B2B Omnichannel Strategy in 2025: AI-Powered Personalization at Scale

Major Takeaways: B2B Omnichannel Strategy

Why are omnichannel buyers changing how B2B sales work?

• B2B buyers now use 10+ channels during their purchasing journey, and 80% of sales interactions happen in digital channels by 2025. This demands a shift in how we structure outreach and engagement.

How does AI enable scalable personalization across channels?

• AI-powered tools analyze behavior across touchpoints to deliver personalized content, messaging, and timing at scale—boosting conversion rates by up to 35%.

What’s the backbone of a successful omnichannel marketing program?

• A strong program includes four key pillars: unified customer data, seamless channel integration, consistent messaging, and real-time responsiveness—each made more effective with AI.

How do high-performing teams integrate AI in their sales processes?

• Leading teams use AI for lead scoring, intent tracking, and content automation. This enables them to prioritize outreach, personalize follow-ups, and improve speed-to-lead without increasing headcount.

What are the common challenges in AI-powered omnichannel strategies?

• Key pitfalls include data silos, tool overload, privacy concerns, and lack of cross-functional alignment. These can be overcome through system integration, role clarity, and AI education.

Where does human interaction fit in an automated strategy?

• Despite automation, human reps remain critical for relationship-building, negotiation, and complex problem solving. AI supports—but does not replace—the human touch in B2B sales.

Why is real-time responsiveness essential in 2025?

• AI allows immediate follow-ups triggered by buyer actions—like content downloads or page visits—ensuring engagement happens when interest is highest, increasing meeting rates significantly.

Introduction

Is your B2B sales approach keeping pace with today’s omnichannel buyer? In 2025, digital engagement and personalization have gone from trendy buzzwords to mission-critical strategy. 

Consider this: by 2025, an estimated 80% of B2B sales interactions between suppliers and buyers will occur in digital channels (1). At the same time, 75% of B2B buyers expect personalized experiences during their purchase journey (2). These sweeping changes mean one thing for sales and marketing leaders – we must embrace an AI-powered, omnichannel strategy or risk falling behind.

We’ve entered an era where B2B customers jump fluidly between email, LinkedIn, video calls, chats, and yes, even the occasional phone call. The average B2B buyer now uses 10 or more channels in their purchasing process (7), researching solutions independently and engaging with content across platforms. No single channel or touchpoint seals the deal anymore. 

Omnichannel marketing programs – coordinated outreach across multiple channels – have become essential to meet buyers where they are. Companies that effectively engage buyers on more channels are more likely to gain market share (7). In fact, organizations with strong omnichannel engagement retain on average 89% of their customers, compared to only 33% retention for those with weak omnichannel strategies (8). Clearly, delivering a seamless experience across channels isn’t just a “nice-to-have” – it’s a competitive advantage that drives loyalty and revenue.

At Martal, we’ve witnessed this transformation first-hand. As a B2B lead generation agency operating as an extension of our clients’ sales teams, we’ve transitioned from traditional outreach to an AI-powered omnichannel sales approach. In the past, a sales rep might stick to calls and emails. Today, we combine personalized emails, LinkedIn touches, phone calls, and even AI-driven chat interactions into cohesive sequences that adapt to each prospect. This first-person perspective guides our discussion below. In this comprehensive guide, we’ll share how AI-powered omnichannel strategies are redefining B2B sales in 2025 – and how you can leverage them to personalize sales at scale.

Expect actionable insights, the latest stats in each section, and strategic best practices. Let’s dive in.

The 2025 B2B Buyer: Digital, Demanding, and Omnichannel

The profile of the B2B buyer has evolved dramatically. Gone are the days when buyers relied solely on trade shows, phone calls, or a single account manager for information. Today’s B2B decision-makers are digital natives, accustomed to the convenience of consumer-like experiences. They hop between channels and expect vendors to keep up. 

Recent data shows that younger procurement professionals increasingly prefer no in-person contact at all – conducting research and purchasing through digital self-serve channels whenever possible (6). 

Older generations of buyers are retiring, and a new generation that grew up with smartphones and social media is now in charge of B2B purchasing. This generational shift has huge implications for outbound sales and marketing teams.

Omnichannel is now the default approach for B2B engagement, not the exception. Jennifer Stanley of McKinsey noted “the accelerated transformation of B2B sales into a fully omnichannel approach is now the predominant path.” (6) 

In practice, this means a prospect might discover your solution via a LinkedIn post, download a whitepaper from your website, join a webinar, then receive a sequence of outreach emails and eventually a call – all as part of one integrated journey. If any of those touchpoints feel disjointed or repetitive, the prospect will notice. B2B buyers don’t distinguish between marketing and sales silos; they expect one unified experience.

The numbers underscore why an omnichannel strategy is mission-critical in 2025:

• Digital-first engagement: By 2025, 80% of B2B sales interactions are expected to happen in digital channels (1) – via email, video calls, chat, social media, and e-commerce portals. B2B sellers must excel online because that’s where the bulk of interactions occur (often before a prospect ever talks to a rep). High-performing organizations have adapted accordingly: fully 90% of B2B companies have shifted to a virtual/remote sales model, and 70% say it’s as effective or more than the old in-person model (5). This digital dominance forces us to orchestrate engagement across multiple online platforms.
  
• Channel proliferation: As noted, B2B buyers commonly use 10+ channels on the path to purchase (7). They might read industry reviews, compare vendors on third-party sites, follow your company on social, and expect instant answers via web chat. Only offering an email address or a phone line is no longer enough – if we’re not present on a buyer’s preferred channel, we may not even be considered. Companies recognizing this have expanded their channel mix; McKinsey found those using more channels in their marketing were significantly more likely to gain market share versus those sticking to few channels (7).
  
• Customer preference for omnichannel: Buyers have voted with their feet. In one study, only 25% of B2B customers expressed a desire to ever resume primarily in-person sales interactions post-pandemic (5). The vast majority are comfortable with (and often prefer) remote and self-serve options. They also reward vendors who deliver great omnichannel experiences – sellers that provide outstanding digital interactions are more than 2X as likely to be chosen as a primary supplier by buyers (5). In other words, if your omnichannel experience is superior, buyers notice and you win more deals.
  
• Retention and loyalty impact: Omnichannel isn’t just about acquisition – it drives retention. A unified experience makes customers more likely to stick around. Research shows companies with strong omnichannel customer engagement retain 89% of their customers on average, vs. 33% for companies with weak engagement (8). That’s a staggering gap. Consistency across channels builds trust; customers feel confident they’ll get the same excellent service whether they contact you via email, phone, or social media. Additionally, omnichannel customers are more valuable, with studies finding they have a 30% higher lifetime value than single-channel customers (8). Over time, these effects compound into significantly higher revenue.

It’s clear that today’s B2B buyer demands an omnichannel approach. They expect to seamlessly transition from reading your blog to chatting with a rep on your website, to receiving a follow-up email – without having to repeat themselves at each step. They want consistent messaging and informed interactions at every turn. Deliver that, and you remove friction from their buying process, which accelerates sales cycles. Fail to deliver, and prospects will gravitate to competitors who offer a smoother experience.

From our perspective at Martal, adapting to this new buyer behavior meant overhauling how we engage sales leads. We could no longer rely on just cold calls or single-threaded emails. Our omnichannel outreach programs now weave together multiple touchpoints. For example, for a given campaign we might start by warming up a prospect on LinkedIn – engaging with their posts or sending a connection request with a brief helpful note. Then, our team follows up with a personalized email sequence referencing that LinkedIn interaction, perhaps sharing a case study relevant to the prospect’s industry. If we see the prospect clicking links or visiting our site (signals our AI platform tracks), we might trigger a well-timed phone call from an SDR who comes prepared with context from the prior touches. Throughout, every interaction is logged in our CRM and shared across our team, so that by the time a live conversation happens, we know exactly what the prospect has seen or responded to. This eliminates the dreaded “why am I getting this call, I already filled out a form!” moments. Instead, the prospect experiences a coherent journey: each touchpoint feels like a continuation of the last, not a disconnected outreach. That is the essence of omnichannel.

Importantly, omnichannel doesn’t mean every channel, all the time. It means using the right channels at the right moments in the buyer’s journey, informed by data. A key part of strategy is identifying which channels your buyers actually use and trust (for example, developers might respond better on community forums or email, whereas HR professionals might prefer LinkedIn). The takeaway for 2025: Meet your B2B customers where they are, and be ready to switch channels fluidly as they move through their journey. In the next sections, we’ll explore how artificial intelligence supercharges this effort, enabling the kind of personalization and scale needed to truly excel at omnichannel.

AI Takes Center Stage in Omnichannel Personalization

Omnichannel engagement generates a flood of data and complexity – there are more interactions to track, more content to serve, and more decisions about when and how to reach out. 

This is where artificial intelligence (AI) becomes a game-changer. In 2025, AI has firmly moved from experimental to essential for B2B sales and marketing. A vast majority of companies are embracing it: 78% of companies now use AI in at least one business function (9). The consensus is clear – AI is the key to delivering personalized omnichannel experiences at scale.

Why has AI become so pivotal? Simply put, it can analyze and automate faster (and smarter) than any human team. Let’s break down how AI specifically turbocharges an omnichannel strategy:

• Turning Data into Insights: In an omnichannel world, every prospect leaves a rich trail of digital breadcrumbs – website clicks, email opens, content downloads, social media interactions, chatbot conversations, etc. This cross-channel data is a goldmine for understanding buyer interests and intent, but it’s far too much for manual analysis. AI excels here: machine learning models can crunch massive datasets to identify patterns and predict behaviors. For example, AI algorithms can score sales ready leads based on their engagement across channels, pinpointing who is likely ready for a sales call. They can segment your audience in new ways (e.g. by behavior or firmographics) that inform tailored outreach. Critically, AI can unify disparate data sources into one view of the customer. A unified customer data platform powered by AI ensures that when we look at a prospect, we see their entire journey – the webinar they attended, the whitepaper they downloaded, the questions they asked in chat – all in one place. That unified view is the foundation of effective omnichannel personalization.
  
• Hyper-Personalized Content at Scale: Personalization in B2B used to mean adding a first name to an email or mentioning a company name in a sales pitch. AI blows past those token gestures. Modern AI – particularly Generative AI (like GPT-4) – can craft highly individualized messages for each prospect by drawing on context. For instance, given a prospect’s industry, role, and the content they engaged with, an AI writing assistant can generate a custom email highlighting exactly how your solution addresses the prospect’s known pain points. 

AI can dynamically personalize website content: your site might show different case studies or product pages depending on the visitor’s profile and past behavior (all decided in real time by an AI engine). The result is that potential customers feel understood from the first touch. And it works – personalization pays dividends. 

Studies have found that personalization is a growth driver. Fast-growing companies earn 40% more of their revenue from personalization than slower competitors (3). 

These are massive lifts that directly impact pipeline. It’s not hard to see why 83% of companies now consider AI a strategic priority for their business (4) – it drives results.

• Optimizing Timing and Channel with Intelligence: AI doesn’t just personalize what message you send – it also optimizes when and where you send it. Machine learning models can determine the ideal contact times for each prospect (e.g. based on past email open patterns or regional work hours). AI scheduling tools then automatically send messages at those optimal times or prompt reps to call at a specific hour. Beyond timing, AI can suggest the best channel for the next touch. For example, if Prospect A never responds to emails but engages on LinkedIn, the AI might cue up a LinkedIn InMail instead of yet another email. 

These kinds of intelligent orchestration decisions ensure that your outreach is not just personalized in content, but also delivered in the most receptive context. The result is higher engagement rates and reply rates. With AI’s help, we’re no longer guessing the next step in the sales cadence or outbound sequence; we’re basing it on data-driven predictions of what will work best.

• Scaling Humanly Impossible Tasks: One of the strongest cases for AI is how it lets us scale up efforts that would be impossible to do manually. Think of a sales development representative trying to personalize 100 emails per day – they might manage a few truly custom notes, then resort to sales email templates for the rest. 

But an AI assistant can personalize hundreds or thousands of emails individually, each reflecting the prospect’s context, in a fraction of the time. Similarly, AI chatbots can handle routine customer inquiries 24/7 on your website or Slack, providing instant responses that feel personal, while your human reps focus on high-value conversations. Internally, AI can auto-update your CRM after calls by transcribing and summarizing meetings – a huge time saver that ensures data quality (no more incomplete notes). In 2025, we’re seeing these tasks shift to AI, freeing salespeople to spend more time selling and less on admin. The payoff is tangible: one survey noted that 64% of B2B organizations have integrated AI to improve lead qualification, and they’re reducing time wasted on unqualified leads as a result (12). The productivity gains AI affords are akin to adding extra team members who work tirelessly and don’t sleep.

• Continuous Learning and Improvement: AI systems get smarter over time. They learn from each interaction – if a particular email subject line yields better open rates in one segment, the AI can start adjusting subject lines for similar contacts. If certain sequence patterns lead to more conversions, the AI picks up on that. In essence, your omnichannel program becomes self-optimizing. This is a huge departure from traditional campaigns that required manual A/B tests and quarterly tweaks. With AI, optimization is ongoing and real-time. For example, Martal’s own proprietary AI sales platform (powered by Landbot GTM-1 Omni) analyzes over 3,000 intent signals and engagement data points to refine our outreach tactics continuously. It might learn that CTOs in the fintech sector respond better to a whitepaper touch first, whereas CMOs in SaaS prefer a quick infographic. Those insights then automatically adjust our playbooks – send more whitepapers to fintech CTOs, use more visuals for SaaS CMOs, and so on. Over hundreds of campaigns, this adaptive learning drives significantly higher overall outcomes.

Put simply, AI is the engine that makes large-scale personalization feasible. We now have the prospecting tools to treat each prospect as a “market of one” without a staff of hundreds to do the research and content creation manually. It’s hard to overstate how transformative that is for B2B sales. By leveraging AI, even lean sales teams can execute complex omnichannel campaigns that feel hand-crafted to each account. No wonder nearly 90% of business leaders say personalized engagement is crucial to their success in the next few years (10). AI is how we deliver that personalization efficiently.

To illustrate, let’s consider a quick scenario: Suppose a target account visits your pricing page (a high-intent signal). Traditionally, a rep might or might not notice that in time to act. An AI-powered system, however, will immediately flag this behavior, update the lead score, and perhaps trigger an action – for example, send an automated “Hey, saw you checking out our pricing – can I answer any questions?” chat message on the site, or notify the account’s SDR to reach out personally within the next hour. If that contact happens on LinkedIn, the SDR will have context from the AI (knowing which pages were viewed, what content was downloaded) and can tailor the conversation accordingly: “Many customers in [Prospect’s industry] who look at our pricing are evaluating X vs Y – I’d be happy to share how we price those modules based on your needs.” This timely, contextual touch would not be possible at scale without AI monitoring and assisting. It’s a great example of AI enabling real-time responsiveness, one of the core pillars of effective omnichannel strategy we’ll discuss next.

Before moving on, it’s worth addressing a common concern: does all this AI-driven automation make B2B sales impersonal? The irony is that AI, when used correctly, actually makes engagement more personal – because it tailors experiences to each individual. Buyers feel more understood, not less. Of course, there’s a balance to maintain (we’ll cover the importance of the human touch later), but in our experience, the companies embracing AI for personalization are forging stronger customer connections and outpacing those that don’t. 

One study even found 68% of companies report their personalization initiatives have exceeded their revenue and conversion targets (13) – underscoring that many firms underestimated how powerful personalized engagement (enabled by AI) could be. The bottom line: AI is no longer optional for B2B omnichannel success in 2025 – it’s a necessity. Now, let’s get practical about how to build an AI-enhanced omnichannel program.

Designing Omnichannel Marketing Programs with AI

If omnichannel is the what and AI is the how, then this section is about putting them together into a concrete program. How do you structure omnichannel marketing programs that leverage AI to drive personalized sales engagement? It starts with a solid foundation. At Martal, when we design an omnichannel campaign for a client, we focus on four foundational pillars:

These pillars form the blueprint of an effective omnichannel program. Let’s walk through how you might actually implement an AI-powered omnichannel strategy step by step, using these principles:

1. Centralize and Clean Your Data (Unified Data): Begin by connecting your data sources – CRM, marketing automation platform, website analytics, social media, customer support logs – into a central repository or customer data platform. This is where investing in data integration and data enrichment tools or CDPs pays off. It might not sound exciting, but data quality is make-or-break for AI personalization. (No one wants to personalize based on the wrong info – *“Hi [First Name]” emails, anyone?)

In fact, 61% of companies worry that inaccurate data is undermining their AI-driven personalization efforts (10). So, ensure your data is de-duplicated, updated, and enriched. Machine learning can assist email list cleaning by reconciling records (identifying that “IBM” and “International Business Machines” in two systems are the same account, for example). At Martal, we feed multiple data streams into our AI platform which then continuously cleans and enriches prospect profiles – verifying emails, updating job titles from LinkedIn, appending intent data, etc. This unified, clean dataset is the bedrock upon which all personalization is built.

2. Map the Customer Journey and Key Touchpoints: With data in hand, outline the typical journey a prospect takes from awareness to consideration to decision. Identify the key touchpoints where engagement happens or where prospects often drop off. For each stage, decide which channel(s) make sense. For instance, early-stage might be best for marketing emails and social content; mid-stage might involve webinars or case study emails; later-stage might trigger direct sales outreach or personalized microsites for the account. AI can assist journey mapping by analyzing historical data to find common paths or choke points. For example, if you find many prospects view the pricing page multiple times but don’t convert, maybe that’s a point to intervene with a personalized offer or live chat. The goal is to architect an omnichannel flow: perhaps a sequence where lead nurturing is conducted via email, then they are invited via LinkedIn to a relevant event, and after the event, a text or call scheduling link is sent. Each step should logically follow from the last, creating a narrative. Our team often uses AI-based journey orchestration tools that can design and automate these multi-channel workflows, ensuring that when a prospect completes step A, they automatically flow to step B across whatever channel is pre-determined (unless the AI dynamically changes it, which it might based on engagement).

3. Personalize Content and Messaging for Each Channel: Now craft the content, leveraging AI for scale. Start with your core messaging (value propositions, pain points solved) – this should remain consistent (pillar: Consistent Messaging). Then tailor that message to each channel format. An email might dive into technical details in text, whereas a LinkedIn message might be shorter and more conversational. Use AI writing assistants to generate first drafts of emails, social posts, ad copy, etc., each tuned to the channel. Crucially, instruct the AI with context about the segment or persona. For example, for CIOs in healthcare, have the AI emphasize compliance and security in the message; for Marketing VPs in tech, emphasize revenue growth and ROI. Good AI tools can take a base message and produce variants for different industries or roles at the click of a button. This is how you achieve personalization at scale – maybe you end up with 5 versions of an email for 5 verticals, each with industry-specific proof points, all generated by AI analyzing your case study repository or external data. During this content phase, also plan consistent visuals or branding – ensure your imagery, tone, and branding align across channels (your design team or AI design tools can help repurpose graphics in various sizes, etc.). The consistent thread could even be as simple as using the same campaign tagline across the email subject, LinkedIn post headline, and webinar title, so the prospect recognizes it’s all part of one narrative. Consistency builds recognition – it typically takes multiple touches for a message to stick, and omnichannel means those touches reinforce rather than dilute each other.

4. Implement AI-Driven Orchestration: This is where you set the plan in motion using technology. Modern marketing automation and sales engagement platforms (many infused with AI) will be your workhorses. You’ll configure the campaign flows: for example, if prospect clicks the email link about Product X, then two days later connect on LinkedIn and show them Case Study X; if they don’t click, send a different follow-up email. These branching scenarios can get complex – AI can optimize them by analyzing what’s working in real-time and adjusting sends or recommending changes. One best practice is to utilize AI scoring and triggers: let an AI lead scoring model update the prospect’s score as they engage. Define threshold scores that trigger sales actions. For instance, when a lead hits a score of 80 (based on cumulative engagements), the system might automatically create a task for an SDR to call within 24 hours. 

In our outbound campaigns, we rely on such triggers heavily. Our AI platform might say, “Lead A has opened 3 emails, clicked 2 links, and visited the pricing page – they are hot!” It then pings the assigned rep with a notification like, “Hot lead: call John Doe (Acme Corp) today, here’s a cheat sheet of his recent activities and likely interests.” The result is immediate, informed follow-up – an omnichannel handoff from marketing to sales that feels seamless to the prospect. They often remark, “Wow, your timing is great,” not realizing it was orchestrated by AI behind the scenes.

5. Integrate Human Touchpoints Thoughtfully: Even as we automate, we keep humans in the loop where it counts. Certain interactions simply demand a human touch – complex B2B solutions often require consultation, Q&A, relationship building. The strategy is to let AI do the heavy lifting in the background (data crunching, initial outreach, follow-ups to gauge interest), then insert a human at the moment it’s most impactful. 

For example, no automated email can replace a thoughtful phone call or a face-to-face Zoom meeting when a prospect is deep in evaluation and has specific concerns. So design your program such that AI identifies when a prospect is sales-ready or needs personal engagement, and then route them appropriately. This might involve notifications to reps as described, or even AI scheduling tools that send the prospect a link to book a meeting with a human when they hit certain milestones (like returning to your site multiple times in a week). 

This way, AI and humans work in tandem – AI nurtures and identifies high-probability opportunities, sales reps focus on building relationships and closing sales deals. Companies that master this blend see great results; McKinsey found that organizations combining digital and human interactions appropriately outperform peers, and those investing in digital tools (AI included) are twice as likely to achieve outsized revenue growth (5). In short, let AI augment, not replace, your human sales team. We constantly remind our clients and reps: AI makes you more effective and informed in your conversations, but it’s still your empathy and expertise that wins trust.

6. Launch, Measure, and Optimize Continuously: Once your AI-powered omnichannel program is live, the work is not done – it’s iterative. Establish lead generation KPIs for each channel and stage (email open/click rates, social engagement, meeting conversion rate, sales cycle length, revenue per account, etc.). Dashboards aggregating these (often powered by AI analytics) will show how the program performs. One striking statistic from Omnisend data showed that campaigns using 3 or more channels see a 0.83% order rate vs just 0.14% for single-channel campaigns – a nearly 5× higher success rate (8). Track metrics like these for your own campaigns: are multi-channel touches correlating with higher lead-to-opportunity conversion? Are certain sequences underperforming? AI can help parse the multivariate data to pinpoint what’s driving success or failure. For example, an AI might discover that prospects who receive at least one phone call convert at higher rates than those fully stuck in email – a signal to incorporate more calling at key points. Or it might find your webinar touch isn’t pulling weight – perhaps prospects drop off before that and you need to engage faster. 

Use these insights to refine the program in near-real-time. The beauty is, AI can even auto-optimize some elements (sending time, channel mix) on the fly, but strategic adjustments (like reordering touches or adding a new content piece) will be your team’s call. Embrace an experimentation mindset; A/B test variations with the help of AI where possible. The goal is a learning loop: your omnichannel strategy gets smarter and more effective each quarter.

By following these steps, you effectively build an AI-assisted omnichannel machine that consistently generates and nurtures leads in a personalized way. Omnichannel, when executed with AI, created more opportunities and accelerated their pipeline.

In building your own programs, remember omnichannel doesn’t necessarily mean every channel under the sun. It means the right combination for your audience.

Focus on the channels that your target accounts actually use and that you can execute well. It’s better to deliver an excellent experience on 3 channels than a mediocre one on 6. AI will help you find that sweet spot by analyzing engagement – maybe you’ll find, for example, that email + LinkedIn + retargeting ads are the power trio that drives most conversions, whereas adding SMS didn’t change much. Use that data. In 2025, the possibilities (chatbots, AI-driven video, personalized micro-sites, etc.) are endless, but strategy is about choosing where to play for maximum impact.

Finally, ensure sales and marketing alignment in these programs. Omnichannel blurs the lines between marketing campaigns and sales outreach – it’s truly a team sport. Everyone working on the account should have visibility into what touches have happened or are scheduled. This is where a platform that both SDRs and marketing folks use (like an account engagement platform) is invaluable. It prevents embarrassing overlaps (e.g. a salesperson calling a lead the same day they already got a heavy pitch email from marketing – which we avoid through internal coordination). When we run omnichannel programs for clients, we often act as that bridge between marketing and sales, making sure messaging is coordinated and the baton passes smoothly. If you’re doing this internally, perhaps hold a joint planning session with both teams when mapping the journey and content, to get buy-in and clarity on roles. Nothing kills an omnichannel strategy faster than internal silos not communicating.

In summary, designing an omnichannel marketing program in 2025 means orchestrating a symphony of channels with AI as the conductor – ensuring every instrument plays in harmony, at the right time, personalized to the listener. Next, we’ll cover some overarching best practices to keep in mind as you execute these programs, as well as common pitfalls to avoid.

Best Practices for AI-Personalized Omnichannel Engagement

Crafting a successful AI-powered omnichannel strategy involves more than just tools and data – it requires a smart playbook. Here are some best practices we recommend, distilled from industry research and our own hands-on experience running omnichannel campaigns. Think of these as guiding principles to maximize your results:

• 💡 Put the Customer at the Center of Your Strategy: It’s easy to get carried away with internal goals or cool tech, but always design your omnichannel efforts from the customer’s perspective. Map out their pain points, questions, and decision criteria at each stage, and ensure your outreach provides value accordingly. Personalization should never feel random or “creepy” – it should feel helpful. For example, if a prospect has shown interest in feature A, prioritize content about how feature A solves their problem, rather than pushing unrelated messages. Use AI to analyze what content similar prospects found useful and mirror that. A customer-centric approach also means respecting their time and channel preferences: if data shows a particular contact never engages on one channel, adjust your mix. Always ask, “Is this touchpoint useful to the buyer?” If not, cut it. This builds trust and keeps prospects engaged.
  
• 🎯 Leverage Data (But Keep It Human): Data-driven decision-making is a must. Segment your audiences based on firmographics, behavior, and engagement level, and tailor your strategy to each segment. Use AI analytics to uncover insights – perhaps mid-market tech companies respond differently than enterprise finance companies. Those nuances matter. However, don’t rely solely on algorithms; overlay human judgment. AI might tell you a lead is qualified because they clicked a lot of things, but a salesperson’s quick LinkedIn glance could reveal the prospect is actually an intern, not a decision-maker (and thus not truly qualified). So, blend AI insights with your team’s domain knowledge. When we get an AI-based lead score, our SDRs always do a brief manual sanity check (like verifying titles) before investing a lot of effort. This combination of AI precision and human intuition yields the best results.
  
• 📢 Maintain Consistent Brand Voice Across Channels: As touched on earlier, consistency is king. Buyers should have a cohesive experience whether they’re reading an email, a LinkedIn post, or speaking live with your team. Develop a set of core messaging pillars and stick to them. One practical tip: create a messaging guide for campaigns that is shared with everyone – the marketing folks writing copy, the sales reps doing calls, the social media manager, etc. This ensures everyone sings from the same songbook (e.g. key value props, terminology to use or avoid, tone of voice). AI can assist by suggesting content that aligns with past messaging or highlighting deviations, but it’s up to us to enforce it. Consistent messaging builds credibility – prospects see you as organized and clear in your value. Inconsistent messaging, by contrast, creates confusion or doubt. (Imagine a prospect hearing a sales rep pitch a completely different benefit than what the website advertised – red flag!). A unified voice doesn’t mean monotony; you can adapt tone per channel (maybe slightly more casual on social, more formal in a whitepaper), but the underlying promise and info should align. If you’re not sure, ask: does each touchpoint feel like it’s coming from the same company? If the answer is yes, you’re doing it right.
  
• 🤖 Personalize Every Touch – Big or Small: With AI, there’s no excuse for generic, one-size-fits-all communications. Strive to personalize each interaction, even if only in a small way. Use merge fields for basic things (name, company), but go further – reference that webinar they attended, the specific concern they raised, or something about their industry. AI can pull in such context automatically from CRM notes or publicly available data (e.g., recent news about their company). For instance, our outreach emails often include a line like: “Noticed on LinkedIn that your team is expanding in Europe – congrats. Many of our clients in your space use omnichannel strategies to break into new regions faster…” This kind of personalization shows the prospect this isn’t a blast email – it’s relevant to them. And it pays off: 78% of consumers (and similarly, a high percentage of B2B buyers) say personally relevant content from brands increases their purchase intent (8). On the flip side, be wary of the uncanny valley – overly personal without context can backfire (e.g., don’t mention their kid’s soccer game you found on Facebook – that’s creepy). Stick to professional and firmographic/persona-based personalization. Also, personalize the channel mix: if one account engages heavily on Twitter (X) vs LinkedIn, adjust your social outreach to match. In summary, aim for the prospect to feel like every touchpoint was crafted for them, not the mass market. This is doable at scale now thanks to AI.
  
• ⏱️ Be Timely and Contextual: In omnichannel, timing is everything. A perfectly crafted message delivered at the wrong time can fall flat. Use AI to identify the right moments – maybe it’s immediately after a prospect views your pricing page, or exactly 3 days after they last engaged. Real-time responsiveness (one of our pillars) delights buyers. If someone downloads a whitepaper at 10am, having a related follow-up email in their inbox by noon (even if automated) feels responsive. If they ask a question in a chatbot, having a rep call them that same day with more info can really impress them (we’ve landed meetings exactly this way – the prospect often says, “Wow, you’re quick!”). Speed and relevance signal that you are attentive to their needs. However, balance urgency with thoughtfulness: don’t bombard someone with four different channel touches in the same hour – that comes off as spammy and desperate. Use AI to set pacing rules (e.g. never more than X touches in Y days unless triggered by a very high intent action). Additionally, context matters: reference the context of the last interaction so each touch feels connected. For example, “Following up on the case study I sent last week, I thought you might also find this ROI calculator useful…” – this sort of continuity is only possible if you track and use context. AI sequence tools excel at this, ensuring that if the prospect did A, we send B, but if they did C instead, we pivot. That context makes the outreach feel like a two-way conversation rather than a series of disjointed marketing blasts.
  
• 🔄 Test, Learn, and Iterate Continuously: Even with the best plan, you need to optimize through experimentation. Cultivate a mindset of constant improvement. Try A/B testing subject lines, cold call scripts, ad creatives, send times – and let AI crunch the results to declare winners. Some AI platforms will even auto-test and iterate (for example, testing multiple email subject lines and then using the one that performs best). Embrace those capabilities. Keep an eye on cohort analysis: e.g., maybe the Q3 campaign performed 15% better than Q2 after you tweaked the channel order – figure out why with AI’s help and codify the learning. Also listen to your sales team’s qualitative feedback – perhaps leads from a certain channel were all high quality, whereas another channel produced a lot of noise. Use that feedback loop to adjust resource allocation. Best-in-class teams run retrospectives after major campaigns to gather insights and feed them into future planning. In short, treat your omnichannel strategy as a living, evolving program. The market will change, buyer preferences will shift (who knows what new platform might emerge in 2026), and your competitors will react. A culture of testing and learning ensures you stay ahead. One metric to watch is ROI per channel or per sequence. If you see that companies with advanced omnichannel capabilities enjoy ~9.5% annual revenue growth versus 3-4% for less mature peers (8), it’s likely because they are continually fine-tuning to get more revenue impact. Make sure you’re on the winning side of that equation by never settling and always iterating.
  
• 🧑‍💼 Don’t Automate the Relationship (Keep the Human Element): Perhaps most importantly, remember that B2B sales is ultimately human-to-human. AI and automation should enhance the relationship, not replace it. Use technology to eliminate trivial tasks and provide intelligence, but have your team spend more time in genuine conversations as a result. 

When a prospect is ready to talk or has a nuanced question, ensure a knowledgeable human is there – whether it’s an SDR, an AE, a solutions engineer, etc. Train your team to use the insights AI provides to hold better discussions. For instance, if AI shows that a prospect has repeatedly checked out your “security features” page, the rep on the call should proactively address security in the conversation. 

That kind of attentiveness can wow the prospect: “It’s like you read my mind – I was going to ask about data security and you already covered it.” It’s not mind reading; it’s AI informing a human to be more relevant. Aim for that synergy. Also, be transparent where appropriate – buyers appreciate authenticity. If you send an automated email, it’s okay that it’s automated as long as it’s useful. 

But if a buyer responds and wants to engage, have a person step in. We train our AI sales agents to smoothly hand off to a human agent if the question becomes complex or the prospect requests it. The human touch builds trust and relationships in ways that even the most advanced AI cannot (at least not yet!). 

And relationships are the heart of B2B deals – the old adage “people buy from people” still holds true, even if those people are augmented by AI. So, ensure your strategy includes plenty of room for genuine human interaction – whether it’s a thoughtful email written by a person when needed, a phone call to check in, or an in-person meeting for enterprise deals. Your omnichannel program should empower these moments, not eliminate them.

By following these best practices, you set yourself up to execute an omnichannel strategy that is strategic, customer-centric, and agile. We’ve seen firsthand that when companies get this right, the results are outstanding: higher engagement, faster pipeline velocity, and improved win rates. One client of ours who implemented these principles saw their email response rates jump by 40% and their opportunity creation double within a quarter, because prospects were responding so much better to the tailored, well-timed outreach.

Remember, the technology is an enabler – excellence still requires smart strategy and flawless execution. It’s the combination of the right content, delivered to the right person, at the right time, through the right channel, in the right way (personalized) that produces magic. AI helps us nail each of those “rights” consistently. But we, as marketers and sellers, define what “right” looks like through clear planning and creative insight. Keep that partnership in mind: AI + Human is the winning formula for 2025.

Next, we’ll look at the other side of the coin: the challenges and pitfalls that come with implementing an AI-powered omnichannel strategy, and how to overcome them. No journey is without hurdles – being prepared for them can mean the difference between success and frustration.

Overcoming Challenges in AI-Powered Omnichannel Strategy

Implementing an AI-driven omnichannel approach isn’t all smooth sailing. As with any transformative strategy, there are challenges and potential pitfalls to navigate. In our journey, we’ve encountered many of these and helped our clients tackle them head-on. Here are the most common challenges B2B organizations face with omnichannel personalization (and how to overcome them):

1. Data Silos and Integration Headaches: Many companies find that their customer data is scattered across systems – perhaps marketing has data in an automation platform, sales has separate data in CRM, and support has its own database. These silos are enemy #1 of omnichannel execution, because they lead to disjointed outreach (e.g., marketing emails a lead not knowing that sales already spoke with them). 

Moreover, AI models are only as good as the data fed into them (“garbage in, garbage out”). If data is fragmented or inconsistent, personalization efforts can misfire. 

In fact, 57% of senior marketing executives cite inconsistent or inaccurate data as a major challenge in providing personalized experiences (8). Solution: Invest in integration early. This could mean implementing a customer data platform (CDP) or using integration middleware to sync systems.

Also, prioritize data governance – establish processes for data entry, cleaning, and updating so that everyone trusts the data. At Martal, before rolling out any AI initiative for a client, we often spend time auditing and consolidating their lead and account data. It’s not glamorous, but it’s critical. 

We also use AI tools that help merge duplicate records and enrich missing fields (for example, automatically appending industry or company size data from external sources to each account). 

Breaking down data silos might involve organizational change too – encourage cross-department data sharing, and perhaps form a data team or council responsible for a unified view of the customer. The effort pays off when your omnichannel system runs on a single source of truth for customer information.

2. Privacy, Compliance, and Ethical Use of AI: With great data comes great responsibility. The more personalized and data-driven your outreach, the more you must be mindful of privacy laws and customer comfort. Regulations like GDPR and CCPA give consumers (and B2B contacts) rights over their data and how it’s used. The last thing you want is to violate those, or even simply to spook prospects by over-personalizing using data they didn’t explicitly give you. 

A significant portion of marketers worry about this – roughly 46% of B2B marketers say data privacy and security is their biggest concern when implementing generative AI in marketing (according to industry surveys)`(11). Solution: Make privacy compliance a foundational aspect of your strategy. Ensure you have consent for communications (honor opt-outs diligently across all channels – nothing kills trust like unsubscribing from emails only to get the same pitch via SMS). Be transparent in your privacy policy about the data you collect and how you use AI (some companies even proudly say “we use AI to better serve you, but your data is safe and never sold,” etc.).

From an execution standpoint, configure your AI tools in a privacy-conscious way – for instance, if you use third-party intent data or web tracking, set reasonable limits (don’t have your sales reps referencing very specific browsing behavior that the prospect wouldn’t expect you to know; instead use it internally to guide outreach). Consider anonymizing data in analysis phases. 

Also, keep your data security tight – AI systems often require centralizing data, so invest in cybersecurity and access controls to protect that treasure trove. By respecting privacy and being ethical in personalization (e.g., focusing on professional context data and not crossing personal boundaries), you build trust rather than erode it. We often err on the side of caution: just because we can use a certain data point in outreach doesn’t always mean we should. Trust your gut – if an AI-suggested personalization feels too intrusive, dial it back. Long-term customer relationships are built on respect.

3. Tool Overload and Integration Complexity: The martech and salestech landscape is exploding with AI-powered tools. It’s easy to end up with a dozen different platforms – one for email automation, one for social selling, one for chatbots, one for analytics, etc. Juggling too many tools can create chaos, drive up costs, and actually re-silo your data. We’ve met teams overwhelmed by dashboards and logins, struggling to get a cohesive view or workflow. 

Solution: Consolidate and integrate tech wherever possible. Seek platforms that offer end-to-end capabilities or at least play nicely together. For example, many CRM platforms now have built-in AI features and omnichannel plugins – leveraging those might be simpler than stitching together many point solutions. If you do use specialized tools, ensure they integrate via APIs into your central system (so data flows freely). It helps to have an architecture diagram of how all your tools connect. 

If something doesn’t connect – that’s a red flag. Simplify your stack: it’s better to fully utilize a few robust, lead generation tools than to have a fragmented stack where each tool is 10% used. We advise clients to audit their tools annually and cut redundancies. Additionally, assign someone (like a revenue operations manager) to own tool integration and data flow – this isn’t a set-and-forget thing; it needs ongoing maintenance as tools update or as you add new channels. 

One more point: provide training. A fancy AI tool is wasted if your team isn’t comfortable using it. We’ve seen companies invest in AI software that reps never log into because they weren’t properly onboarded or it was too complex. 

So, part of overcoming tool overload is change management: choose user-friendly solutions and train your people to wield them effectively (soliciting their feedback on what actually helps vs what is burdensome).

4. Content Volume and Quality Concerns: Omnichannel personalization demands a lot of content – tailored emails, social posts, blog articles, videos, whitepapers for different industries, chatbot scripts, and more. Creating all this can strain marketing teams, and there’s a risk that quality dips in the rush to produce quantity. AI can help generate content, but AI-generated content needs oversight too (it might lack the perfect tone, or worse, occasionally spit out incorrect or biased information if not carefully checked). The challenge is scaling content production without sacrificing accuracy or brand voice. 

Solution: Develop a content strategy and workflow that leverages AI as an assistant, not an autopilot. For example, use AI to draft pieces of content, but have subject matter experts review and refine them. Create templates for common content types to ensure consistency (AI can fill in the blanks in the template for each persona). Maintain a content library – as you create personalized assets, catalogue them; you might reuse a case study snippet across channels to maintain coherence. Also consider modular content: create core blocks (like a paragraph about each key benefit) and let AI or your marketing ops assemble them in different orders for different contexts. This ensures each piece is well-crafted and reviewed, but you still get variety through recombination. 

Another tip: prioritize key content first (e.g., the main nurture emails or the core sales deck) and get those right; secondary content like an extra tweet or an extra infographic can follow. In terms of quality control, implement a review process: for instance, any AI-written email might be set to “approved” by a manager or content strategist before it’s unleashed at scale. Over time, as the AI learns your preferences, you might gain trust to automate more. But initially, keep a human in the loop for quality. Also monitor performance metrics of content: if a certain whitepaper has low engagement, maybe the content isn’t resonating – revise it. Quality over quantity should still guide you, even though you need a certain volume. One high-quality personalized touch will outperform three low-quality spammy touches. In summary, aim for efficient content creation, not just mindless cranking. AI will give you efficiency, you ensure the effectiveness.

5. Organizational Silos and Skill Gaps: Omnichannel AI initiatives often reveal internal organizational issues. Marketing, sales, customer success – all need to work together closely, but traditionally they might be siloed with different KPIs and processes. Additionally, your team may lack certain skills – whether it’s data analysis, AI model understanding, or simply operating the new tools. Rolling out an AI omnichannel strategy can falter if people resist adopting new ways or don’t know how to use the tech. 

Solution: Drive alignment and invest in skill development. First, get buy-in from all stakeholders by communicating the vision and benefits clearly. Show each team how this strategy helps them (e.g., sales will get warmer leads, marketing will see better conversion on their campaigns, etc.). Consider forming a cross-functional “omnichannel task force” that meets regularly to coordinate messaging and share insights from each team’s perspective. This breaks down silos. 

Also, celebrate joint successes – for example, when a personalized campaign goes well, highlight both marketing’s creative and sales’ execution in the win. On the skill side, assess what gaps you have. Do you need to educate your team on how AI algorithms make decisions, so they trust the lead scores? Do your content creators need training on prompt engineering to get the best outputs from AI writing tools? Provide workshops, bring in experts, or use online courses to upskill your staff. 

We’ve done internal AI training sessions at Martal to ensure our team knows how to interpret and leverage AI suggestions (e.g., how to use our AI platform’s dashboard to prioritize their day). Additionally, sometimes hiring or role evolution is needed – maybe you add a Marketing Operations or Sales Operations role focused on the tech and data. Or you task certain team members to act as “AI champions” who become power-users and help others. The goal is to create a culture that embraces data and AI in day-to-day decision making, rather than seeing it as a black box or a threat. When your BDRs, AEs, marketers all trust and use the system collaboratively, you unlock its full potential. As an anecdote: one client’s sales team initially distrusted the AI lead scores (preferring their gut instincts). Over time, by showing them evidence of success (and tweaking the model with their feedback), we got their buy-in. Eventually those reps were saying “I won’t start my day until I check the AI recommendations,” because it had proven its worth. That mindset shift is crucial, and it comes from inclusion and education.

6. Over-Automation and Losing the Human Touch: This is a subtle but serious pitfall. With powerful AI automation, there’s a temptation to automate everything and let the system run on autopilot. But as we stressed in best practices, too much automation can make your outreach feel robotic and impersonal, or you might miss nuances a human would catch. Customers can sense when they’re stuck in an automated loop versus when someone actually cares. 

Solution: Be deliberate about where human intervention occurs. Design your workflows with checkpoints that require a human decision or input, especially for high-value accounts or deals at critical stages. For example, in our sequences, if an account reaches a certain engagement level, we often switch from automated emails to one-to-one handcrafted emails from a rep. We also schedule periodic manual call blitzes to supplement automated touches – hearing a human voice can do wonders in re-engaging a cold lead. 

Leverage AI to do the grunt work (suggest who to call, provide a call script outline based on data, etc.), but then let the human do the talking authentically. Another important area: customer support or issue resolution should have easy human escalation. If a prospect or client expresses frustration or complexity, don’t make them talk to a bot through 20 prompts – get a human on it. In essence, use automation to enhance personalization, not to replace it. 

When in doubt, err on the side of adding a personal touch. We find that a surprise personal gesture in an otherwise automated flow can really set you apart. For instance, one of our strategies for key enterprise accounts is sending a personalized video message from the account manager after a sequence of automated emails. The AI helps identify the opportune moment and maybe even drafts a script, but the video is recorded by a human speaking directly to that account’s situation. It’s automated to the extent that we plan it and trigger it, but the content is very human. Things like that ensure you don’t lose the connection. And as mentioned, monitor for any backlash – if you get signs that prospects feel like they’re just talking to a machine, recalibrate. Regularly solicit feedback from some friendly customers about their experience interacting with your sales/marketing – if they note it felt impersonal at any point, dig into that and adjust.

By anticipating these challenges and proactively addressing them, you can dramatically increase your likelihood of success. It’s much better to bake in solutions from the start than to scramble later after a misstep. For example, investing in data integration upfront might be weeks of work, but it’s far better than launching a program only to have a major embarrassment because sales called a CEO by the wrong name due to a CRM mismatch (yes, we’ve seen that happen elsewhere!).

One more challenge worth noting is measuring ROI across channels – attributing success in an omnichannel world can be tough (was it the email that made the sale or the webinar or the call? It’s all interconnected). The key is to adopt a multi-touch attribution mindset and look at overall lift rather than single-touch attribution. 

Utilize your analytics (and AI attribution models if available) to understand the holistic impact. Companies that persist with only last-click or single-touch attribution may undervalue important top-of-funnel touches. Our approach is to measure everything and then use AI models to assign credit proportionally. That said, in the end, the most important metric is overall revenue and pipeline growth. 

The good news: a well-run omnichannel program tends to show clear boosts in those macro metrics. For instance, businesses with effective omnichannel customer engagement report significantly higher year-over-year revenue growth (as noted, ~9.5% vs 3.4% for less effective) (8), and even reduced costs per contact by integrating channels (8). Those broad outcomes justify the investment and effort many times over.

To sum up this section, every innovation comes with hurdles – but none of the challenges above are insurmountable. With careful planning, a good team, and the right mindset, you can avoid the common pitfalls that trip others up. In our experience, transparency and customer-focus guide you well: be transparent internally about how AI works and how data is used (to get buy-in and ethical consistency), and stay focused on delivering a great customer experience above all. If you do that, technology issues, content issues, etc., will be resolved in service of that goal.

Having covered the “how” and “watch out for,” let’s wrap up with a look to the future and a quick recap. Then, we’ll present a brief FAQ addressing some common questions about B2B omnichannel strategy (the pillars, examples, terminology), before concluding with how Martal can help you put all this into action.

Conclusion: Personalizing Sales at Scale – The New Competitive Advantage

The world of B2B sales and marketing in 2025 is undoubtedly more complex than ever – multiple channels, empowered buyers, and the rise of AI have changed the game. Yet, with complexity comes opportunity. Those organizations that harness AI-powered omnichannel strategies are finding they can deliver personalization at scale, turning what was once a pipe dream (treating every prospect like a VIP) into a daily reality. This capability is fast becoming the new competitive advantage. It’s no longer enough to have a great product or service; the experience you provide around that offering can be the difference between winning or losing the deal. And a cohesive, personalized omnichannel experience is exactly what today’s decision-makers respond to.

Let’s quickly recap the journey we’ve taken in this blog:

• We started by understanding the modern B2B buyer – digitally savvy, expecting frictionless, relevant interactions across many channels. The data is unequivocal: buyers are omnichannel, so sellers must be too. Companies that embraced this shift are pulling ahead in customer retention, engagement, and growth (8) (7).
  
• We saw that AI is the catalyst that makes omnichannel personalization scalable. From analyzing big data for insights to automating individualized content and optimizing outreach timing, AI has infused every step of the sales process with greater intelligence and efficiency. The broad adoption rates underscore that AI isn’t experimental fluff – it’s driving tangible business outcomes.
  
• We outlined how to design AI-powered omnichannel programs, anchored on pillars like unified data, integrated channels, consistent messaging, and real-time action. We walked through implementation steps – from data centralization and journey mapping to content creation and continuous optimization. Key takeaway: success lies in harmonizing tools and teams to work in unison, always with the customer’s journey in mind.
  
• We listed best practices, emphasizing things like customer-centricity, data leverage with human oversight, consistent brand voice, thorough personalization, timely responsiveness, iterative improvement, and maintaining the human element. These are the habits of highly effective omnichannel teams.
  
• We tackled challenges, from data silos and privacy concerns to tool overload and skill gaps, offering solutions to each. Forewarned is forearmed – knowing these pitfalls means you can avoid them and navigate smoothly.

Stepping back, one thing is clear: executing an AI-powered B2B omnichannel strategy is a significant undertaking, but one that can reap significant rewards. When done right, it creates a flywheel effect – better engagement leads to more pipeline, which leads to more wins and revenue, which provides resources to further invest in personalization and customer experience, which in turn drives even better engagement and loyalty, and so on. In competitive markets, this can be a game-changer. We often ask clients, “How many of your competitors do you think are doing this well?” The answer is usually “Not many.” That’s the opportunity – to differentiate not just on what you sell, but how you sell.

At Martal, we’ve built our business around helping companies achieve exactly this kind of differentiated sales approach. We’ve spent over a decade refining our omnichannel outreach strategies and methods, layering in AI capabilities as they’ve matured, to ensure our clients can connect with their ideal buyers in a personalized, impactful way. 

We’ve seen startup clients go from zero to robust sales pipelines in new markets using our strategies, and established firms reinvigorate stalled pipelines by adopting an omnichannel refresh. It’s incredibly satisfying to see the lightbulb go on when a client realizes they can have the scale of automation and the intimacy of personal touch – they don’t have to choose one or the other anymore.

As we conclude, you might be thinking: this sounds great, but it’s a lot to execute. It’s true – building an AI-driven omnichannel engine requires expertise, technology, and bandwidth. 

That’s where partnering can make sense. Rather than reinventing the wheel internally, many companies opt to partner with lead generation specialists like Martal who already have the infrastructure, know-how, and teams in place. We offer what we call Sales-as-a-Service – effectively delivering you a turnkey omnichannel SDR team on demand.

Whether you need to fill the top of the funnel or nurture leads until they’re sales-ready, our approach ensures no lead falls through the cracks – every prospect gets timely, relevant touches on the channels they prefer. And we do it at scale: our clients benefit from having an entire outsourced SDR team without the overhead of hiring and managing one. 

In short, we make omnichannel personalization easy for you, and drive the results you need – more meetings, more opportunities, and more revenue. Let us at Martal show you what a modern omnichannel strategy can do for your sales. 🚀 

Interested in learning more? Reach out to schedule a consultation or visit our website.

Thank you for reading this deep dive. We hope it provided valuable insights and actionable ideas to elevate your sales and marketing strategy in 2025 and beyond. 

The technology and techniques will continue to evolve – but staying focused on delivering a seamless, personalized experience for your buyers will remain the North Star. Embrace the change, empower your team with AI and data, and you’ll be well on your way to B2B sales excellence in the omnichannel age.

References

1. Gartner
2. Forrester – Predictions
3. Mckinsey & Company
4. Cisco
5. McKinsey & Co. – B2B Omnichannel Opportunity
6. Digital Commerce 360
7. Reputation Ink
8. UniformMarket
9. McKinsey & Co. – The State of AI
10. Twilio Segment
11. ViB Tech
12. Landbase
13. Adam Connell