already use.

A CRM can generate a sales summary without requiring data to be exported.

An email platform can draft variants using existing customer segments.

An analytics platform can explain a performance change using the data it already holds.

An ecommerce platform can generate product descriptions using its product catalogue.

This creates a strong argument for checking existing platform capabilities before purchasing a new specialist tool.

A business may discover that it is already paying for:

• AI content generation

• Predictive scoring

• Conversation summaries

• Workflow recommendations

• Campaign analysis

• Customer segmentation

• Creative variations

• Natural-language reporting

This does not mean the embedded AI will always be the best option.

Platform-native features can be:

• Relatively generic

• Restricted to expensive plans

• Limited to data inside that platform

• Difficult to customise

• Designed to strengthen vendor lock-in

But they have a significant practical advantage:

They already sit inside the work.

2. AI-native tools are attacking individual tasks

AI-native companies can focus on solving one problem exceptionally well.

They may specialise in:

• Ad creative production

• Sales-call analysis

• Content repurposing

• Customer research

• AI-search monitoring

• Video generation

• Data enrichment

• Proposal creation

• Personalised outbound

• Campaign reporting

These products can often move faster than established platforms.

They are not constrained by legacy interfaces, historical architecture or the need to serve every existing customer workflow.

But the category also contains considerable risk.

A specialist product may be little more than:

• A well-designed interface around a general AI model

• A feature that an incumbent platform will soon include

• A workflow that a business could reproduce itself

• A product with limited access to proprietary data

• A business without a durable competitive advantage

The rapid contraction of the content-marketing software category illustrates this problem.

As general-purpose AI platforms and established SaaS providers absorbed basic writing, summarisation and repurposing features, many standalone content-AI tools became harder to justify. The 2026 martech landscape recorded 176 removals from the content-marketing category, the highest outflow of any category.

Before adding an AI-native tool, ask:

• Does it solve a sufficiently important problem?

• Is it meaningfully better than our general AI workspace?

• Does it have access to the context it needs?

• Can it integrate with our existing process?

• Does it produce measurable additional value?

• Is this a durable product or a temporary feature gap?

3. Businesses can build their own marketing capabilities

The third force may be the most consequential.

AI, APIs, automation platforms and low-code tools have made it easier for businesses to build small pieces of software around their own processes.

A custom marketing capability no longer necessarily means commissioning a major development project.

It might be:

• An assistant trained on brand and product information

• A workflow that analyses new enquiries

• An agent that prepares sales-meeting research

• A system that identifies declining content

• A reporting assistant connected to campaign data

• A workflow that checks campaign setup before launch

• A tool that classifies customer feedback

• An agent that monitors CRM data quality

These solutions sit between traditional software and manual work.

They are not broad platforms designed for thousands of companies. They are narrow capabilities designed around the context of one business.

This changes the martech purchasing question.

Previously, a new requirement often led to:

Which platform should we buy?

The new question is:

Should we use a feature we already have, buy a specialist product, build a workflow ourselves or retain a human-led process?

AI app builders are making custom martech applications more accessible

Custom marketing technology previously implied a significant software-development project.

That is beginning to change.

AI application-building platforms such as Lovable, Base44 and Retool allow businesses to describe the application they need using natural language, then generate and iterate on a working web application.

These tools can be used to create:

• Internal marketing dashboards

• Campaign-planning systems

• Customer-research repositories

• Lead-qualification interfaces

• Content workflow tools

• Client reporting portals

• CRM data-quality applications

• Product-selection tools

• Bespoke marketing calculators and planning tools

This is different from using an automation platform such as Make or n8n.

An automation platform primarily connects systems and moves work between them. An AI app builder can create the interface through which people interact with that workflow, review information and make decisions.

The two can also work together.

For example:

1. Marketing data is retrieved from advertising, analytics and CRM platforms.

2. An AI model analyses the information.

3. Make or n8n orchestrates the workflow.

4. A custom application built in Lovable presents the findings.

5. A marketer reviews the recommendations and approves an action.

6. The workflow updates the relevant systems.

This gives startups and SMEs another alternative to purchasing a large specialist SaaS product.

Rather than subscribing to software designed around the average needs of thousands of companies, they may be able to build a smaller application around their own processes, data and terminology.

However, easier development does not eliminate the responsibilities associated with software.

Businesses still need to consider:

• Security

• Data protection

• User permissions

• Testing

• Reliability

• Maintenance

• Code and data ownership

• Integration with systems of record

• What happens when the original builder leaves

• Whether the application is robust enough for a business-critical process

AI app builders reduce the barrier to creating software. They do not remove the need for appropriate technical judgement and governance.

Martech is moving from applications towards agent-accessible infrastructure

For most of the SaaS era, martech companies competed to become the place where marketers worked.

The marketer opened the application, used its interface and completed the task.

In an agentic environment, a growing share of a platform’s value may sit below the interface.

The important questions become:

• Can an AI system retrieve the correct data?

• Can it understand what the data means?

• Can it use the platform’s tools?

• Can it act with the correct permissions?

• Can it update the system of record?

• Can its actions be monitored and reversed?

The 2026 State of Martech report describes this as a shift from applications humans operate towards infrastructure agents can use.

Technologies such as APIs and the Model Context Protocol can allow AI systems to interact with external tools and information.

This does not make interfaces irrelevant.

People will continue to use dashboards, campaign builders and CRM records.

But it creates a second operating model:

The marketer expresses the objective, while an AI system retrieves information and uses approved tools to complete parts of the work.

That changes what businesses should assess when choosing martech.

Traditional software-selection questions still matter:

• Is it easy to use?

• Does it contain the features we need?

• Is it affordable?

• Does it integrate with our existing systems?

But new questions are emerging:

• Does it provide an API or agent connector?

• Can access be restricted by role and task?

• Can an agent read and write information safely?

• Can actions require human approval?

• Is there a clear audit trail?

• Can the data be exported?

• Is the underlying context accessible outside the platform?

A platform may have an excellent interface but become less valuable if it cannot participate in the wider AI operating environment.

Marketing automation is becoming more agentic

Traditional marketing automation remains extremely useful.

Many processes do not require artificial intelligence.

For example:

• Add a form submission to the CRM.

• Send a booking confirmation.

• Update a contact field.

• Notify a sales owner.

• Add a customer to an agreed sequence.

• Copy campaign data into a reporting table.

These activities follow predictable rules.

Using an AI agent would add cost and uncertainty without improving the outcome.

AI becomes valuable when a process requires interpretation.

Traditional automation

When a contact submits this form, create a CRM record and assign it to the specified owner.

AI-assisted workflow

When a contact submits this form, use AI to summarise the requirement, then create the CRM record and assign it using predefined rules.

Supervised agentic workflow

Analyse the enquiry, research the company, retrieve relevant CRM history, assess likely fit, recommend the appropriate owner and draft a response for human approval.

Governed agent

Monitor new enquiries, use approved data and tools, handle routine cases within defined boundaries and escalate uncertain or high-value opportunities.

The difference is not simply that the AI writes some text.

An agent can potentially:

• Interpret an objective

• Decide which information is required

• Select from approved tools

• Perform several steps

• Adapt to what it discovers

• Recommend or execute an action

AI-agent adoption is increasing, but enterprise deployment remains relatively early.

McKinsey’s 2025 global survey found that 23% of respondents said their organisations were scaling an agentic AI system somewhere in the enterprise, while a further 39% were experimenting. However, no individual business function had more than 10% of respondents reporting scaled agent use.

This matters because the immediate future of agentic marketing is unlikely to involve completely autonomous marketing departments.

It is more likely to involve supervised agents operating within defined workflows.

The agent performs research, interpretation and preparation.

The human sets the objective, approves significant decisions and remains accountable for the outcome.

Context is becoming the most valuable layer of the stack

General AI models are widely available.

Your competitors can often access the same models you can.

The model itself is therefore unlikely to be a lasting competitive advantage.

The advantage comes from the context available to it.

That context includes:

Customer context

• Who is the customer?

• What are they trying to achieve?

• What have they previously done?

• Which products or services are relevant?

• What has sales already discussed?

• What questions or concerns have they expressed?

• Which permissions and preferences apply?

Company context

• What is the business trying to achieve?

• Who is the intended audience?

• What is the positioning?

• What claims are approved?

• How should the brand communicate?

• Which commercial rules apply?

• What does a good outcome look like?

• Which actions require approval?

Systems context

• Which data is available?

• Where is the authoritative record?

• Which tools can be used?

• What permissions does the agent have?

• Which workflow should follow?

• Where should the result be stored?

• What happens when information conflicts?

The State of Martech 2026 argues that context is the difference between plausible output and useful action.

An AI system does not simply need to know how to “send an email”. It needs to understand which customer should receive it, what they have already experienced, what the company is permitted to say and what should happen next.

This is why AI makes several existing marketing disciplines more important:

• Customer research

• Brand strategy

• CRM architecture

• Data governance

• Measurement planning

• Content management

• Workflow documentation

• Product information management

• Consent management

AI does not eliminate the need for clear marketing thinking.

It exposes where that thinking has never been documented.

AI makes good data more important, not less

A common misconception is that AI can compensate for disorganised data.

It cannot reliably fix:

• Duplicate CRM records

• Inconsistent acquisition sources

• Broken tracking

• Missing consent information

• Conflicting lifecycle stages

• Unclear customer definitions

• Disconnected platforms

• Inaccurate product data

• Unreliable revenue records

An AI system may help identify or clean some of these problems.

But if the information supplied to it is incomplete or contradictory, its conclusions and actions will also be unreliable.

Salesforce’s 2026 State of Marketing research found that 83% of marketers recognised the move towards personalised, two-way engagement, but only one in four were satisfied with how they used data to support those experiences.

Adobe similarly argues that organisations should prioritise data unification, quality and accessibility before using agentic AI in customer interactions. Its 2026 research identifies unified data, scalable content and connected workflows as the foundations required to turn AI investment into operational value.

This is why the most valuable AI project may not begin with an agent.

It may begin with:

• Cleaning the CRM

• Agreeing lifecycle definitions

• Improving conversion tracking

• Connecting sales and marketing data

• Documenting the customer journey

• Defining the system of record

• Establishing permissions

• Creating reliable product and brand information

Our guide to common data problems for marketers covers several of the underlying issues that can undermine analytics, automation and personalisation.

Content production is becoming cheaper — and therefore less differentiating

Content generation was one of the first obvious applications of generative AI.

Marketing teams can now produce first drafts of:

• Articles

• Emails

• Ad copy

• Social posts

• Product descriptions

• Landing pages

• Images

• Videos

• Reports

• Sales collateral

This can reduce production time and make adaptation easier.

But it also creates a new problem:

When everyone can produce more content, producing content is no longer enough.

The supply of competent but undistinguished content is increasing.

The differentiators become:

• Original experience

• Proprietary evidence

• Clear opinions

• Recognisable expertise

• Customer understanding

• Useful tools

• Strong creative direction

• Trust

• Distribution

• Brand consistency

AI can help organise an argument, improve a draft or adapt an idea for different formats.

It cannot manufacture genuine experience that the business does not possess.

The collapse of some specialist AI-content products also demonstrates that “generate more copy” is not necessarily a durable value proposition. General AI platforms and established martech providers now include many capabilities that previously justified separate subscriptions.

The strategic question is therefore shifting from:

How can we produce more content?

to:

What can we publish that is genuinely worth finding, trusting and citing?

AI is changing how customers discover brands

AI is not only changing the tools marketers use.

It is changing the environment in which customers make decisions.

Customers can now ask AI systems to:

• Explain a problem

• Compare solutions

• Recommend suppliers

• Summarise reviews

• Research products

• Build a shortlist

• Interpret technical information

• Prepare purchasing questions

• Plan an implementation

Adobe’s 2026 research found that 65% of surveyed customers regularly or occasionally used AI tools in their work or personal lives, while 56% believed AI would improve their overall experience with brands.

This creates an important new audience for websites and marketing information.

Traditionally, websites were built primarily for:

1. Human visitors

2. Search-engine crawlers

A third audience is emerging:

3. AI systems acting on behalf of humans

These systems may want to:

• Extract information

• Compare claims

• Verify evidence

• Understand pricing

• Identify suitability

• Summarise differences

• Recommend a next action

The 2026 Marketing Technology Landscape attributes some of the renewed growth in CMS, ecommerce and analytics technologies to the need to serve AI assistants, shopping agents and other machine-mediated experiences.

This does not mean businesses should create content for machines instead of people.

It means information should be:

• Clear

• Accurate

• Structured

• Specific

• Accessible

• Consistent

• Supported by evidence

• Easy to extract without losing its meaning

For practical guidance, read our complete guide to AI search optimisation and getting your website cited by ChatGPT, Google AI Overviews, Gemini, Perplexity and Claude.

Customer journeys are becoming conversations

Traditional martech was designed around journeys planned by the business.

A marketer created a sequence:

1. Show an advert.

2. Send someone to a landing page.

3. Capture an email address.

4. Send a nurture sequence.

5. Encourage a sales conversation.

6. Retarget non-converters.

These journeys still exist.

But AI can make customer behaviour less linear and less controllable.

A potential customer may:

• Research the problem through an AI assistant

• Ask it to compare several approaches

• Visit one or two supplier websites

• Return to the AI with follow-up questions

• Ask it to summarise a technical document

• Use it to evaluate a proposal

• Involve it again during implementation

The customer’s AI assistant may become an intermediary throughout the buying journey.

At the same time, businesses are introducing their own conversational interfaces:

• Product advisors

• Support assistants

• Sales agents

• Configurators

• Onboarding guides

• Account assistants

This creates a shift from predefined journeys towards dynamic conversations.

But personalisation will only be valuable when it is based on relevant, reliable context.

A poorly informed agent can produce an experience that is fast but inappropriate.

Our article on AI-powered personalisation and the importance of data infrastructure examines this relationship in more detail.

It is also important to retain a genuine understanding of how people make decisions. Our guide to the marketing funnel and customer journey explains how to combine measurable funnel stages with the less linear reality of customer behaviour.

Marketing roles are changing from tool operation to system design

AI will not affect every marketing role in the same way.

But work that consists primarily of transferring information between systems or producing predictable first drafts is likely to change significantly.

Marketers may spend less time:

• Manually compiling reports

• Reformatting content

• Moving data between platforms

• Producing routine variations

• Summarising meetings

• Creating repetitive campaign documentation

• Checking straightforward setup requirements

They may spend more time:

• Defining objectives

• Designing workflows

• Supplying context

• Evaluating outputs

• Managing exceptions

• Setting permissions

• Improving customer experiences

• Interpreting commercial impact

• Coordinating human and AI specialists

The State of Martech 2026 describes a progression from campaign manager to agent operator and, ultimately, value engineer.

It also suggests marketing-operations roles will shift from system administration towards context engineering: ensuring the right data, instructions, permissions and tools are available to the right agent at the right time.

This does not necessarily mean every marketer needs to become a developer.

But marketing teams will need stronger capabilities in:

• Process design

• Data literacy

• AI evaluation

• Workflow management

• Experimentation

• Governance

• Cross-functional collaboration

The valuable marketer will not simply know how to operate one platform.

They will understand:

• The customer

• The commercial objective

• The channel

• The data

• The technology

• Where AI can improve the process

• Where human judgement still matters

Martech purchasing will become more fluid

The traditional martech stack was relatively fixed.

Businesses bought platforms, implemented them and expected to use them for several years.

AI is creating a more fluid environment.

A capability might be supplied through:

• An existing SaaS platform

• A general-purpose AI workspace

• A specialist AI tool

• An automation platform

• A custom-built agent

• A temporary workflow

• A human specialist

The correct solution may change as:

• Model capabilities improve

• Platform features expand

• Pricing changes

• Integration standards develop

• The business accumulates more data

• Internal expertise increases

• A process becomes more valuable or complex

This means martech governance must include regular simplification.

Businesses should periodically ask:

• Do we still need this specialist tool?

• Has another platform absorbed the functionality?

• Could a general AI workspace now perform the task?

• Is a custom workflow more appropriate?

• Is this process important enough to automate?

• Has the tool become a system of record without being designed as one?

• Can we remove anything before adding something new?

The winning stack may not be the one that adds AI most aggressively.

It may be the one that uses AI to remove unnecessary complexity.

The risks of an AI-shaped martech landscape

Tool proliferation

AI can make the martech stack larger rather than simpler.

Teams may buy separate tools for:

• Content

• Research

• Reporting

• Email

• Advertising

• Social media

• Meeting notes

• Personalisation

• Lead qualification

Many may overlap with each other or with functionality already available elsewhere.

AI washing

Some products apply the AI label to functionality that is largely conventional automation, prediction or third-party model access.

The presence of AI does not automatically make a product more useful.

Poor data and misplaced confidence

AI can turn unreliable information into persuasive recommendations.

The confidence of the output should not be mistaken for the quality of the evidence.

Brand dilution

High-volume AI content can make a brand sound generic, inconsistent or indistinguishable from its competitors.

Privacy and permissions

Teams need to understand:

• Which information is being supplied

• Where it is processed

• How it is retained

• Whether it may be used for training

• Which users and agents can access it

• Whether customer consent permits the intended use

Automation without accountability

When an agent can take action, the risk changes.

The question is no longer only whether AI might say something inaccurate.

It may:

• Update the wrong record

• Send an inappropriate message

• Change a campaign

• Misclassify a customer

• Use a tool outside its intended scope

McKinsey’s 2026 AI-trust research warns that organisations must now manage the risk of systems doing the wrong thing, including taking unintended actions, misusing tools or operating beyond suitable guardrails.

Its wider AI survey also found that 51% of respondents from organisations using AI had experienced at least one negative consequence, with inaccuracy the most frequently reported issue.

Unclear ROI

More content, more automations and more AI usage do not necessarily create more business value.

AI initiatives should be measured against outcomes such as:

• Revenue

• Conversion rate

• Customer acquisition cost

• Lead quality

• Retention

• Time to launch

• Customer satisfaction

• Error rate

• Cost of delivery

• Time saved

What does this mean for startups and SMEs?

AI creates an unusually significant opportunity for smaller businesses.

Capabilities that previously required large teams or expensive platforms are becoming more accessible.

A small business can now use AI to support:

• Research

• Content operations

• Campaign analysis

• Customer-service triage

• CRM administration

• Meeting preparation

• Reporting

• Workflow automation

• Sales enablement

• Customer-insight synthesis

This can allow a smaller team to operate with greater breadth and speed.

But startups and SMEs also have less capacity to absorb mistakes.

They cannot afford to accumulate:

• Dozens of subscriptions

• Fragile integrations

• Unmaintained agents

• Conflicting customer data

• Tools nobody owns

• Workflows nobody understands

• Outputs nobody checks

The objective should not be to imitate an enterprise martech stack with a smaller budget.

It should be to build the smallest connected system that supports the current customer journey and growth priorities.

AI should make the stack leaner and the team more capable—not merely increase the number of products being used.

How should marketing teams respond?

1. Start with the growth constraint, not the AI tool

Do not begin with:

Where can we use an AI agent?

Begin with:

What is currently limiting growth or consuming disproportionate time?

The problem might be:

• Slow lead follow-up

• Poor campaign reporting

• Weak content production

• Disconnected customer insight

• Inconsistent CRM data

• Inefficient campaign setup

• Lost sales information

• Limited lifecycle communication

A structured growth marketing strategy can help identify which opportunities deserve attention before technology is selected.

2. Audit the current stack and existing capabilities

Document:

• What every platform does

• What it costs

• Who uses it

• Which data it holds

• How it integrates

• Which AI features are included

• Where functionality overlaps

• Which workflows depend on it

Our guide to building the best marketing tech stack for every growth stage includes a practical audit framework and specific stack recommendations.

3. Prioritise use cases rather than buying tools

Assess each opportunity against:

• Commercial value

• Frequency

• Time saved

• Data readiness

• Implementation effort

• Risk

• Ease of measurement

• Need for human judgement

A frequent, rules-based process may need conventional automation.

A variable process involving interpretation may justify an AI-assisted workflow.

A low-frequency strategic decision probably does not need an agent at all.

4. Decide whether to build, buy, borrow or remain human-led

For each use case, consider:

• Use an AI feature in an existing platform.

• Add a specialist AI-native tool.

• Build a custom workflow or agent.

• Keep the process human-led.

• Combine several of these approaches.

Avoid assuming that building is always cheaper or that buying is always safer.

5. Supply the right context

An AI system may need:

• Brand guidance

• Audience definitions

• Product information

• Customer history

• Commercial rules

• Approved examples

• Performance targets

• Workflow instructions

• Escalation criteria

Do not expect the model to infer undocumented knowledge accurately.

6. Keep humans at the right decision points

Define:

• Which actions can happen automatically

• Which require review

• Which must always remain human-led

• What happens when confidence is low

• Who is accountable

• How errors can be reversed

7. Measure business outcomes

Do not treat prompts submitted, words generated or agents created as success metrics.

Measure the change in the underlying process.

8. Simplify regularly

AI functionality is moving quickly.

Review the stack at least quarterly and remove capabilities that have become:

• Duplicated

• Unused

• Absorbed elsewhere

• Unreliable

• Disconnected

• Poor value

The future is not necessarily a bigger martech stack

AI is expanding the marketing technology landscape while simultaneously undermining parts of the traditional SaaS model.

It is creating new tools.

It is making existing platforms more capable.

It is enabling businesses to build their own workflows.

It is turning applications into infrastructure that agents can use.

It is changing customer journeys from sequences controlled by marketers into conversations increasingly influenced by AI.

The short-term result may be more complexity.

The longer-term direction could be a smaller core of trusted systems surrounded by flexible AI workspaces, specialised agents and temporary workflows.

The businesses that benefit most will not necessarily be those using the largest number of AI products.

They will be those that:

• Understand their customers

• Organise their data

• Define their processes

• Choose worthwhile use cases

• Maintain human accountability

• Measure commercial value

• Remove unnecessary complexity

The central marketing technology question is therefore changing.

It is no longer only:

Which tools should be in our stack?

It is becoming:

What should people, platforms and AI each be responsible for—and how should they work together?

Frequently asked questions

What is AI martech?

AI martech is marketing technology that uses artificial intelligence to generate content, analyse data, personalise experiences, support decisions or carry out marketing tasks.

AI may be embedded in an existing platform, provided through an AI-native tool or introduced through a custom workflow or agent.

How is AI changing the martech landscape?

AI is changing martech in several ways:

• Established platforms are embedding AI functionality.

• New AI-native tools are targeting specific marketing tasks.

• Businesses can build custom workflows more easily.

• AI agents can operate across several platforms.

• Data, context, integration and governance are becoming more important.

• Some standalone tools are being replaced by broader AI platforms.

Will AI replace traditional martech platforms?

AI is unlikely to replace every martech platform.

Businesses will still need systems of record for customer, product, transaction and consent data.

However, AI may reduce the number of standalone tools required and change how people interact with the remaining platforms.

What is agentic marketing?

Agentic marketing involves using AI systems that can interpret an objective, retrieve information, select approved tools and perform several steps within a marketing workflow.

Most organisations should initially use supervised agents with clear permissions and human approval for significant actions.

What is the difference between marketing automation and an AI agent?

Marketing automation follows predefined rules.

An AI agent can interpret variable information, make a recommendation and choose between approved actions.

A standard confirmation email needs automation. Analysing a complex enquiry and recommending the appropriate response may justify an agent.

What does an AI-ready martech stack need?

An AI-ready marketing stack needs:

• Reliable systems of record

• Accessible and well-defined data

• Integration between platforms

• Documented business context

• Appropriate permissions

• Workflow orchestration

• Human approval points

• Monitoring and audit trails

Should a business buy an AI tool or build its own workflow?

It depends on the use case.

Use existing platform functionality where it adequately solves the problem.

Buy a specialist product where it provides a meaningful advantage.

Build a custom workflow where the process is specific to the business and requires proprietary context.

Retain human delivery where the work is strategic, sensitive or too infrequent to justify automation.

Can Rise help with AI and marketing technology strategy?

Yes.

Rise can help businesses:

• Audit their current martech stack

• Identify valuable AI use cases

• Select appropriate tools

• Improve marketing data

• Design integrations and automations

• Build supervised agentic workflows

• Establish practical governance

• Measure whether AI is producing genuine value

Learn more about our AI marketing consulting, strategy and automation services.

Build an AI-enabled marketing system without losing sight of the customer

AI can make marketing faster, more connected and more responsive.

It can also make weak processes run faster, poor data more influential and generic marketing easier to produce.

The difference is not the model or the number of tools in the stack.

It is the quality of the strategy, context, workflow and human judgement surrounding them.

Rise helps startups and growing businesses apply AI where it creates genuine value—without the unnecessary technology, inflated promises or hidden delivery models often associated with conventional agencies.

You work directly with the UK-based specialists supporting your business. You know who is doing what, how time is being used and why each recommendation has been made.

Because the future of martech should not be less transparent simply because more of the work is being completed by machines.

Get in touch to discuss how Rise can help you