Many hotels know their bookings. Few truly know their guests.

Most hotels today run on three core systems: a PMS to manage reservations, a POS to record consumption, and a handful of marketing tools to communicate with guests. Each one does its operational job well. None of them was ever designed to work together inside a single, unified data architecture.

As a result, the industry has spent years optimising its operations, but not its customer intelligence.

Three structural limitations 

This reality creates three structural limitations that hold hotels back. 

The first is guest data fragmentation. Customer interactions sit scattered across different systems: the booking in the PMS, the meal at the restaurant or bar recorded in the POS, and communications handled by marketing tools. Without a central layer to consolidate this information, building a complete and reliable guest profile becomes extremely difficult. The scale of the problem is well documented: nearly half of hotel professionals (49%) struggle to access the data they need for critical revenue and operational decisions, and 40% point to disconnected systems as their biggest obstacle. 

The second is dependence on OTAs. When hotels lack a deep understanding of the direct relationship with the guest, they end up relying on intermediaries to generate demand and repeat bookings. That dependence is significant and expensive. Online travel agencies accounted for 63.4% of bookings for independent hotels in 2025, climbing to 76.5% in EMEA, and analysts consider a property overdependent once OTAs exceed 60% of its bookings. The cost compounds the problem: OTAs charge 15–25% commission per booking, against roughly 4–5% for direct acquisition. 

The third is the absence of revenue intelligence. Many hotels concentrate on operational metrics such as occupancy or RevPAR. These matter, but they show only part of the picture. Without integrated customer data, hotels cannot measure what truly compounds over time: Guest Lifetime Value. Industry benchmarks place average guest lifetime value between $2,000 and $5,000, rising above $10,000 in the luxury segment — figures that dwarf any single-night RevPAR reading. 

The solution is architectural, not another tool 

The answer to this challenge is not one more piece of software. It is an architectural shift. 

Hotels need a Customer Intelligence layer within their technology ecosystem. This layer sits between the operational systems and the commercial strategy, aggregating and structuring guest data across the entire journey. That layer is the CRM. 

In a modern hospitality architecture, the PMS remains the system of record for reservations and the POS remains the system of record for transactions. The CRM takes on a different role: it becomes the system of intelligence for the guest — not a contact-management tool, but a revenue intelligence platform. This is precisely what platforms such as Salesforce for hospitality are designed to do: consolidate the view, identify value patterns and activate commercial journeys based on real behaviour. 

Three capabilities this unlocks 

When this architecture is in place, three powerful capabilities emerge. 

First, hotels gain unified guest profiles, consolidating information from every interaction across the customer journey. This matters because poor data quality is the single biggest barrier to personalisation: only 23% of guests report a high level of personalisation after recent hotel stays, even though 61% of consumers would spend more for a tailored experience. 

Second, they can identify predictive revenue opportunities, knowing when and how to present upgrades, experiences or additional services. Done well, personalisation typically drives a 10–15% revenue uplift, and returning guests tend to spend up to 67% more per stay than first-time visitors. 

Third, they can build data-driven loyalty strategies, increasing the repeat rate and strengthening the direct relationship with the guest. The economics are compelling: a 5% increase in retention can lift profitability by 25–95%. 

Where AI finally makes sense 

This is also the point at which AI — artificial intelligence — genuinely starts to make sense. 

AI does not create value on its own. Value appears when it can learn from structured data spanning the whole guest journey. When data is fragmented across multiple systems, AI has little to work with. When that same data is centralised and organised, AI can identify patterns, predict behaviour and unlock new revenue opportunities. 

From selling stays to selling Lifetime Value 

Hotels that master their guest data will not simply improve their operations. They will transform the way they generate revenue. 

Traditional hotels sell stays. 

Data-driven hotels sell Lifetime Value. 

Sources 

1. Revinate — The Future of Hotel Data Report (2025) 

2. Asian Hospitality — OTAs vs Direct Bookings for Independent Hotels (2025) 

3. Cloudbeds — OTA Commission Rates 

4. Sovereign Magazine — Hotels’ Marketing Spending Crisis (2025) 

5. Click Vision — Hotel Marketing Statistics (2026) 

6. Hospitality Technology — The Future of Personalised Hospitality Experiences (2025) 

7. McKinsey & Company — The Value of Getting Personalisation Right (or Wrong) 

8. eTip — Guest Retention Strategies 

9. Forbes / Bain & Company — Customer Retention and Profitability 

Nonprofit organizations operate in a space driven by purpose, not profit. But while the mission is often clear, the operational reality behind delivering that mission is often far more complex. From our experience working with nonprofit organizations, governance challenges rarely appear suddenly. They usually emerge gradually as funding grows, programs expand, and reporting requirements become more demanding.

When mission delivery outgrows operational structure

Many nonprofits begin with a strong mission and a small, highly committed team. In the early stages, operations are flexible. Information is shared informally, and data often lives in spreadsheets, emails, or disconnected systems.

This approach works well when managing a few programs or funding sources.

However, as organizations scale, complexity increases quickly:

• multiple grants with different reporting requirements
• both restricted and unrestricted funding streams
• growing expectations from donors and funding bodies
• increasing need for structured impact measurement

At this point, operational gaps begin to surface. Not because teams are underperforming, because the underlying structure was not designed for multi-grant, multi-program complexity.

Nonprofit governance challenges often start with fragmented funding and reporting

In the nonprofit sector, governance challenges are often closely linked to how financial and impact data is managed.

We frequently see difficulties in:

• tracking grant utilization across multiple programs
• consolidating donor reporting across different funding sources
• aligning internal data with external reporting obligations
• ensuring consistency in impact measurement frameworks

When each program or team manages its own data view, it becomes increasingly difficult to maintain a single, reliable picture of how funds are being used and what impact is being delivered.

Over time, this affects more than operational efficiency it directly impacts donor trust and stakeholder confidence.

How this typically works in practice

In real-world implementations, these challenges often become visible during digital transformation or AI initiatives. Consulting firms are frequently brought in when organizations attempt to modernize reporting or automate donor engagement processes, only to discover that the underlying data is fragmented or incomplete.At that point, the issue is rarely about the technology itself it is about data ownership, structure, and consistency across systems.

A real-world pattern we often see

In one recent project, an AI-driven assistant was introduced to support a nonprofit’s donor engagement and program reporting teams.

The goal was to improve responsiveness to donors and provide more personalized communication across multiple fundraising initiatives. However, during implementation, a critical issue emerged. Key data related to grant allocation and donor-funded program outcomes was not fully stored in Salesforce. Instead, it was distributed across external tools and manually maintained spreadsheets.

As a result, the AI agent was operating without full context.

It was not making incorrect decisions it simply lacked access to the complete dataset required to provide meaningful responses.

This quickly surfaced a broader governance question:

• Where is the source of truth for donor and grant data?
• How is impact data validated and maintained?
• Which system holds authoritative reporting information?

What initially appeared to be an AI limitation was, in reality, a data and governance structure issue.

How Salesforce helps address nonprofit governance challenges

Working as a Salesforce consulting company, we see how Salesforce can act as a central foundation for nonprofit operations when implemented correctly. In nonprofit environments, Salesforce is often more than a CRM it becomes a system that connects:

• fundraising and donor management
• grant tracking and allocation
• program delivery data
• impact measurement and reporting

By consolidating these areas into a single system of record, organizations can significantly improve visibility and consistency across operations.

Salesforce also enables:

• structured reporting aligned with donor and grant requirements
• clearer ownership of data across teams
• improved transparency for leadership and boards
• automation of repetitive reporting processes

This does not eliminate governance challenges on its own but it creates the foundation needed for governance to function effectively.

Why this matters for nonprofit organizations today? Nonprofit governance challenges in modern organizations

Nonprofit organizations are under increasing pressure from multiple directions:

• more complex and competitive grant funding environments
• higher expectations for impact transparency
• growing demand for real-time donor reporting
• increasing adoption of AI and automation in operations

In this environment, governance is closely tied to operational capability. Without structured data and clear systems, even well-designed processes struggle to scale. Organizations that invest in strong data foundations are better positioned to:

• demonstrate measurable impact
• maintain donor trust
• comply with funding requirements
• scale programs sustainably

Final thoughts

Governance in nonprofit organizations is not only about policies or oversight structures. It is also about how effectively data, funding, and impact information are managed in practice. Technology alone is not the solution. However, without the right systems in place particularly around data structure and integration governance quickly becomes difficult to sustain at scale.

Platforms like Salesforce can play a key role in bringing structure to fragmented environments, especially when dealing with multiple grants, complex funding models, and evolving reporting requirements.

Ultimately, the ability to connect mission delivery with structured, reliable data is what enables nonprofits to grow without losing clarity, accountability, or trust.

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Hotels that optimise only for RevPAR (Revenue Per Available Room) are answering the wrong question. 

For decades, RevPAR has been the central metric in hotel management. It is a useful measure. It combines occupancy with average daily rate and offers a quick snapshot of short-term performance. It is easy to compare across properties and simple to report in management meetings. 

The problem is not what RevPAR measures. The problem is what it does not measure. 

What RevPAR cannot see 

RevPAR measures revenue per available room within a given time window. It is a snapshot, not a film. Even expanded metrics such as TRevPAR or GOPPAR, while broader, remain property-level and time-bound. None of them tracks the value of the guest across stays. 

It does not distinguish between the guest who stayed for one night out of necessity and the guest who returns three times a year and spends on F&B, spa services and additional experiences. It does not differentiate between a customer acquired through an OTA, with a high acquisition cost and no real relationship, and a direct-booking customer with known preferences, booking history and a greater likelihood to recommend the hotel. It does not reveal which segment generates the most value over time. 

The economics are stark. OTAs charge 15–25% commission per booking, while direct acquisition costs sit around 4–5%. Returning guests spend up to 67% more per stay than first-time visitors and repeat bookings can account for 20–40% of total revenue in properties with mature retention programmes. A 5% increase in guest retention has been shown to lift profitability by 25–95%. Yet a hotel may have an excellent RevPAR while building a customer base with low value, high dependency on intermediaries and weak retention potential. The problem remains invisible. 

What Guest Lifetime Value is and why it changes everything 

Guest Lifetime Value does not measure a stay. It measures a relationship. Industry benchmarks place average guest lifetime value between $2,000 and $5,000, with luxury-segment guests often exceeding $10,000 — figures that dwarf any single-night RevPAR reading. 

It includes not only the direct revenue from all stays, but also spend on additional services, the acquisition channel and associated acquisition cost, return frequency and behavioural trends over time, as well as indirect impact through recommendations and influence over other customers. 

When a hotel organisation starts thinking in terms of Guest Lifetime Value, the way it makes commercial decisions changes. The old question is operational: how do I maximise tonight’s revenue? The new question is strategic: how do I build a relationship that becomes more valuable over time? The difference between the two is not semantic. It is structural. 

The link between data, CRM and revenue 

The impact is not only analytical. It is economic. 

Guest Lifetime Value is not just a concept. It is an operational capability. And like any operational capability, it requires an architecture to support it. 

To calculate and activate Guest Lifetime Value, a hotel needs integrated data across the entire guest journey. It needs to know who booked, how they arrived, what they consumed, when they returned and how they engaged. It needs to be able to connect that information in a structured way and turn it into concrete commercial decisions. 

That is exactly what CRM is for in a modern hospitality architecture. Not as a contact management tool, but as a revenue intelligence platform. Platforms such as Salesforce CRM are designed to consolidate this view, identify value patterns and activate commercial journeys based on real behaviour rather than generic segmentation. 

RevPAR still has its place in management reporting. But the strategic decisions of a hotel group focused on sustainable growth require a metric that measures the relationship, not just the night. 

A different management model  

The shift from RevPAR to Guest Lifetime Value is not simply a change in metrics. It is a change in management model. 

Hotels operating with this mindset stop selling stays. They start building relationships with measurable, recurring and growing value, while creating a revenue base that does not depend on each new acquisition. 

The question worth asking in the next revenue meeting is not what last month’s RevPAR was. It is: do we know what our best guests are worth, and are we doing anything with that knowledge? 

If the answer is no, the first step is not a new dashboard. It is a decision to measure the relationship, not just the room. Everything else follows from there. 

Sources

1. Cloudbeds — OTA Commission Rates (2026) 

2. Sovereign Magazine — Hotels’ Marketing Spending Crisis (2025) 

3. eTip — Guest Retention Strategies (BIA/Kelsey, 2026) 

4. Forbes / Bain & Company — Customer Retention and Profitability 

5. Click Vision — Hotel Marketing Statistics (2026) 

6. Emersion Wellness — Hotel Marketing Statistics (2025) 

In hospitality, the guest journey no longer starts at check-in and ends at check-out. It begins with a search or a social post, moves through booking engines and OTAs, and continues across emails, WhatsApp, apps, front-desk conversations and online reviews. 

At the same time, hotels and tourism brands face tighter margins, staffing challenges and guests who expect both high-touch service and frictionless digital. Recent hospitality reports highlight the same forces shaping 2025–2026: AI-powered personalisation, contactless journeys, sustainability and wellness-driven stays. 

This is where Salesforce for hospitality stops being “just a CRM” and becomes the platform for orchestrating the full guest journey. Salesforce itself describes its hospitality CRM as a way to unify data, connect teams and leverage AI to transform guest experiences. 

1. Beyond bookings: why hospitality needs a connected CRM 

Most hospitality businesses already run a PMS, a booking engine and several point solutions. The real problem is what sits between them: 

• Marketing does not see full stay and spend history. 

• Sales works in spreadsheets for groups and corporate. 

• Operations only see guest sentiment when a complaint escalates. 

Specialist partners working with Salesforce in hospitality consistently point to the same issues: fragmented systems, duplicated data and manual work across sales, reservations and guest relations. 

A hospitality CRM does not replace PMS or revenue management. It sits above them as a guest-centric layer: 

• One place to see bookings, preferences, loyalty, enquiries and cases. 

• One set of processes for sales, marketing, operations and service. 

• One platform where automation and AI can safely act on trusted data. 

2. How Salesforce transforms the guest journey 

Personalised marketing and direct bookings 

With Salesforce Marketing Cloud, hospitality brands move from generic blasts to journeys grounded in real data: 

• Segment guests by purpose of travel, value, channel preference and stay patterns. 

• Trigger pre-stay flows with dynamic content for business, family, leisure or event stays. 

• Offer targeted upsells, such as late check-out, spa services and experiences, based on what guests actually used last time. 

• Run win-back journeys when high-value guests go quiet. 

Because these journeys sit on top of unified profiles in Salesforce and Data 360, they can respond to real-time events such as cancellations, new reviews or loyalty status changes. 

Service and feedback on one platform 

With Service Cloud, every email, form submission or social message becomes a case with an owner and SLA. Features like email-to-case and AI-assisted replies reduce response times and standardise quality, even with lean teams. 

Self-service is handled through Experience Cloud: 

• Guests can manage bookings, preferences and requests online. 

• Loyalty members can see their benefits, vouchers and history. 

• Invoices and documents are accessible without contacting the front desk. 

Feedback and complaints no longer disappear into inboxes. They feed into CRM Analytics dashboards where patterns in satisfaction, NPS and issues become visible by property, segment or channel. 

Automation and smarter operations 

Behind the scenes, Salesforce helps hospitality teams take repetitive work off the table: 

• Group and corporate sales: lead capture, proposals, approvals and follow-ups can run through guided processes rather than ad hoc email chains. 

• Einstein Activity Capture reduces manual data entry by logging emails and meetings against the right guests or accounts. 

• Flows and alerts notify teams about VIP arrivals, high-value cancellations or at-risk corporate accounts in time to act. 

Specialist hospitality consultancies working with Salesforce report better pipeline visibility, faster response times and higher upsell revenue once these processes move into CRM. 

Agentforce: a 24/7 digital concierge 

On top of this, Agentforce can act as a 24/7 digital concierge with a 360-degree view of each guest. Grounded in centralised Customer 360 and Data 360 data, it understands the guest’s profile, preferences and history, and can tailor conversations, recommendations and actions in real time across channels. 

3. Sustainability, AI and data-driven decisions 

Sustainability has moved from “nice story” to a core decision factor for many guests and investors. Hospitality trend reports for 2025–2026 highlight eco-friendly travel, reduced food waste and smart resource management as key themes. 

Salesforce can support this in practical ways: 

• Track energy, water and waste metrics alongside revenue and occupancy in CRM Analytics. 

• Use guest data to design campaigns around low-impact stays, local partnerships or “green” packages. 

• Integrate with AI-driven tools that monitor food waste or resource usage and surface insights directly in Salesforce dashboards. 

When sustainability lives in the same data model as guest and commercial metrics, it becomes part of everyday decision-making rather than a separate report. 

4. How Target Everest supports hospitality teams 

At Target Everest, we work with hospitality and tourism clients who want Salesforce to act as a true guest-journey and operations platform, not just a contact database. Our hospitality work has included end-to-end Salesforce implementations for hotel groups, from sales and reservations to document generation and reporting. 

Across these projects, our role is to: 

• Map the guest lifecycle and decide which touchpoints should live in Salesforce. 

• Integrate PMS, booking engines, channel managers and finance so teams share a single source of truth. 

• Configure Sales, Service, Marketing and Experience Cloud to fit real operations, not just ideal diagrams. 

• Introduce automation and AI with clear governance, so teams feel supported, not replaced. 

The outcome we aim for is simple: 

• Guests experience coherent, personalised journeys. 

• Teams gain clarity and time, rather than another tool to manage. 

• Leadership sees data they can trust to make commercial and sustainability decisions. 

If you operate in hospitality and want to explore what Salesforce for hospitality could look like in your world, from group sales and reservations to guest experience and sustainability, this is the right moment to move from scattered tools to a connected, data-driven guest journey.

5 mistakes companies make when introducing AI agents in Salesforce

Many companies are excited about Agentforce, but this is exactly why Agentforce projects fail if the organisation is not prepared. Companies expect immediate improvements in efficiency, productivity, and customer experience. Yet the biggest challenge is rarely the technology itself; it’s the readiness of the organization. In practice, AI rarely fails because the model is weak. It fails because the company asks it to work inside unclear processes, fragmented data, and systems that were never properly connected.

We often see Agentforce amplify both the strengths and weaknesses of an existing Salesforce environment. When the foundation is weak, AI doesn’t solve the problem, it exposes it.

Mistake #1: Why Agentforce projects fail when companies start with the tool instead of the business objective

One of the the most common mistakes is implementing Agentforce simply because it is new and widely discussed.

We recently worked with a company that wanted to launch Agentforce as quickly as possible because their competitors were already talking about AI.

When we asked what they expected it to do, the answers were vague:

• “We want to use AI in Salesforce.”
• “We want to automate something.”
• “We want to be more efficient.”

But there was no specific business objective behind the project.

It turned out the real issue had nothing to do with “using AI” in general. Their sales team was spending hours every week manually qualifying leads and writing follow-up emails. 

Instead of launching a broad AI initiative, we focused on one concrete goal: reduce the time spent on lead qualification by 30%.

Only then did Agentforce become useful.

Before implementing AI, companies should first ask:

• Do we want to reduce response times?
• Do we want to improve lead qualification?
• Do we want to eliminate repetitive work?

Without a clear objective, even the most advanced AI will struggle to deliver measurable value.

Mistake #2: Why Agentforce projects fail when Salesforce data is poor

Agentforce depends entirely on the quality of the information stored in Salesforce. Duplicate, incomplete, or inconsistent records prevent the AI agent from providing reliable recommendations.

In one recent project, an AI agent was expected to support a customer service team. 

The goal was simple: help employees answer customer questions faster. 

The problem was that key customer information wasn’t stored in Salesforce at all. Previous interactions were hidden in email inboxes, complaint history was stored in Excel, and contract details lived in another system. 

The agent wasn’t “wrong”,  it simply didn’t have enough context to be useful.

As a result, employees quickly stopped trusting the recommendations because the answers felt incomplete or generic.

Typical data issues we see include:

• Missing customer or donor history
• Duplicate records
• Different versions of the same data across systems
• Unclear ownership of information

Many companies only realize these issues exist when an AI initiative exposes them.

By that point, the project is often delayed, and internal trust is already starting to disappear.

Mistake #3: Why Agentforce projects fail when companies try to automate everything at once

Many organisations try to transform multiple departments at the same time. Sales, customer service, marketing, reporting, everyone wants to be included from day one.

One company we worked with wanted Agentforce to:

• Answer customer emails
• Qualify leads
• Prepare reports
• Generate meeting summaries
• Support internal HR requests

All in the first phase.

The result? Nobody really understood what the project was supposed to do, deadlines kept moving, and users became frustrated.

Instead, we recommended starting with one small but visible use case: automatically summarising support cases before they were assigned to an agent.

Within two weeks, the team could already see the benefit. Response times improved, employees saved time, and trust in the solution grew naturally.

A more effective approach is to start small. For example, Agentforce can first be used to:

• Summarise support cases
• Draft follow-up emails
• Create meeting summaries
• Support repetitive internal tasks

Once users gain confidence, additional use cases can be added gradually.

Mistake #4: Forgetting governance and security

Employees need clarity on:

• What decisions Agentforce can make
• Where human approval is still required
• Who is responsible for the final outcome

We once saw a project where an AI agent was allowed to recommend discounts for customers. Technically, everything worked correctly, but nobody had defined the limits. 

After a few weeks, the system started suggesting discounts that were far outside the company’s pricing policy. Employees didn’t know whether they were allowed to approve them, and managers blamed the tool.

The real issue wasn’t the AI. The issue was that nobody had established rules, ownership, or approval processes.

At Target Everest, we see AI as an extension of enterprise architecture. That means governance and security must come first.

Projects that overlook these fundamentals quickly lose user trust and fail to scale.

Mistake #5: Expecting employees to trust the system immediately

Even the best AI solution will fail if employees don’t trust it.

In one project, the company launched Agentforce across the entire support team overnight. The tool worked technically, but employees kept ignoring its recommendations. The tool worked technically, but employees kept ignoring its recommendations.

When we asked why, the answer was simple:

“They don’t understand where the answer comes from.”

People need time to understand:

• How Agentforce works
• Why it made a recommendation
• How they can use it in their daily work

The companies that succeed introduce AI step by step. They involve employees early, test the solution with a small group, and communicate openly about both the benefits and the limitations.

Trust is built gradually, not automatically.

Final thoughts

The companies that succeed with Agentforce are not necessarily those with the biggest budgets. They are the ones that:

• Prepare their Salesforce environment
• Define a realistic business goal
• Introduce AI gradually
• Create strong governance from the beginning

We have learned one thing repeatedly: AI will reveal both your strengths and weaknesses.

Prepare the foundation first, and only then expect the technology to deliver real value.

Before launching Agentforce, ask yourself one simple question: Are we solving a real business problem, or are we simply following the AI trend?

You can learn more about Agentforce on the official Salesforce website. Explore more articles on our blog!

Artificial intelligence on Salesforce governance is becoming a critical topic as organisations accelerate enterprise AI adoption across the Salesforce platform.

It is evolving at an unprecedented speed. New tools, models and capabilities appear almost daily, promising efficiency, automation and smarter decision making. Yet from our experience as a Salesforce Partner and consulting firm, we see a different reality emerging. As organisations rush to adopt AI, many discover that the real challenge is not the technology itself. AI does not fail because it is too advanced. It fails because the basics are not ready.
AI does not only create new opportunities. It exposes unresolved issues in security, data ownership and responsibility. In many organisations, AI becomes the moment of truth. It reveals whether the foundations are strong enough to support innovation at scale.az eglsz

When Progress Demands Stronger Foundations

Strong AI on Salesforce governance ensures that security, data ownership and responsibility are clearly defined before AI is scaled.

AI significantly raises the stakes for security. Modern AI solutions rely on large volumes of data that move across systems, teams and platforms. When governance is weak, AI does not hide the problem. It amplifies it.

As a Salesforce Partner, we see AI magnify both the strengths and weaknesses of existing Salesforce architectures.Well designed platforms become powerful enablers of AI driven processes. Poorly governed environments, on the other hand, turn AI into a risk rather than a benefit. This is why organisations implementing AI on Salesforce are returning to core security principles. Clear access management, strict data ownership, auditability and accountability are no longer “nice to have”. They are prerequisites. Without them, AI cannot be trusted, scaled or safely adopted.

Just Because We Can, Should We?

AI thrives on data sharing. Integrated platforms and connected systems promise deeper insights and smarter automation. But the ability to share data does not automatically mean it should be shared freely.

In our consulting work, we are frequently brought in when AI initiatives expose gaps in data ownership, security or responsibility. Questions such as who owns the data, who can access it and under what conditions it can be reused suddenly become critical. These questions were always important. AI simply makes ignoring them impossible. Organisations that succeed with AI move away from uncontrolled sharing. They design purpose driven data flows, clear governance models and transparent rules. What once felt like bureaucracy becomes the foundation that makes AI usable, scalable and trustworthy.

Redefining Roles and Responsibility

One of the most underestimated challenges of AI is responsibility. As AI systems start supporting recommendations, predictions and automation, someone still needs to be accountable. AI can analyse and suggest, but it does not decide. People do. This forces organisations to clearly define roles, permissions and escalation paths. Who can rely on AI outputs? Who can override them?

At Target Everest, we treat AI as an extension of enterprise architecture. That means governance and security come first. Without clear ownership and responsibility, AI driven processes quickly become opaque, risky and difficult to control.

AI as a Mirror, Not a Shortcut

There is a persistent myth that AI can fix structural problems. In reality, AI acts as a mirror. It reflects existing weaknesses in data quality, governance and organisational design.

Organisations with strong foundations accelerate faster. Those with fragmented systems and unclear rules struggle. Not because AI does not work, but because it exposes what was already broken. AI is not a shortcut to maturity. It is a stress test.

Progress Through Fundamentals

The real power of AI is not in complexity, but in discipline. By demanding stronger security, clearer data sharing models and well defined responsibilities, AI brings organisations back to the fundamentals of good architecture and governance.

The future of AI on platforms like Salesforce is not about experimenting harder. It is about building smarter. If you are considering AI on Salesforce and are unsure whether your foundations are ready, this is exactly the moment to pause and ask the right questions.

At Target Everest, we help organisations implement Salesforce governance and AI solutions that turn ambition into a secure, scalable reality.

Because when the basics are right, AI finally delivers on its promise.

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Salesforce is entering a new chapter in 2026. 

Over this year, three movements will reshape how teams work on the platform: 

• The rise of the Agentic Enterprise, led by Agentforce 360.  

• Data 360 as the strategic data layer for AI and agents, evolving from Data Cloud. 

• Slack positioned as an agentic OS, the workspace where people, data and AI agents meet.  

In parallel, the EU AI Act moves from theory to enforcement, with high-risk AI systems expected to be compliant by 2 August 2026. 

For many organisations, this mix of opportunity and regulation feels exciting and slightly uncomfortable. The goal of this article is simple: show what is really changing, where Agentforce fits in your business, and how to move forward with structure. 

1. What is really changing in 2026? 

Salesforce’s message is clear: AI does not replace people. It elevates them, through agents that understand goals, call the right tools and act across systems.  

Three pillars support this vision: 

• Agentforce & Agentforce 360 
  The platform to design, deploy and monitor AI agents across Sales, Service, Marketing and custom apps, all grounded in Salesforce data and security. 

• Data 360 as the foundation 
  Salesforce has formally rebranded Data Cloud to Data 360, emphasising a unified, AI-ready view of customers across channels and systems.  

• Slack as the agentic workspace 
  Slack is being reframed as your “agentic OS”: a place where channel-based experts, a rebuilt Slackbot and Enterprise Search connect conversations, data and agents.  

On the regulatory side, guidance from the European Parliament and analysis from organisations such as Trilateral Research confirm a phased EU AI Act timeline: prohibited practices from 2025, rules for general-purpose AI in 2025, and full obligations for high-risk systems from August 2026.  

Put together, 2026 is not “just another release”. It is a shift from static processes to human + agent collaboration, under clearer expectations of transparency, risk management and human oversight. 

2. Why many Salesforce teams feel unprepared 

Across Salesforce, IT and business leaders, the same patterns keep appearing: 

• Fragmented data 
  Information is spread across multiple orgs, legacy tools and spreadsheets. Agents cannot make good decisions without a complete, consistent view. 

• Brittle processes 
  Years of urgent requirements have produced flows, triggers and workarounds that nobody fully owns. Letting agents act on top of this feels risky. 

• AI as a side project 
  There are pilots in sales or service, but no shared architecture or risk model. Wins are hard to scale; failures are hard to learn from. 

• Regulation that feels abstract 
  The EU AI Act is on the radar, but it is not yet mapped to concrete use cases in Salesforce, Slack or Agentforce. 

The result: impressive demos, but a daily reality still dominated by manual work and exports. 

3. Where Agentforce fits in your business 

To make this tangible, it helps to look at practical Agentforce scenarios – some internal, some customer-facing. 

3.1 Agentforce for internal teams 

Sales – Opportunity Assistant (Sales Cloud) 

• Reviews open opportunities (stage, history, activities, emails). 

• Flags risks such as stalled deals or missing decision-makers. 

• Suggests next best actions directly in the opportunity or via Slack. 

Example: 
“This opportunity has been in Proposal for 14 days without a reply. Suggest following up with decision-maker X and attaching document Y.” 

Value: fewer forgotten deals, better forecasts, higher win rates. 

Finance – Contract Validation (Sales + Service + Data 360) 

• Reads contracts in PDFs, attachments and email chains. 

• Checks terms, penalties and amounts against your standards. 

• Alerts finance or legal when something is outside the norm. 

Example: 
“Contract with customer ABC has a 36-month term instead of the standard 24. Requires validation.” 

Value: reduced contractual risk, less manual review, stronger compliance. 

Support – Case Triage (Service Cloud) 

• Analyses case descriptions, emails and customer history. 

• Classifies type, urgency and impact. 

• Routes to the right team or proposes an initial response. 

Value: faster response times, fewer reassignments, better agent experience. 

3.2 Agentforce for customer experience 

Omnichannel Service (Service Cloud + Chat/WhatsApp) 

• Handles conversations on chat, WhatsApp or web. 

• Uses full customer history to personalise answers. 

• Resolves simple requests or hands off to humans with full context. 

Value: quicker answers, less friction, consistent experience across channels. 

Real-Time Marketing Personalisation (Marketing Cloud + Data 360) 

• Reads behaviour (opens, clicks, browsing, purchases). 

• Chooses the best message, channel and timing. 

• Adjusts journeys in real time. 

Example: 
Customer ignores several emails → the agent pauses email and recommends WhatsApp or a call. 

Value: less noise, more relevance, better conversion. 

Behind all this, Agentforce respects Salesforce permissions; actions are logged; and humans stay in control where it matters – a natural fit with EU AI Act principles such as human-in-the-loop and traceability.  

4. A practical Salesforce roadmap for 2026 

You do not need a perfect architecture to start. You do need a clear sequence: 

1. Map AI use cases to risk, not just to features 
   Catalogue where you use or plan to use AI and agents. Classify each use case by business impact and EU AI Act risk tier, especially anything that affects access to services, credit, employment or public decisions.  

2. Build a focused Data 360 core 
   Start with the journeys that matter most (for example, lead → opportunity → contract → onboarding → support). Bring those objects and events into Data 360 and align access, masking and retention with your existing Salesforce governance.  

3. Design agentic workflows with guardrails 
   Give each agent a clear “job description”: what it can do, which tools it may call, when it must hand off and how its actions are logged. 

4. Prepare people for Slack as the AI hub 
   Standardise channel usage, train teams to work with Slack AI features, and involve legal and security early so Slack policies reflect how people actually collaborate.  

If you are looking at 2026 and wondering how to move from “CRM with AI features” to a genuine agentic enterprise, this is the moment to set the foundations – with clarity, discipline and a bit of ambition. Let’s talk! 

Salesforce has moved fast into AI. Einstein 1, Data Cloud, Agentforce, Slack AI, new releases every few months. 

If you are responsible for revenue, operations or technology, it may feel exciting and overwhelming at the same time. Licences are getting more powerful, but your reality may still be dashboards exported to Excel, manual handoffs and AI pilots that never quite go live. 

This article cuts through the noise and focuses on what has really changed in Salesforce, where most organisations get stuck, and how to start unlocking value in a realistic way. 

1. Salesforce is now an AI and data platform, not “just CRM” 

The biggest shift is strategic. Salesforce is repositioning Customer 360 around Einstein 1 and Data Cloud, so your CRM becomes a real time data and AI platform, not only a system of record. 

Data Cloud sits at the centre. It connects data from your Salesforce orgs and from external systems, builds unified customer profiles and makes that context available to AI, analytics and automation. If AI is the brain, Data Cloud is the nervous system. 

On top of that, Salesforce has introduced Agentforce. This is an AI helper and agent embedded inside Sales, Service and other clouds. It can summarise records, suggest next actions, generate content and trigger Flows from natural language, without forcing your teams to learn a new tool. 

The message is clear. Salesforce is no longer adding AI as a side feature. It is rebuilding the platform around AI and data. 

2. Why most organisations are not seeing the value yet 

When we talk to leaders, three pain points appear again and again. 

Fragmented data 
Customer interactions, contracts, product usage and finance data live in different systems, owned by different teams. AI cannot make good decisions if it does not see the full picture or if basic fields are incomplete and inconsistent. 

Complex, fragile processes 
Years of urgent requirements have created layers of flows, rules and workarounds. Nobody feels comfortable touching them. New AI features are dropped on top of this, which makes everything feel more unpredictable instead of more intelligent. 

Lack of AI governance 
Everyone wants automation. Nobody wants a compliance issue or a bad customer experience. If there are no agreed guardrails, legal and security teams slow things down, often for good reasons, and AI projects stay in pilot mode. 

The result is a familiar pattern. The organisation pays for powerful Salesforce capabilities, but day to day, teams still rely on manual work and side spreadsheets. 

3. What to focus on now 

You do not need to implement every new feature to benefit from this new Salesforce wave. You need a sequence. 

Start with the data that matters most 
Pick one or two high-value slices of data, such as customer profiles and recent interactions, and focus on making them clean, connected and available in Data Cloud. The goal is not perfection. The goal is an initial, trusted foundation that AI can use. 

Choose a small number of focused AI use cases 
Instead of “doing AI everywhere”, pick one or two scenarios with clear outcomes. For example, inbound lead qualification, first-line support for a defined set of cases, or an internal knowledge assistant for your sales or service team. Design them so that humans stay in the loop, especially at the start. 

Design governance from day one 
Agree what AI agents can and cannot do. Decide where a human must approve actions, what is logged, and how issues will be reviewed. This makes legal and security part of the solution, not blockers at the end. 

Invest in people, not only in licences 
AI features will keep evolving. Your real advantage is people who know how to work with them. Train admins, architects and business owners on how to design prompts, flows and guardrails. Help users understand when to trust AI, when to question it and how to give feedback. 

4. How Target Everest fits into this picture 

If you have not come across us before, we are a specialist consultancy that works at the intersection of Salesforce architecture, data, AI and strategy. We focus on turning platforms into performance, not just technology into licences. 

In practice, that means we help organisations: 

• Analyse their current Salesforce landscape and identify where data and processes block AI. 

• Design an architecture that makes sensible use of Einstein 1, Data Cloud, Agentforce and Slack AI, without over-complicating things. 

• Prioritise a small number of AI use cases that map directly to revenue, service or efficiency outcomes. 

• Run safe pilots and then scale what works, with governance and change management built in. 

The outcome is simple. Fewer disconnected pilots. More visible value, faster, from the Salesforce investments you already have and the AI capabilities that are arriving. 

If you recognise these pain points and would like to explore what an AI-ready Salesforce roadmap could look like for your organisation, we are always open to a conversation. 

Artificial intelligence is transforming the Salesforce ecosystem faster than ever.
But have you ever wondered how these smart systems actually connect with data, apps, and workflows?

The answer lies in something new and something everyone in the Salesforce world will soon be thinking about: the Model Context Protocol.

What Is the Model Context Protocol?

The Model Context Protocol (MCP) is an open standard designed to make AI connections simple, secure, and consistent.
Think of it as the USB-C of AI: one universal connector that lets different AI systems speak the same language when interacting with data and tools.

In the context of Salesforce, the MCP allows AI models to securely access information from multiple systems without complex custom integrations.
It defines a clear, structured way for AI to find the right data, understand its context, and perform actions when needed.In short, MCP builds a standard bridge between AI and Salesforce data – helping automation become more intelligent and more connected.

Why the Model Context Protocol in Salesforce Matters

Salesforce has already started supporting the MCP within its new AI framework, Agentforce.
This means that intelligent agents inside Salesforce can now:

• Retrieve data from Sales, Service, or Marketing Cloud
• Connect to external apps and databases through a secure “interface bridge”
• Trigger Salesforce automations like Flows, Approvals, or Apex actions

For Salesforce admins and AI enthusiasts, this is a big deal.
It’s not just about asking AI to summarize data anymore – soon, AI will be able to take action directly inside your org, based on real-time context.

How It Works (Without the Jargon)

Every MCP setup has two main parts:

• the MCP client, which is the AI system asking for information (for example, Agentforce or another AI assistant), and
• the MCP server, which provides access to the right data or actions.

When they connect, the client asks, “What resources or tools can I use?”
The server then replies with a list of available options – such as datasets, APIs, or commands.

From there, the AI can execute actions in Salesforce safely, without the need to hard-code every integration.
It’s all done through a standardized, secure framework, which makes development faster and governance easier.

Why Everyone’s Thinking About MCP

Even though MCP is still new, it’s already being called the future “glue” of AI integration.
Salesforce’s decision to adopt it shows a clear direction: a world where AI agents are open, interoperable, and context-aware.

For Salesforce professionals, learning about the MCP now is like getting an early look at how AI and CRM will merge over the next few years.
It’s the foundation for smarter, more connected workflows and the kind of automation that truly feels intelligent.

Built with Trust and Security in Mind

Security has always been part of Salesforce’s DNA, and the MCP follows the same principle.
It uses standard Salesforce authentication and permissions, meaning AI agents can only access what their user or app is allowed to.
No shortcuts, no open back doors – just governed, scalable access to data.

Looking Ahead

The MCP in Salesforce is still evolving, but its direction is clear.
In the near future, AI assistants will become real teammates – not just answering questions, but completing tasks, managing workflows, and learning from context.

This shift will redefine what efficiency means for Salesforce teams.
And those who understand MCP early will be ready for what’s coming next.

Final Thought

At Target Everest, we see the MCP as more than just another technical update.
It’s a symbol of connection, linking people, data, and intelligent systems in a smarter way.

MCP marks the beginning of a new chapter where AI and Salesforce work hand in hand, creating smarter, more connected business solutions.

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In today’s competitive business, email marketing is no longer just about mass sends. To achieve real results, such as increased conversions, customer loyalty, and lasting engagement, brands need to invest in personalised journeys, relevant content, and artificial intelligence. With Salesforce Marketing Cloud, all of this becomes scalable and automated. 

In this article, we’ll explore how to build personalised email journeys using Marketing Cloud, focusing on strategic segmentation, AI-driven personalisation with Einstein, and optimisation through A/B testing and best send times. 

1. The Importance of Personalised Journeys 

Before getting technical, it’s worth highlighting the value of personalisation: it puts the customer at the centre. Instead of sending the same message to everyone, you build a sequence of communications tailored to the behaviour, preferences, and funnel stage of each contact. 

Example: a customer who clicks on a “New Arrivals” email can be automatically placed in a follow-up journey with similar products, while someone who hasn’t opened any emails recently may receive a re-engagement campaign with a special incentive. 

This kind of experience is exactly what increases click-through rates, conversions, and customer retention. 

2. How to Segment Lists for More Effective Campaigns 

Segmentation is the foundation of personalisation. In Marketing Cloud, you can segment using Data Extensions, Audiences, and SQL Queries, with filters based on: 

• Demographic data (age, location, gender) 

• Behavioural data (clicks, opens, purchase history) 

• Declarative data (preferences, signup forms) 

• Engagement scores 

Additionally, the Audience Builder allows you to create visual segments with cross-filter criteria, for example: “customers who purchased in the last 2 weeks + didn’t open the last 2 emails.” 

? Pro tip: Combine simple segments with Contact Builder attributes to create dynamic audiences. E.g.: customers with more than 3 interactions in the past 7 days + declared interest in “technology”. 

3. Personalisation with Einstein AI 

Einstein’s artificial intelligence within Marketing Cloud allows you to automate and scale personalisation in a smart way. Here are some features you can use directly within email journeys: 

Einstein Email Recommendations 

Suggests personalised products or content based on browsing behaviour, past purchases, or engagement history. 

Einstein Send Time Optimisation (STO) 

Analyses each contact’s open behaviour to determine the best individual send time. This means a single email campaign can be delivered at different times for each person, maximising the chance of being opened. 

Einstein Engagement Scoring 

Classifies contacts into groups (Engaged, Disengaged, etc.), enabling automated decisions like: “If the person is highly engaged, send a direct CTA version; if less engaged, use a softer approach.” 

? Pro tip: Use Personalisation Strings and AMPscript to dynamically display details in the email body, such as the person’s name, location, or last product viewed. 

4. Best Time to Send Emails and A/B Testing 

Time Optimisation with Einstein STO 

As mentioned, Send Time Optimisation helps move away from the “send to all at 10 am on a Tuesday” approach. With STO enabled, Marketing Cloud calculates the best possible time to send for each contact, based on past behaviour. 

Effective A/B Testing 

It’s not just about testing subject lines. Marketing Cloud enables A/B testing for: 

• Subject line 

• Sender name 

• Email content (version A with image X, version B with image Y) 

• CTA (Call to Action) 

• Send time 

• Different layouts 

And the best part: you can define a test audience (e.g. 10%), and the winning version (based on open rate, click rate, or conversion) is automatically sent to the remainder of the list. 

? Pro tip: Always document your A/B test results and use them to guide future campaigns. A consistent history of testing supports data-driven improvements to your email performance. 

5. Building Personalized Journeys with Journey Builder 

The Journey Builder is the core of personalised journeys in Marketing Cloud. It allows you to map every step of the customer’s experience and apply logic based on data and behaviour. 

Key Components: 

• Entry Source: the trigger point (e.g. a new subscriber list) 

• Wait Time: defines timing between steps (e.g. wait 3 days before the next email) 

• Decision Split: conditional branches (e.g. “did they open the last email?”) 

• Email Activity: sends a specific email in each step 

• Einstein Split: AI-based decision logic 

• Salesforce Data Events: connect with other Salesforce objects like Leads, Opportunities, or Cases 

Real-world example: A welcome journey might include 3 emails over 9 days. If the user opens the second email and clicks “View Products”, they can be redirected to a conversion-focused journey. 

6. Extra Tips for Smart Email Campaigns 

• Avoid over-sending. Set channel frequency limits to prevent inbox fatigue. 

• Keep your database clean. Regularly remove inactive or invalid contacts. 

• Prioritise mobile design. Most emails are opened on smartphones, ensure your layout is responsive. 

• Integrate other channels. Combine your email journeys with SMS, push notifications, and what’s app.

Conclusion 

Creating effective email marketing campaigns in Marketing Cloud goes far beyond designing a nice-looking message. It’s about using data, automation, and artificial intelligence to deliver the right message, to the right person, at the right time. 

Smart segmentation, AI personalisation with Einstein, strategic A/B testing, and automated journeys form the foundation of truly intelligent, results-oriented email marketing. 

With these best practices in place, you’ll not only improve campaign ROI but also strengthen your long-term relationship with customers. 

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