Sales reps spend 70% of their time on nonselling tasks, according to a 2024 report from Salesforce. This includes time-intensive manual work like data entry, internal meetings, and admin.

For sales leaders weighing up how humans can focus on discovery, relationships, and negotiation – the case for sales automation is clear.

What is sales automation (and why now)?

CX Today defines this technology as solutions to assist and automate sales tasks, admin, and workflows.

Sometimes referred to Sales Force Automation (SFA), this software looks to maximise efficiency while keeping manual efforts to a minimum.

The impact of this can be huge. Gartner predicts AI-powered SFA could cut meeting preparation time by 50% within two years.

Where automation reduces repetitive workload

1. Automatic data capture and CRM hygiene

Manual logging is a morale killer – and can lead to human error lowering data quality.

Contemporary SFA systems auto-capture emails, meetings, call notes to ensure that all relevant data is present. It can then enrich these contacts automatically, so sales reps aren’t spending all day copying fields between systems.

The payoff isn’t just time saved; it’s more reliable pipeline data for managers and finance. This means sales reps can spend more time selling, while creating a clean paper trail for the rest of the team to build upon.

2. Smarter lead and account prioritisation

If everything is a priority, nothing is. Predictive models rank accounts by likelihood to convert based on engagement signals and historical patterns.

With this automation-created intelligence, sales reps know their next best action without having to think about.

When prioritisation is automated, teams spend less time guessing and more time dedicated to where it will make the most difference.

Beyond just the sales rep, this process will also help managers to distribute sales meetings in the fairest way. This will help boost morale and give each rep a consistent chance to earn their commission bonus.

3. Guided outreach and content automation

Sequencing tools can now generate drafts, personalise at scale, and schedule multi-step cadences – allowing sales reps to make a good first impression and beyond.

With AI refining tone, subject lines, and message length for each persona, the humans don’t have to deal with a repetitive copy-and-paste grind. This creates more engaging content and leaves more time for the humans to seal the deal in conversations.

This can be an especially powerful tool for new members of a sales team. It enables them to skip the tedious onboarding process and immediately start creating brand-safe messaging.

Helping people do the work only people can do

With SFA at their side, sales leaders and decision makers in the buying committee can expect to see 3 key rewards from their purchase:

• Time reallocation: With automation capturing activity and drafting first drafts, teams can claw back hours for customer work.
• Higher conversion rates: Account/lead scoring puts energy on high-yield targets while AI-assisted cadences lift reply rates without manual rewriting.
• Cleaner data: Automated hygiene improves forecast quality and reduces last-minute admin, giving managers more bandwidth for training.

By stripping out repetitive steps from the sales workflow, automation can surface what matters most.

The salesperson of the future will have the bandwidth to build trust, shape deals, and close business without having to worry about repetitive manual tasks.

To find out more about how your sales team can stay ahead in a competitive landscape, read CX Today’s Ultimate Guide to Sales & Marketing Technology.

Amidst this pressure, there are opportunities for AI to boost these metrics and help marketers to prove ROI.

For tech buyers in evaluation mode, it’s critical to recognize which marketing KPIs they’ll be judged on, and which tools can improve those metrics.

Follow the Money: Connecting Every Click to Revenue

What it is: This metric looks to measure the qualified business opportunities that have either been sourced or influenced by the work of the marketing team.

Teams measure this by tracking engaged potential customers and the touchpoints they’ve interacted with, whether its demos, landing pages, chatbots, or newsletters, for example.

With this information, CMOs and the board can assess which channels are the most valuable. This allows for resources to then be allocated appropriately.

How AI helps: With its capabilities for advanced data analysis, AI can stitch every meaningful touch back to the account. Whether it’s an ad click, webinar, email, or site visit.

In the past, attribution models prioritized a customer’s first touch (the first interaction they have with your brand) or the last touch (the final step before conversion).

However, AI software can now see the entire complex multi-touch journey of the modern customer. In practice, that means less spreadsheet wrangling and a defensible, CFO-ready number tied to the CRM.

Filtering only successful conversions helps eliminate vanity metrics and reward channels that genuinely drive results. And AI can keep all of this shared data clean automatically, reducing errors and letting sales reps sell instead of sorting through systems.

Predictive AI: Prioritizing the Prospects That Matter

What it is: Marketing teams are often measured on how well they combine with the downstream sales reps. This is usually done by looking at the quality of leads they pass to Sales, and the speed (Lead Velocity Rate) that they do this.

These metrics let decision-makers predict revenue and assess how efficiently demand turns into opportunities.

How AI helps: The first key step to improving lead generation is smarter prioritization of potential customers.

Predictive AI ranks prospects by behavior and intent, helping sales reps focus on high-quality leads faster.

AI can also take a conservational form to handle FAQs and automatically book meetings for high-intent visitors. This shortens the time from website visit to sales meeting, boosting the number of priority leads.

Smarter Spend, Sharper ROI

What it is: Beyond revenue considerations, marketers are also expected to deliver ROI in a financial sense.

One of the most common marketing KPIs is the customer acquisition cost. It measures whether marketing investments deliver strong returns.

These costs can include the spending on paid advertisements, creative production costs, conference travel budgets, and the costs of marketing tools being deployed.

How AI helps: Generative AI reduces the cost of creating marketing content. Marketing text, visuals, and landing pages can all be crafted by AI to suit unique target audiences with ease.

Furthermore, effective audience analytics reveal which channels customers use most. This will help an enterprise to decide whether they should pause or cut underperforming paid campaigns.

AI That Thinks Like a CMO

For buyers in evaluation mode, the mandate is clear: choose AI that does the boring work brilliantly – identity, attribution, prioritization, scheduling, and hygiene.

Whatever KPI they pursue, AI enables marketers to focus on high-value work like positioning, discovery, and closing.

Navigate the sales & marketing landscape

To discover how technology can transform workplace productivity in your marketing team, dive into CX Today’s Ultimate Guide to Sales & Marketing Technology.

Tim Banting doesn’t mince words. “Given that we’ve just had Halloween, I’m introducing the term: Frankenstack,” says the Head of Research at Techtelligence. The definition?

“A horrible cobbled together layering of bots and automation and analytics.”

It’s a vivid metaphor for a very real enterprise challenge. In their race to modernise customer experience, many organisations have piled on AI tools, each solving isolated problems but collectively creating confusion. He explains:

“They’ve hit this wall where adding tech adds cost and complexity and it doesn’t provide any degree of clarity.”

Instead of scaling value, enterprises are scaling frustration.

From AI overload to orchestration clarity: Making CX systems sing

The pendulum, Banting argues, is now swinging back. “What we’re looking at now is this resurgence of journey orchestration,” he says. “It offers a way to make existing systems talk to each other and automate handoffs between these Frankenstack systems.”

He explains that AI excels at optimizing moments within the customer journey, for example, agent assist tools or chatbots handling simple transactions. However orchestration optimizes the full journey.

Banting compares it to a conductor leading an orchestra: “You don’t have the brass section doing their own thing and percussion doing their own thing. It really does require something at the top to help guide it, coordinate it, schedule it and orchestrate that journey.”

Ultimately, the goal should be not more machinery but a smoother flow.

The buying shift: From AI expansion to workflow simplification

Techtelligence’s latest data backs up this trend. “Buyers aren’t hunting for new AI platforms,” Banting confirms. “They’re researching workflow orchestration, data unification, and process simplification.”

This is the latest chapter in 2025’s quietly growing trend – a lean towards ‘cost to serve’ as the key metric for success. Especially as enterprises are under pressure to do more but with a fewer headcount.

When “every customer interaction involves three or four different systems and multiple handoffs, your cost to serve really skyrockets”, Banting says. Automating this process is where orchestration shines, enabling enterprises to increase productivity.

Less tech, more flow

As enterprises consolidate, one message rings clear: the AI arms race is over; orchestration wins the war on complexity.

“There’s no one platform to rule them all,” Banting concludes.

“You really need to do your due diligence and talk about workflow integration with vendors. That will become more important to get the best productivity, both from individuals and also from teams.”

Orchestration is the quiet revolution bringing order to the AI chaos – and the smartest CIOs and CX leaders are already tuning in.

Keep up to date with the latest tech buyer trends

Find Tim’s full analysis on Techtelligence.

If you’re an enterprise technology buyer or involved in procurement decisions for your business, follow Techtelligence on LinkedIn for weekly insights, analysis, and expert advice to help you make smarter technology choices.

You can also join its growing LinkedIn Community Group to discuss trends, share experiences, and connect with like-minded business professionals driving digital transformation in their industries.

These are clear signs that AI for the CMO is already being woven into day-to-day execution – not parked in innovation labs. 

For those who want to explore how AI is becoming an unmissable part of the modern marketing team, here are 3 examples of how: 

AI-Powered Personalization: Turning Data into Revenue Growth 

Why it matters now
Personalization isn’t a nice-to-have; it impacts revenue. In a recent HubSpot survey, 44% of marketers said offering customers a personalized experience “increased sales significantly.” That’s a striking proof point for CMOs trying to justify deeper investment in data, decisioning and creative ops.  

How AI is optimizing the work
Generative and predictive models help teams scale what used to be hand-built. AI-powered chatbots can resolve queries with brand-safe answers, while still delivering a unique personal experience. 

Meanwhile enterprise landing pages can now automatically adapt to context (target source, segment, behaviour) without manual production of hundreds of variants.  

Use case
Vervoe, an HCM skills platform, used Twilio Segment to personalise ad copy to a target’s specific job role and objectives. The company reported a 2x – 5x average lift in campaign conversions. And a 25% reduction in customer acquisition cost. All from switching to dynamic, role-specific messages.

Predictive Analytics: Smarter Insights, Stronger ROI 

Why it matters now
Salesforce’s 2024 research tied the rise of AI in marketing directly to two families of use cases: generative AI and predictive AI, noting that over half (54%) of AI-using marketers apply predictive tools today.  

For CMOs that don’t want to be left behind, they should be considering how AI and analytics can identify moving trends and changing attitudes. Further down the buyer journey, analytics can also inform leaders about churn risk – an upgrade on legacy dashboards. 

How AI is optimizing the work
Modern analytics platforms surface patterns no human eye will catch – and do it at speed. For example, it can scan to detect sentiment shifts on social media, or even in conversations with customers.  

Analytics can also identify hidden links between customers, helping teams to then refine segmentation. This also helps to schedule interventions with at-risk customers. High-risk customers can be flagged and routed towards ‘save plays.’  

Case study
NinjaCat adopted 6sense AI solutions to sharpen targeting for its “Big Data Day” campaign. By analyzing the relevant LinkedIn community, the team engaged 397 high-value accounts – a 422% increase on prior campaigns. All while cutting cost-per-click by 48%. That’s the practical value of predictive account selection meeting precise media activation. 

Generative Content Creation: Scaling Output Without Losing Relevance 

Why it matters now
Content demand is exploding – and AI is the only way many teams can keep pace according to HubSpot’s 2025 State of Marketing report. A breakout tactic is using AI to turn text into multimedia assets such as demos, presentations and podcasts. This accelerates production without sacrificing personalization.

Adobe’s 2025 trends report echoes the pressure: customers expect relevant offers, at the right time, consistently across touchpoints.  

How AI is optimizing the work
Canva’s Magic Studio can translate prompts into on-brand visuals and video variations, while workflow features keep assets aligned to brand guidelines at scale.  

For video, Synthesia lets spokespeople or trainers produce multilingual clips from scripts – ideal for localized launches and support.  

And when offers are personalized in real time, generative models can render copy variants that fit a “best-next-offer” without manual rewrites.  

Case study
Lab-tech firm Cphnano previously produced one video a year using an external crew. After adopting Synthesia, it now creates 10x more videos. Not only that, but scripts can be updated without reshoots, and there was a 50% increase in SEO visibility within three months. Concrete proof of AI turning content velocity into discoverability.  

The CMO’s Next Steps 

AI for the CMO is no longer a speculative line item – it’s a force multiplier.  

The evidence shows three repeatable wins:

• Personalization that measurably lifts conversion while reducing CAC
• Predictive analytics that concentrate spend on high-yield accounts and steady the forecast
• Content creation pipelines that produce and localize assets at speed.

The common denominator is disciplined data and workflow design. Get that right, and AI doesn’t just make marketing faster; it makes it smarter, cheaper and closer to the customer. 

To discover more insights into the latest & greatest tools driving productivity, dive into our Ultimate Guide to Sales & Marketing Technology. 

Sales and marketing departments have entered a new era. One defined by rising customer expectations, fierce digital competition, and intense pressure on revenue performance.  

As growth slows and investor scrutiny increases, commercial leaders must deliver more profitable and customer-centric outcomes. Technology now sits at the heart of that mission. 

Long gone are the days of the salesperson with only charm in their utility belt. Modern sales teams are harnessing digital tools to personalize their message, deeply understand their target buyers, and streamline their own workflows. 

When it comes to marketing, the modern buyer wants more than catchy slogans and tech demos. More independent than ever, buyers respond to marketers who can provide real data and relevant content. 

This guide explores the capabilities, considerations, and opportunities within the evolving sales & marketing technology landscape. Its goal is to equip leaders with clarity, structure, and direction. To help them find the right tools to keep up – or dominate – in a competitive enterprise landscape. 

This comprehensive guide will help you understand: 

• What is Sales & Marketing Technology?
• The Key Trends Defining This Sector 
• The Different Categories Within Sales & Marketing Tech 
• What is in the Sales & Marketing Tech Stack?
• Why Enterprises are Struggling with New Tech Adoption
• Vendor Selection: How Do I Do It? 
• 2030 and Beyond: The Future Trends Defining Sales & Marketing 
• Where Do I Go to Find Out More? 

Sales & Marketing Technology, Explained: The Engine Behind Modern Growth

Sales and Marketing Technology refers to a connected ecosystem of tools and platforms. Their aim is to help organizations attract, engage, convert, and retain customers across the full buyer journey. 

Its purpose is to unite data, digital channels, and revenue teams so businesses can strengthen customer lifetime value.  

In today’s competitive climate, technology has become the backbone of modern commercial strategy.  

The New Rules of Revenue: Trends Reshaping Sales & Marketing

Analysts have identified three key areas where sales & marketing leaders are looking to improve – driving interest in new technology:

“CSOs must balance revenue generation with operational efficiency as investor demands rise.” – Gartner

 

“Leaders must prioritize improving revenue processes and customer-centric strategies.” – Forrester  

 

“The 2025 trend to watch for is the Personalization Renaissance.” – Mintel  

In other words, three capabilities have now become competitive differentiators. Better revenue generation, better personalization, and better processes among sales and marketing teams.  

Forward-thinking enterprises have responded to these challenges in a similar way – harnessing artificial intelligence. Here are three recent case studies where these areas were addressed with new AI products. 

• Better revenue generation:
  • PWC identified one key differentiator enabling some marketing teams to deliver nearly 80% more shareholder value than competitors. Their trick? Harnessing and investing in AI.
  • “Used narrowly, AI can make marketing less expensive – faster content, smaller budgets, leaner teams. Used strategically, it can make marketing indispensable – unlocking new growth, higher profitability, greater enterprise value”, it concluded.
  • Top performing marketing teams were found to regularly invest in new AI-powered capabilities. This in turn generates revenue, and funds marketing teams to invest in more tools – creating a cycle of enterprise success. 

• Better personalization:
  • A European telecom company found that customers receiving personalized messages took action 10 percent more than those who received generic advertising materials.
  • According to a 2025 McKinsey report, the company used GenAI to personalize messaging based on age, gender, and data usage.
  • McKinsey reported that it has “seen some marketers deploy gen AI to personalize content development 50 times faster than a more manual approach”. 

• Better processes:
  • Enterprise IT platform Workday saw 3,500% ROI after adopting an automation solution for client contracts. 
  • By connecting data across platforms, the Workday team saved hundreds of thousands of hours in its global projects. 

The Four Categories of the Sales & Marketing Technology Landscape

Whether you’re an inbound marketing expert, a sales team leader, or a CIO, you have an important role to play in harnessing and optimizing these technologies.  

The team at CX Today have distilled this wide-range of products into 4 core categories. 

1. Building Awareness (Acquisition)
   Tools that help organizations attract attention, generate demand, and capture leads through content, campaigns, and data-driven targeting.
2. Sales Enablement (Closing Deals)
   Platforms that empower sales teams with insights, automation, messaging, and training to shorten sales cycles and increase conversion.
3. Retaining Customers (Client Success)
   Technology that strengthens onboarding, engagement, renewal, and advocacy – protecting recurring revenue and reducing opportunities for churn. 
4. Omnichannel Connection (Linking Everything Together)
   Integration and automation technologies that unify data to deliver consistent experiences end-to-end. 

Your Tech Arsenal: Understanding the Tools That Fuel Revenue

Vendors are looking to support a broad spectrum of job titles within sales & marketing, with an aim to automate, optimize, and manage workloads. With the four categories as a dividing structure, here are some of the typical solutions that are found in the modern sales & marketing tech stack: 

Building Awareness 

This is the front door of the enterprise growth engine. Awareness tools help brands capture attention, generate qualified leads, and convert insights into pipeline opportunities.  

• Social media and content sharing: These platforms look to generate leads through inbound marketing where interested readers are prompted to engage further with the brand. This is particularly valuable for industries that rely on thought-leadership to grow brand awareness, such as professional & business services (consulting, legal, accounting). 

• Lead capture: This can include strategic forms where brands can gather intent data and contact details to set priority accounts for further marketing. It may also include web deanonymization, where brands can reach out to visitors to its site for sourcing potential leads. 

• Smart webpages & content journeys: Modern customers want more independence and feel more confident doing their own research. These platforms can help personalize and enhance how customers reach the decision stage, providing relevant content without being overbearing. 

• Related vendors: HubSpot, Hootsuite, 6sense, Uberflip 

Sales Enablement 

Once awareness is established, sales enablement technology equips revenue teams to engage prospects intelligently and close business deals efficiently. This leads to shorter sales cycles and higher conversation rates. 

• CRM systems: A strategic approach to converting sales relies on a centralized database which highlights opportunities, past interactions – in sync with the awareness-building marketing teams. 

• Smarter sales pitches: Salespeople are in a stronger position to increase conversions when they have personalised decks, playbooks, and pitches. These solutions help manage and track these sales content pieces. 

• Training and Coaching: These solutions put salespeople in a meeting with AI avatars, letting them practice their pitch in a risk-free environment. And after a genuine sales meeting, they can analyse transcripts to find areas for improvement. 

• Related vendors: Highspot, Seismic, Showell, Microsoft Dynamics 365 

Retaining Customers 

Retention and expansion are now as critical as acquisition. Customer success technology ensures that value delivery continues beyond the sale, and helps Chief Revenue Officers to better predict financial forecasts. 

• Customer Success Platforms: With data analytics and dashboards, these solutions can help client teams to understand the health of the accounts they manage. From there, they can upsell promising accounts or prevent churn. 

• Product Usage Analytics: One of the core ways to retaining customers is to ensure their onboarding and usage is going smoothly. These tools track this information and enhance the work of support teams who address problems. 

• Post-purchase communication tools: Once customers are onboarded, client teams can use a lifecycle communication tool to keep customers engaged and prime them for upsell pitches. 

• Related vendors: Qualtrics, Amplitude, Totango, Braze. 

Omnichannel Connection 

The final layer – and the most transformative – focuses on unifying data and experience across the organization. These platforms make marketing, sales, and service work as a single, insight-driven system. 

• Customer data & identity management: With these intuitive systems, client teams have a top-down 360° view of all customer touchpoints. This enables personalized content and predictive insights across the client journey. 

• Smarter digital engagement: When clients communicate with a company’s AI bots or human agents, omnichannel systems enable seamless conversations across sessions and channels. 

• Integration & automation layers: Acting as the connective tissue itself, these tools enable information to automatically flow to the right places and at the right times, feeding the 360° vision. 

• Related vendors: Salesforce, Zeta Global, Sprinklr, Zapier. 

Mind the Gaps: Adoption Pitfalls That Can Stall Your Tech ROI

While these solutions can open new doors for marketing & sales teams, adoption is not always a straightforward path. And the stakes are high.  

Putting aside financial consequences for the business, members of a buying committee may lose credibility if they support a purchasing decision that later fails to bear fruit. 

Going one step further, an unsuitable adoption journey can spark a negative reaction from customers, regulators, and investors.  

Vendors often pitch that their tools are simply ‘plug and play’. The reality is that enterprises must consider these adoption challenges: 

Privacy and first-party data:

Concerns around user privacy have been growing steadily, and this has resulted in a clamp down on legacy forms of data collection, such as third-party cookies. 

• Marketing teams today are expected to comply with all new privacy regulations. They should aim to responsibly collect consented data while fostering valuable personalization.
• Enterprise buyers must consider GDPR, CCPA, and other AI regulations – or risk the resulting backlash. 

Preventing underutilization:

A 2022 study from Gartner found that marketers were only utilizing 42% of their martech stack, a 16% drop from two years prior. The most common reason for this was an overlap of tech solutions, rendering part of the stack obsolete.

• With this in mind, sales and marketing teams can first consider how legacy solutions can be upgraded. Implementing a whole new solution may result in waste and underutilization. 

Employee satisfaction and skills:

One of the most common adoption challenges across every aspect of tech is ensuring the satisfaction of the humans that will be interacting with it day in and day out. 

• Adoption can be stalled when teams don’t have the skills or motivation to complete the onboarding. In relationship-driven industries such as sales, hesitancy about handing over the reins to AI is understandable.
• Secondly, if vendors can’t provide comprehensive support post-purchase, this will also extend the adoption process unnecessarily.  

Proving value:

“It’s not what you know, it’s what you can prove”. This isn’t just a stereotypical line from a legal drama, it’s also a key consideration when purchasing a new sales & marketing tool. 

• Due to the nature of outbound marketing, for example, it can be difficult for CMOs to prove that their department is truly delivering ROI. 
• More page views? More likes on a LinkedIn post? An increased email open rate? Consider which metrics are strong enough to justify a purchasing decision and whether a new solution will complement them. 
• For sales leaders, one common target is maximizing time with potential clients. This means they must prove that any new tech solution adopted is truly optimizing their time and workflow to open up their calendars for more calls. 

Rather than looking at the new features of an AI tool, tech buyers can start by looking at their own KPIs. Considering how a new solution will integrate and enhance existing processes of success measurement is key.

Cutting Through the Noise: A Buyer’s Guide to Selection Criteria

15,384.  

In kilometres, it’s roughly the distance between Guatemala City and Hyderabad. To count to from one, it would take you around four hours. But it’s also the number of Martech solutions that exist in 2025, according to a recent report. 

There isn’t even a recognised number of Salestech solutions out there, depending how wide one wants to cast their net, but it’s estimated to be over 1000. 

Rather than cause a serious bout of decision paralysis, these statistics are meant to illustrate how critical it is for enterprise buyers to have effective selection criteria. 

Here are some starting points to help you get from thousands to just one. The right one. 

Identifying your pain point:

Enterprise buyers will likely know what their primary growth constraint is, whether it’s more leads, more conversions, or a more comprehensive view of the entire customer experience. 

• Using the four categories system, buyers should ensure that there is a strategic fit with the relevant buying stage.  
• Keep in mind however, how processes upstream and downstream will impact your KPIs and growth. If you’re a sales leader struggling to convert meetings into successful deals, it could be that the marketing team is not generating targeted enough leads, for example. 

Data unification costs:

While it’s been established that AI can be a major differentiator in sales & marketing, but it is very much the ‘cherry on top’, as opposed to the solid foundation to build upon.  

• Without unifying new tools with existing data systems, creating a single, reliable source of truth becomes difficult.
• Without that, AI can expose more problems than it solves according to Tim Banting of Techtelligence. 
• Therefore, enterprise buyers should not expect a flashy new AI solution to be a ‘do-it-all’ tool. Instead, selection criteria should evaluate the integration costs that will create a fertile soil for AI to grow from. 

Success-centric vendors:

Vendors will already be thinking about how to prove their solution is right for you. Consider whether a vendor can provide a measurable realistic forecast of productivity gains that you can take to the buying committee.

• This mindset signals a shift away from promises, and more towards tying programs to results. 

The Future Trends Defining Sales & Marketing Technology

As we look toward 2030 and beyond, the sales & marketing technology landscape will undergo deeper transformation driven by greater automation, personalization, and data management.  

These teams can expect to see transformation in the content they produce, but also the workflows that define their workplace experience.  

Here are some future capabilities and trends to consider: 

The human and the machine:

While automation and AI will be embedded deeply, the differentiator will shift to how humans play a strategic role. Can they use technology to amplify creativity, judgement and empathy – not replace it?

• More than 50% of the internet is estimated to be AI-generated content. That trend has no signs of slowing. Companies that utilize AI, while amplifying their humanness, could capitalize on AI-fatigue among customers. 

Outcome-driven models:

As pressure mounts to prove ROI among sales & marketing teams, vendors may change how they frame their products.  

• The time of features, capabilities and upgrades, could be no more. 
• The future could vendors and buyers more oriented around business outcomes (e.g., increased net revenue retention, shorter sales cycles), rather than flashy products. 

Convergence of martech-adtech-servicetech:

The segmentation between “marketing tools”, “sales tools” and “customer service tools” will blur. This comes as omnichannel data connects everything and intelligence is found everywhere. 

• The future could see enterprises normalize an integrated revenue ecosystem with real-time feedback loops across the customer journey.

Final Takeaway: Building a Smarter, More Human Sales & Marketing Engine

Over the coming years, AI-driven orchestration, unified customer data, and outcome-based measurement will redefine what effective go-to-market execution looks like.  

Leaders who invest in scalable, ethical, and modular platforms today will find themselves better equipped to navigate tomorrow’s volatility. A future where buyer journeys are self-directed, expectations are personal, and attention is fleeting. 

Stay up to date on the latest sales & technology news by following CX Today on LinkedIn and keeping an eye on our new website category: Sales & Marketing Technology.

To hear from fellow buyers and tech decision makers, join the CX Today community group on LinkedIn where over 40,000 industry professionals are inspiring change. 

The next twelve months will test every assumption in customer experience. Economic pressure, rising costs, and cautious spending are pushing CX leaders to prove value faster. 

Tim Banting, Head of Research at Techtelligence, says: 

“The latter half of 2025 and 2026 will be a year of pragmatism.” 

“Enterprises aren’t slamming on the brakes – they’re shifting focus toward tangible benefits.” 

According to Techtelligence data from more than 1,800 organizations, 62% are actively researching or deploying new CX and UC platforms. These companies want measurable efficiency, reduced redundancy, and technology that pays back in time saved, not just promises kept. 

Predictive, Personal, and Human: The New CX Frontier 

An emerging CX trend focuses on evolving from reactive customer service to predictive empathy. Banting explains: 

“We’re seeing AI move from automation toward predictive and personal intelligence. It’s becoming a more empathetic layer rather than a reactive service.” 

Techtelligence data shows over 1,100 enterprises are currently exploring systems that combine automation with emotion-sensing AI to anticipate dissatisfaction before it surfaces. The intent is to retain loyalty in a slower economy where every customer counts. 

However, this personal touch introduces new governance challenges. “You need to make sure your AI is explainable and that your data policies are auditable,” Banting warns. “You can’t just take AI’s word for it.” 

For CX buyers, key evaluation questions include: 

• How does the vendor ensure AI explainability? 

• Is there a unified data layer to fuel the AI’s learning? 

• What metrics show measurable impact on resolution speed or satisfaction? 

Time Capital as the New Resource to Watch 

If 2023 was about ROI,  a CX trend to watch for in 2026 is ROT – Return on Time. “Success is being measured by time given back to employees and customers, not just money saved,” Banting explains. “Time capital will become as valuable as financial capital.” 

This shift reframes efficiency from cost-cutting to time-creation. Shaving seconds off a call or automating back-office tasks compounds across thousands of interactions. Yet Banting cautions against shallow interpretations: “Time saved doesn’t automatically mean it’s used wisely. We should look at how AI helps productivity, not how many employees can be replaced with the time saved”. 

For CX leaders, this is a wake-up call to expand metrics beyond the contact center. True efficiency spans the entire customer journey, from inquiry to resolution. Measuring time across departments – support, logistics, and product – reveals the real impact of AI on business productivity. 

Experience Infrastructure: Where CX Meets UC 

The boundary between customer experience and collaboration is fading fast. “By 2026, unified communications and customer experience won’t be treated as separate categories,” says Banting. “They’ll merge into what we call experience infrastructure.” 

Techtelligence defines Experience Infrastructure as the unified system that connects employee collaboration (UC) with customer engagement (CX). Nearly one in five enterprises are already researching both areas together. 

The logic is simple: internal speed equals external satisfaction. When back-office experts, service agents, and AI tools share data in real time, resolution times shrink, and consistency improves. The outcome is a seamless flow from internal teamwork to external delivery. 

CX buyers evaluating new platforms should ask vendors: 

• Can our UC and CX data layers interoperate natively? 

• How easily can customer-facing insights flow back to internal teams? 

• What governance controls exist across both systems? 

From Testing to Doing: The Execution Era 

Enterprises are done experimenting. “We’re moving from experimentation to execution,” Banting says. “AI and automation aren’t pilots anymore—they’re being scaled for measurable outcomes.” 

Techtelligence’s Q4 2025 Buyer Intent Index shows that 41% of large enterprises are using AI to improve collaboration and 39% are adopting predictive CX tools. This marks a shift from proof-of-concept to proof-of-performance. 

Conclusion: The CX Buyer’s 2026 Playbook 

The next phase of CX maturity will hinge on efficiency, intelligence, and trust – the three benchmarks Techtelligence calls the “enterprise decision-maker’s agenda.” 

To stay ahead, CX leaders should: 

• Invest in predictive AI that understands emotion and intent. 

• Quantify “time capital” across the full customer journey. 

• Demand explainable AI and visible data governance. 

• Align UC and CX investments within an experience infrastructure framework. 

• Prioritize partners that show proof over promises. 

In Banting’s words, “This is really the year of implementation and the year of pragmatism.” For CX buyers, that means acting on insight, measuring in time, and proving value – fast. 

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To find Tim’s advice on buyer power in 2026, dive into his article on Techtelligence.

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“CX isn’t viewed as a single application anymore. It’s just a systems integration problem,” says Tim Banting, Head of Research at Techtelligence.

Modern CX enterprises juggle dozens of disconnected systems – CRM, HR, marketing, data lakes – all tracking partial versions of the customer.

Even something as simple as moving contacts from one phone to another can be painful, Banting notes. Multiply that complexity across global systems, and it’s easy to see where things start to break.

AI Doesn’t Fix Bad Data – It Exposes It

The belief that AI can “make sense” of messy data is widespread – and wrong.

“A lot of people think all I need to do is turn it on, point it to a whole load of PDFs, and AI will make sense of it,” Banting explains. “But it takes a lot of data hygiene, a lot of taxonomy, a lot of clearing up.”

Rather than clarifying customer insights, AI often compounds the confusion. “It’s got so many sources that it just shows you everything and doesn’t know how to react,” he says.

Without a unified data layer, AI in customer experience will churn out inconsistent results and unreliable recommendations.

The outcome: stalled AI initiatives and frustrated leadership teams waiting for their data foundations to catch up.

The New Battleground: Data Integrity

Techtelligence forecasts that by 2027, businesses that unify CX and communications data could reduce service costs by up to 25% and improve retention by 10–15%.

The shift is already visible. Banting observes that “the most read articles on CX Today recently have been about integration, like the Agentforce rollout, rather than new features.”

Buyers, he says, are now prioritizing orchestration and connected workflows over flashy tools. Clean, unified data is fast becoming the new competitive weapon.

The Pressure to Unify Is Mounting

Data unification can no longer be an afterthought. Regulatory, financial, and operational pressures are accelerating the need for action:

• Regulation: New frameworks such as the EU AI Act demand clear data lineage and consent management.
• Economics: With static IT budgets, “buyers want fewer vendors and not more middleware.”
• Fatigue: “People are done stitching together best-of-breed tools that don’t talk to each other.”

Delaying data integration now risks slowing growth, introducing compliance hurdles, and increasing technical debt, Banting highlights.

Four Lenses to Evaluate CX and AI Vendors

Banting warns against chasing the next “shiny object” to fix data chaos. Instead, he suggests evaluating vendors through four practical lenses:

• Data Architecture: Does it build a single, unified customer profile?
• Explainability: Can AI “show its workings out”?
• Openness: Does it support open standards like MCP for integrating multiple AI agents?
• Governance: Is the solution “audit proof” with full data lineage and compliance tracking?

“You need AI to explain how it’s reached a decision,” Banting adds. “I was always told to show my workings out in maths class – AI needs to do the same.”

Matching Tools to Strategy, Not Hype

Techtelligence’s “strategic clusters” framework offers a smarter way to align CX investments.

“If the best car is a Ferrari, that’s great for racing, but not for your groceries,” Banting quips. “The same applies to CX solutions. You match the tool to the task.”

He advises buyers to focus less on technical specs and more on long-term alignment – how well a vendor fits enterprise data strategy, IT architecture, and financial goals.

The right match can transform data from a bottleneck into a springboard, reigniting stalled AI projects and restoring the promise of intelligent customer experience.

Stay Ahead in Enterprise Technology

To find Tim Banting’s full advice for tech buyers, read his latest post on Techtelligence.com.

If you’re an enterprise technology buyer or involved in procurement decisions for your business, follow Techtelligence on LinkedIn for weekly insights, analysis, and expert advice to help you make smarter technology choices.

You can also join its growing LinkedIn Community Group to discuss trends, share experiences, and connect with like-minded business professionals driving digital transformation in their industries.