Sales organizations in the consumer goods industry are at a technological crossroads. Those investing in clean data, user-friendly systems, and AI capabilities are opening new frontiers of growth, while others risk stagnation.
Executive Summary
The consumer packaged goods (CPG) industry is facing a critical juncture. Over the next 12–18 months, sales organizations will diverge into two distinct categories:
- AI-ready teams, equipped with intuitive sales tools, high-quality CRM data, and advanced AI capabilities, achieving superior sales performance, precise forecasting, and accelerated growth.
- Teams left behind, hindered by fragmented, low-quality CRM data, minimal system adoption, and reluctance toward AI adoption, facing inaccurate forecasts, declining rep productivity, and stagnant revenues.
Currently, most CPG companies recognize AI's transformative potential—71% have adopted AI in some business function—but few have fully scaled it. The difference between leaders and laggards hinges on CRM data quality and sales rep adoption of technology, both of which directly influence the effectiveness of AI solutions.
Organizations proactively investing in clean, integrated CRM data are seeing significant, measurable benefits, including:
- 32% improvement in sales forecast accuracy, directly translating into higher revenues.
- Up to 26% gains in rep productivity due to streamlined workflows and mobile CRM usage.
- Higher CRM adoption rates (>90%), driven by intuitive interfaces and automation.
- Personalized customer outreach leveraging AI-driven analytics, increasing retailer engagement by as much as 30%.
In contrast, teams neglecting data quality and usability face critical risks:
- Only 35% trust their CRM data fully, resulting in forecasting errors and reactive decision-making.
- Reps spend over 70% of their time on non-selling tasks, reducing sales productivity and increasing turnover.
- Stagnation in AI pilots and limited scalability, widening competitive gaps over time.
This divergence is already visible among major CPG players. For instance, Coca-Cola's strategic AI investments have driven incremental sales and protected market share in challenging conditions, whereas competitors slower to leverage AI suffered disproportionate volume declines.
The choice for sales leaders is clear and urgent: prioritize creating a rep-friendly, data-rich CRM environment that fully leverages AI, or risk falling irreversibly behind competitors who are already moving swiftly ahead. The competitive advantage AI offers today will rapidly compound, making immediate action not just beneficial, but essential.
Introduction: A Divergence in the CPG Sales Landscape
The consumer packaged goods (CPG) industry is undergoing a digital transformation that is reshaping how sales teams operate. In particular, large CPG companies (>$100M revenue) are increasingly leveraging AI-powered tools and CRM platforms like Salesforce to gain a competitive edge. Recent surveys confirm that AI adoption in CPG is accelerating:
- 71% of CPG leaders in a 2024 McKinsey poll said they use AI in at least one business function (up from 42% a year prior)
- A 2025 study of 200 U.S. CPG executives found 66% are implementing or scaling generative AI across their operations.
These forward-looking firms view AI as “a growth imperative, competitive moat, and foundational capability for winning in 2025 and beyond.”
However, not all sales organizations are keeping pace. No CPG player has fully scaled AI across the enterprise yet, and many remain stuck with legacy processes.
A stark contrast is emerging between two futures for sales teams:
- Those ready for AI-driven growth, equipped with rep-friendly systems, clean data, and advanced analytics.
- Those left behind, struggling with low user adoption, poor data quality, and outdated tools.
This article delves into each scenario with verified facts, case studies, and expert insights from 2023–2025, focusing on North American CPG companies and their use of Salesforce and AI. We'll see why clean CRM data, intuitive design, and AI adoption are critical, and how organizations that embrace these elements are outperforming those that don’t.
The AI-Driven Growth Path: Rep-Friendy, Data-Driven, and AI-Augmented
Leading CPG sales teams that are “AI-ready” share a few key characteristics. They have:
- designed CRM tools with the sales rep experience in mind,
- invested in data quality and integration, and
- embedded AI into their sales process to augment human effort.
These foundations enable superior performance in forecasting, efficiency, and revenue growth.
Sales Tools Designed for Reps (High Adoption and Productivity)
AI-ready sales teams distinguish themselves through high user adoption of their CRM and sales tools, achieved by making these tools as rep-friendly as possible.
This matters because even the best system is useless if salespeople won’t use it. Top organizations invest in service design, automation, and training to ensure the CRM works for reps, not just as a monitoring tool for management. As a result, their salespeople actively engage with the platform, inputting rich data and leveraging insights in return.
Ease of use and efficiency are critical to rep adoption.
Eliminating wasted time on data entry
Notably, a significant pain point in many sales orgs is the time reps spend on administrative tasks and data entry.
Industry research shows that the average salesperson spends only about 28% of their week actively selling, with the rest consumed by non-selling tasks like updating CRM records, managing emails, and generating reports. Leading teams are reversing this trend by simplifying workflows and offloading grunt work to technology.
For instance, mobile CRM access and automation can cut down data entry burdens. 32% of reps report spending over an hour per day on manual data entry, often resulting in incomplete data. High-performing organizations respond by automating data capture and streamlining interfaces, which “keeps the data clean and complete, so you can use advanced CRM features like analytics to make better decisions.”
In practice, this might include AI-powered tools that automatically log call notes or populate fields, preventing the tedium and errors of manual entry.
Mobile-friendliness
Making CRM accessible on the go is another rep-friendly move that pays dividends. Sales teams that rolled out mobile CRM apps (so reps can update info from anywhere, e.g. right after a store visit) saw data quality and productivity improve. In fact, 82% of employees with mobile CRM access say it improves data quality in the system, and providing mobile access has been shown to increase user productivity by ~15–26%.
These gains come from enabling reps to input and retrieve data in real time without delays or forgetting details. It’s no surprise, then, that companies prioritizing rep experience achieve higher CRM adoption rates – roughly 60% of firms have >90% adoption when they focus on user needs, whereas lack of user-friendliness and understanding is a top cause (22%) of CRM implementation problems.
Tech stack consolidation
Beyond mobile access, consolidating the sales tech stack is another strategy leaders use to simplify reps’ lives.
Many sales teams have accumulated a patchwork of tools (for CRM, forecasting, quotes, communication, etc.), which can overwhelm users. 66% of sales reps say they’re overwhelmed by the number of tools they use. The best sales organizations address this by integrating or trimming tools so that the CRM platform (e.g. Salesforce) serves as the central hub.
In fact, 9 out of 10 sales organizations plan to consolidate their tech stack within 12 months (as of late 2022) to create efficiencies and “give sellers their time back.” This consolidation means fewer logins and better data flow between systems, making it easier for reps to find information and update one system rather than several.
All these efforts contribute to a virtuous cycle: an intuitive, helpful CRM encourages more frequent and thorough use by reps, which in turn yields cleaner data and better analytics to guide the sales team.
Leadership and culture also play a role in adoption
High-performing CPG companies cultivate a culture where using data and CRM tools is ingrained in the sales process (often through coaching and incentives).
As Kerri Linsenbigler, Executive Editor for The CRO Club, puts it, “if the platform isn’t user-friendly or a salesperson doesn’t understand the benefits of CRM, it simply won’t be used.”
Thus, top firms not only deploy better technology but also communicate its value and provide ongoing training. This aligns with findings that as of 2025, many CPG companies are establishing AI and data “Centers of Excellence” to upskill employees and embed AI as a daily co-pilot across teams.
By investing in their people, and not just tech, these organizations ensure that tools are adopted and trusted. It’s a strategy that pays off: companies that prioritize the seller experience (e.g. reducing busywork, providing coaching) see lower turnover and higher quota attainment, even in challenging environments.
Clean, Unified Data as a Foundation
AI-driven sales growth requires clean, unified data.
High-performing teams treat their CRM as a single source of truth for customer and sales information, ensuring data is accurate, complete, and up-to-date. This is crucial because AI is only as good as the data it’s trained on. As one industry report bluntly put it: “You can’t scale AI without first fixing your data house.” Organizations with mature data management practices are far better positioned to leverage AI effectively. In fact, business leaders with high data maturity are 2× more likely than low-maturity peers to have the quality data needed to use AI effectively.
The benefits of clean CRM data are tangible. Companies that successfully integrate and cleanse their data see significant improvements in forecasting and decision-making.
For example, one study showed that a well-utilized CRM can improve sales forecast accuracy by ~32%, which in turn can boost revenue by 29%. With reliable data, sales leaders gain a clearer picture of pipeline health and future revenue, leading to more proactive and precise strategies.
CPG executives overwhelmingly recognize this: 93% say they can effectively use data to optimize prices/promotions, and 92% use data to drive profitability. These data-driven practices allow AI algorithms to identify patterns (such as which stores or segments to target) and produce trusted recommendations.
By contrast, teams with messy, siloed data struggle to get value from even the most powerful AI tools, as bad inputs yield bad outputs.
Leading CPG firms are backing their data ambitions with resources. Nearly 88% of CPG companies have budget allocated to AI initiatives in 2025, indicating they are moving from pilot projects to serious, data-fueled deployments.
They are also drawing on more data sources than ever. One report from Salesforce noted consumer goods companies now use an average of 18 data sources to inform customer engagement, up from 10 sources just two years prior.
The commitment to data quality and breadth provides a rich fuel for AI models and analytics. It’s telling that data-leading companies see an 89% improvement in customer acquisition/retention and a 41% faster time-to-market on average, highlighting how clean data translates into real business agility.
AI Embedded in the Sales Process for Insight and Efficiency
Perhaps the clearest hallmark of an AI-driven sales team is that artificial intelligence is deeply embedded in their sales process, from planning and forecasting to day-to-day execution.
These teams go beyond using CRM as a database. They leverage AI and machine learning models (often built into platforms like Salesforce Einstein or add-on analytics) to gain predictive insights and automate routine tasks. Crucially, their focus with this new wave of AI is on driving growth and differentiation, not just cutting costs.
Sales forecasting and pipeline management
Advanced sales teams use AI to analyze historical sales data, current pipeline activity, and external signals to forecast more accurately and detect risk in deals. The payoff is greater confidence in projections and the ability to course-correct early.
For example, consumer goods companies using AI for demand or sales forecasting have seen measurable improvements. Nestlé, in one case study, used AI-driven demand forecasts to increase prediction accuracy by about 9%, translating into multi-million dollar savings in inventory costs (by reducing overstock and stockouts).
Similarly, global research indicates companies that harness AI and analytics for forecasting and opportunity scoring can significantly outperform those relying on spreadsheets or gut feel. As noted earlier, forecast accuracy can improve by roughly one-third with a robust CRM+AI approach.
Better forecasts not only drive efficiency but also revenue: when you know where to focus, you close more deals.
Pricing, promotions, and personalization
Beyond forecasting, AI is helping CPG sales teams optimize pricing, promotions, and personalized offers in real time.
A notable case is The Coca-Cola Company’s recent experiments with AI to assist their sales and commercial teams. Coca-Cola developed an AI-driven tool for price-pack optimization and personalized retailer outreach. Early results have been promising: in a 2024 pilot, AI-generated suggestions (like tailored product recommendations sent to retail partners) boosted uptake – “retailers who receive the AI-driven messages are over 30% more likely to purchase the recommended SKUs,” leading to incremental sales for both Coca-Cola and its retailers.
This shows how AI can uncover upsell opportunities and drive volume even in a challenging market. Coca-Cola’s CEO James Quincey attributed part of their stronger-than-expected Q2 2024 results to “leveraging digital and tech-enabled innovations” including AI, which helped them beat forecasts despite tough economic conditions.
By contrast, a major competitor (PepsiCo) saw larger volume declines at the same time, highlighting how strategic use of AI and data can create a performance gap.
Automation and augmentation
Sales teams prepared for AI-driven growth also use AI to automate and augment various selling tasks.
This can range from AI chatbots handling routine customer inquiries, to recommendation engines suggesting the next best action for a rep, to generative AI drafting a first version of a sales proposal or email.
In CPG, many use cases are emerging for “agentic AI,” such as autonomous sales outreach, auto-generated promo calendars, and AI-assisted retailer negotiations.
Procter & Gamble’s experiments with generative AI offer a window into the potential: in a controlled study with 776 employees, P&G found that teams using generative AI (as a “team member” for brainstorming and problem-solving) completed tasks about 12% faster than teams without AI. The AI helped employees produce high-quality work and even improved team morale by taking on some workload and sparking creativity.
While that study spanned R&D and commercial roles, the implications for sales teams are clear: AI can accelerate prep work and insights, allowing human reps to spend more time on high-value activities like building relationships and negotiating deals.
Revenue growth
Notably, the impact on revenue growth is already evident for sales organizations embracing AI.
According to Salesforce’s latest “State of Sales” research, 83% of sales teams using AI have met or exceeded their revenue targets, compared to only 66% of teams not using AI. In other words, AI-equipped teams were far more likely to grow sales, underscoring that effective AI use is translating to bottom-line results.
Sales professionals report that AI is delivering benefits such as improved data quality, higher productivity, and better personalization for customers. These advantages compound over time: leaders describe a flywheel effect where the more they use AI, the better it performs, as algorithms learn from new data and users grow more adept at leveraging insights.
Crucially, AI is seen not as a replacement for sales reps but as a “co-pilot” that collaborates with humans. High-performing teams invest in training their staff to work alongside AI and to trust (but verify) AI-driven recommendations. By building user trust and alignment with business goals, they ensure technology adoption actually yields the intended outcomes.
Case in point: Several leading CPG firms are now all-in on scaling AI. PepsiCo, for example, announced in 2025 a multi-year partnership with AWS to build an “agentic AI-first” platform across its global sales and operations, aiming to infuse AI into everything from supply chain to go-to-market strategies. The fact that even traditionally conservative CPG companies are making such moves underscores the industry’s recognition that AI is a competitive differentiator.
As Accenture’s report with Salesforce emphasized, companies deploying AI for growth (personalized promotions, dynamic pricing, etc.) will set themselves apart, while those that stick to old playbooks risk falling behind.
In summary, the AI-ready sales teams are leveraging data and AI at every turn and reaping the rewards through better forecasts, smarter selling, and stronger growth.
The “Left Behind” Path: Stagnation Through Low Adoption, Poor Data, and Tech Reluctance
In stark contrast to the AI-driven organizations above, many sales teams are struggling, essentially “left behind” in this new era.
These teams often suffer from low CRM adoption among reps, dirty or siloed data, and a minimal use of AI. The result is inefficient processes, missed opportunities, and eroding performance relative to more advanced competitors. In this section, we explore the common pitfalls and consequences for those lagging in the AI and digital transformation of sales.
Low Adoption and User Frustration
The hallmark of left-behind sales organizations is poor CRM adoption and general resistance to new tools among their sales reps.
Whereas AI-ready teams have high engagement with their systems, laggards often see their CRM as an “unloved” tool, used begrudgingly or inconsistently, which leads to shallow data and minimal benefit. There are several reasons for low adoption, often rooted in the systems being not user-friendly, not well understood, or not embedded in rep workflows.
One telling statistic: 22% of sales professionals are unsure about what a CRM even does.
This points to a failure in change management and training. If reps haven’t been shown how the CRM can help them (and if the UI is clunky), they understandably won’t use it diligently. Additionally, many reps feel that CRM software is designed for their bosses (for reporting and micromanagement) rather than for them. When the user experience is cumbersome (for example, having to fill out dozens of fields or navigate a slow interface), salespeople will find ways to avoid it.
It’s noted that manual data entry is so tedious that reps often skip it or leave fields blank. This behavior is rampant in low-adoption environments: a rep might hastily input only a deal’s basics, leaving out valuable context, or update stages infrequently. The outcome is a half-baked CRM dataset that no one fully trusts (connecting forward to the data quality issue).
Furthermore, tool overload and lack of integration plague these teams.
While leading organizations consolidate apps, left-behind teams might require reps to use separate disconnected tools (one for CRM, another for order entry, another for tracking their KPIs, etc.). This not only wastes time but frustrates users.
This problem is evidenced by the high percentage of reps feeling overwhelmed by too many systems. Without a unified platform, reps end up spending time copying data from one place to another or repeatedly logging into different interfaces. Such inefficiencies directly cut into selling time and motivation. It’s little wonder that in many lagging sales forces, reps spend 70%+ of their time on non-selling tasks. As noted earlier, the typical seller only spends 28% on actual selling, and at worse-off companies it can be even less. This leaves sellers with less time to meet customers or close deals, contributing to poorer performance.
Severe consequences of low adoption
If reps don’t log their activities and customer interactions, sales managers have no visibility into pipeline health, and forecasting becomes guesswork.
It also means leadership lacks data to coach the team or identify bottlenecks.
Perhaps most critically, failing to digitize and standardize processes means sales teams can’t benefit from AI or advanced analytics, because you can't optimize what isn’t measured.
For example, an AI model can’t accurately score leads or opportunities if half the opportunities are not even entered into the system or key fields (like industry, deal size, last contact) are missing. This is why many underperforming teams remain stuck with gut-driven decision making, which is inherently less reliable. Indeed, sales leaders in such organizations often express that they rely on “tribal knowledge” or the instincts of a few veteran executives, rather than data-driven insight — a stark contrast to data-informed cultures of AI leaders.
There is also a human toll: sales rep turnover tends to be higher in poorly enabled environments.
A Salesforce survey noted sales teams averaged 25% annual turnover, and one driver is that reps feel they are not set up for success (spending days on admin and fighting internal systems instead of selling). Left-behind teams often struggle to retain talent, and their managers find it hard to ramp up new hires quickly because processes aren’t streamlined or documented in the CRM.
In sum, low adoption and user frustration create a negative feedback loop that keeps these organizations trailing in productivity and results.
Dirty Data and Fragmented Systems Erode Confidence
A primary issue for left-behind teams is poor data quality in their CRM and sales databases.
If account and pipeline data are incomplete, out-of-date, or riddled with duplicates, it becomes nearly impossible to forecast accurately or draw meaningful insights. Unfortunately, this scenario is all too common. Only 35% of sales professionals “completely trust” the accuracy of their customer data in CRM, according to Salesforce. In other words, nearly two-thirds have doubts about their data, which suggests that things like contact info, deal values, or close dates are often wrong or not kept current.
This lack of trust in data creates a vicious cycle: because data is unreliable, managers and reps fall back on gut instinct rather than using the CRM’s analysis – and with less usage, the data quality worsens (the old “garbage in, garbage out” problem).
For CPG companies with extensive product lines and large customer bases (e.g. retail distributors, wholesalers), data fragmentation is a big part of the problem.
Many have sales data spread across multiple legacy systems or spreadsheets. In fact, despite the availability of modern tools, a significant portion of trade sales processes still run on spreadsheets. Salesforce reported that only 36% of consumer goods firms had high adoption of trade promotion management software, leaving the rest relying on antiquated tools for retail execution and analytics.
These patchwork systems lead to inconsistencies and an incomplete view of the business.
Sales teams left behind often lack a unified customer database; their field reps might each keep separate records, or data might be siloed by region or channel.
The impact on forecasting and planning is severe: if, say, out-of-stock instances or promotion results aren’t tracked accurately due to system gaps, companies end up with nearly 40% of in-store initiatives not going as intended and limited ability to diagnose why. In short, laggards are flying blind or with blurry instruments.
Hindering AI use
Crucially, bad data undermines any potential AI efforts.
As noted earlier, AI algorithms can’t fix fundamentally flawed input. Executives in less digitally mature organizations cite data complexity and quality issues as a major hurdle. 50% of CPG leaders worry about data governance and fragmentation when deploying AI.
Some CPG companies have indeed tried advanced analytics or pilot AI projects, only to see them stall because the underlying data wasn’t reliable or was scattered in silos. Without addressing root issues (like cleansing data and integrating systems), these firms remain stuck in pilot mode. This is corroborated by McKinsey’s finding mentioned above that no CPG company has truly scaled AI to its full potential yet; many dabble in it, but those without a strong data foundation can’t move beyond proofs of concept.
In essence, teams that neglect data quality are depriving themselves of the AI revolution, because they simply can’t leverage it effectively. And meanwhile, their competitors with cleaner data are generating insights and efficiencies that widen the performance gap.
Hesitancy in AI Adoption (or Stuck in Pilots)
Perhaps the most defining gap between the futures of sales teams is whether or not they embrace AI.
Teams left behind typically show hesitancy, limited experimentation, or a narrow focus on cost-cutting automation rather than transformative AI use cases. While their competitors turn AI into a growth driver, these laggards risk falling further behind each year they delay meaningful adoption.
Mindset
Some companies remain skeptical of AI’s ROI or fear risks, resulting in tentative approaches.
CPG has historically been slower than tech or finance sectors in tech adoption, and this can still be seen: even though a majority have tried AI in some function, many CPG firms use it sparingly in sales, unsure of the value or concerned about data privacy and algorithm fairness.
This caution can manifest as prolonged pilot projects that never scale.
For instance, a sales team might test an AI lead scoring tool in one region but not roll it out company-wide due to lukewarm initial results (often tied to the data problems mentioned). Or they might use AI only in a limited way (like a basic chatbot on the website) and not in core sales planning.
Meanwhile, the leaders are pushing ahead. As mentioned above, 66% of CPG companies are not just piloting but actively scaling generative AI now. The laggards face a risk of falling into a permanent “pilot purgatory” where they never realize AI’s benefits at scale.
Wrong goals
Many past automation efforts were about efficiency and cost-cutting alone.
For example, using AI to reduce headcount or automate simple tasks. While efficiency is important (and AI can indeed automate low-level work), the growth-oriented companies use AI to increase revenue and market share.
Left-behind teams often haven’t made that leap; they might deploy a bot to handle some service tickets (saving a bit of support cost) but ignore AI opportunities in sales like upselling or dynamic pricing. This limited vision means they don’t get the dramatic top-line impact that AI can provide when fully embraced as a strategic tool.
Lack of internal capabilities
Companies lagging behind likely haven’t invested in data science talent or training their salesforce to use AI tools.
Without upskilling, even the best AI software will go underutilized. Executives acknowledge this: they predict that by 2026 over half of their workforce will regularly use AI, “but without upskilling, change management, and clear use-case ownership, these tools will stall.”
Those who fail to implement robust training programs and change management find that reps either don’t trust the AI outputs or don’t know how to incorporate them into their routine.
For example, an AI forecasting tool might flag a deal as unlikely to close, but an untrained rep or skeptical manager might ignore the warning, rendering the tool moot. Trust is essential, and it’s telling that companies leading in AI work hard on culture and trust-building, whereas laggards often have a culture of “this is how we’ve always done it” that resists AI recommendations.
Compounding consequences of not adopting AI
In the near term, we see gaps like the revenue growth stat (teams using AI outpacing others by a wide margin in hitting targets). Over a few years, these gaps can widen into chasms.
Let's say that Competitor A is using AI to personalize retailer promotions and achieve higher sell-through. They will start capturing market share from Competitor B who is still doing one-size-fits-all promotions. If one CPG manufacturer uses AI to optimize their field sales routes and call schedules, their reps can visit more accounts in a week than a rival’s reps who plan manually. It translates into more shelf space won or issues resolved.
These incremental advantages accumulate.
Bain analysts suggest that AI could even accelerate industry disruption, where brands that cling to old practices get “disintermediated” or outmaneuvered by those that reinvent their go-to-market with data and AI. In essence, the longer a sales team delays serious AI adoption, the harder it will be for them to catch up.
They’ll not only miss immediate gains but also forfeit the learning curve and data network effects that early adopters enjoy.
To illustrate, consider the earlier Coca-Cola vs. PepsiCo example.
Coca-Cola’s embrace of digital innovation helped it weather a tough market with only a 1% volume drop in North America, whereas PepsiCo (which was also investing in tech, but perhaps hadn’t realized the same execution) saw a 4% volume drop and missed some revenue expectations.
Over time, if Coke’s AI-driven initiatives continue to yield even small percentage advantages in growth or efficiency each quarter, those will aggregate into a significant lead in market share and profitability.
The “left behind” teams risk entering a spiral of decline. As results falter, they have fewer resources to invest in innovation, further widening the gap.
Outcomes: What Falling Behind Looks Like
To summarize the fate of left-behind sales teams, we can look at a few concrete outcomes and compare them with their AI-enabled peers.
Forecasting and Pipeline Visibility: Laggards rely on Excel and gut feel, resulting in frequent surprises and misses. Leaders, with clean data and AI, enjoy forecasts they can confidently act on (often with 30%+ better accuracy). A sales VP at a “left behind” firm might routinely find that end-of-quarter results are way off the early forecast, whereas at an AI-driven firm the forecast is a living document updated in real time by predictive models.
Sales Efficiency: Left-behind teams are inefficient, with reps drowning in admin work. As noted, an average rep may spend >70% of time on non-selling tasks in these environments. In contrast, AI-enabled teams automate much of that drudgery. Even if the AI leaders haven’t fully flipped the ratio yet, they are aggressively working to increase active selling time (for example, using AI to auto-log activities or qualify leads). This means AI-ready reps can manage more accounts or opportunities per person, or simply devote more attention to key customers, driving better outcomes.
Revenue and Growth: Ultimately, being left behind reflects in the top and bottom line. High-performing sales organizations (often those embracing AI) are far more likely to hit their revenue goals – recall the 83% vs 66% stat for teams with vs. without AI meeting growth targets. Over a year or two, that translates to meaningful difference in growth rates. We can also infer that customer retention and satisfaction diverge: leaders use CRM data to personalize service (e.g. tailoring pitches by retailer, using insights to solve customer problems proactively), whereas laggards provide a generic experience. It’s reported that 74% of businesses say CRM software gives them better access to customer data and helps build relationships – something the left-behind are not fully leveraging, likely resulting in lost customers or smaller share of wallet.
Talent and Morale: Sales teams stuck with bad systems often suffer morale issues and higher turnover. Reps today, especially younger ones, expect modern tools. If a company cannot provide a streamlined digital selling platform, it may lose talent to competitors that do. Furthermore, as noted, only 17% of sellers in a recent survey expected their team to hit quota, a reflection of how challenging they perceive the environment. Persistent failure to meet goals becomes demoralizing and creates a culture of underperformance. Meanwhile, salespeople at AI-forward organizations may feel empowered by the “co-pilot” tools and more optimistic in hitting targets due to the insights at their fingertips.
The table below highlights key differences between AI-ready sales teams and those left behind.
Sources: Compiled from industry research and surveys
As the table illustrates, the gap between the two archetypes of sales teams is wide across every dimension, and it’s widening. The left-behind teams face a reality of flat or declining performance, while the AI-ready teams push ahead with gains in efficiency and revenue. Importantly, none of this is set in stone: laggards can still catch up by learning from what the leaders are doing.
Why These Differences Matter and How to Catch Up
The contrast between the two futures of sales teams is more than academic. It has real implications for competitiveness in the CPG sector. As we’ve seen, those who harness AI and data are achieving superior outcomes.
The critical question for any organization finding itself on the “left behind” side is: How can we catch up?
The research and case studies highlighted point to several focus areas:
Make Tools People-Friendly
If your sales team isn’t enthusiastically using the CRM and other sales apps, find out why and address it.
- Is the interface confusing?
- Are there too many steps to log a simple piece of info?
- Is it missing mobile capabilities?
By gathering rep feedback, sales ops and IT teams can identify quick wins to improve usability.
Sometimes the solution is training and communication: show the reps what’s in it for them.
For example, demonstrate how entering their opportunity data properly could lead the AI to suggest a tactic that helps them close the deal (thus putting money in their pocket). Often, reps don’t use a system because they’ve never seen the upside.
The goal is to turn the CRM from a chore into an assistant. When that happens, usage skyrockets, giving leaders the visibility and data needed to truly manage the business scientifically.
Improve CRM Data Quality
It’s often said that “Great AI starts with great data”, and our findings reinforce that truth.
Companies must invest in data cleansing, integration, and governance. This may involve a one-time cleanup of duplicates and errors (using tools or services designed for CRM data quality) and implementing ongoing processes to keep data fresh (such as validation rules or automated enrichment from third-party sources).
Executives should champion a data-driven culture where every sales decision is expected to be backed by data. This sets the tone that maintaining good data is everyone’s job. By doing so, a company not only fixes current issues but also establishes the foundation to deploy AI tools effectively.
In sum, clean and connected data is non-negotiable for those wanting AI-driven growth.
Upskill and Involve Your Team
Adopting AI in sales is as much a people project as a tech project. The companies winning are investing in change management, training, and creating internal champions for AI.
- Establish an “AI Academy” or a center of excellence where sales team members can learn about new tools (e.g. how to interpret an AI lead score, or how to use a new forecasting dashboard).
- Involve star reps in pilot programs and have them share success stories of how using an AI insight helped them win – this peer influence can convert skeptics.
- Clarify that AI is there to augment, not replace.
The fear of automation can be a barrier, so leaders should emphasize (with examples) how AI takes away drudgery and helps reps sell more, rather than implying any reduction in the need for human sellers.
The more employees trust and feel comfortable with AI, the faster the organization can deploy it for gains.
Start with High-Impact AI Use Cases
To build momentum, lagging teams should identify a few AI or analytics use cases that address pain points and can show quick wins.
For example, implementing an AI tool that helps reps prioritize leads or opportunities (Salesforce’s Einstein Lead Scoring, for instance) could quickly improve conversion rates .
Another area is forecasting: introducing an AI-driven forecast that flags risky deals can improve accuracy and prevent end-of-quarter surprises, which management will appreciate.
Choose a market or segment to trial these in, measure the results, and then broadcast those results. This can help convert the broader organization.
Learn from Peers and Partners
Finally, companies behind the curve should not reinvent the wheel.
They can learn from case studies of peers (like the ones in this article) and even consider partnerships. Many CPG firms are collaborating with tech partners or consulting firms to leapfrog ahead (some are working with Salesforce’s experts to optimize their CRM usage).
There’s a rich ecosystem of solutions and best practices out there. Industry forums, conferences, and research can provide benchmarks and guidance. One key learning is that AI adoption is not a one-time project but a journey.
Therefore, a company shouldn’t be discouraged if the first attempt isn’t perfect. The important part is to commit to the journey now, rather than delay further.
Conclusion: Embracing the AI-Driven Future in CPG Sales
The message is clear: sales teams in the CPG industry are at a fork in the road.
One path leads to AI-driven growth, marked by clean data, empowered reps, and intelligent automation. Those who take this path are already seeing higher revenues, better efficiency, and stronger customer relationships.
The other path, staying stuck in legacy habits, leads to being outpaced and left behind, as competitors capitalize on technology to move faster and connect more closely with customers.
The years 2023–2025 have shown an inflection point where AI and data have transitioned from buzzwords to real-world differentiators in sales.
Organizations that have prepared – by getting their data in order, fostering user adoption, and boldly deploying AI – are pulling ahead.
For example, we saw Coca-Cola outperforming expectations partly due to AI-powered sales initiatives, and broad surveys indicating the majority of AI-enabled sales teams hitting their targets. These are compelling evidence that the combination of AI + clean CRM data + human talent yields tangible growth in the CPG context.
On the flip side, the cost of inaction is rising.
Continual reliance on intuition, messy spreadsheets, and status-quo processes is proving untenable in an era where competitors can make decisions with pinpoint precision and speed.
The gap will not simply hold steady. it will widen.
As AI capabilities (like more advanced generative AI and autonomous agents) improve, the competitive advantage of using them will compound. Late adopters may find that by the time they try to implement, leaders have moved even further to next-generation AI techniques, making the catch-up even harder.
This is why experts call AI a “competitive moat” for those who embrace it early.
The encouraging news is that it’s not too late for companies on the trailing side to change course.
The playbook is becoming well-defined:
- Prioritize ease of use
- Invest in data
- Start incorporating AI in targeted ways
- Continuously train your people.
Many large CPG organizations are doing exactly this.
There is intense focus across the industry now on data platforms, AI partnerships, and digital upskilling. In our research, 88% of CPG companies had budget earmarked for AI, and essentially all (93%+) were leaning into data for growth. This means the race is on; the majority understand what needs to be done, and it’s a matter of execution.
In conclusion, the future of sales belongs to teams that are ready and willing to adapt.
Those that treat their CRM not as a record-keeping tool but as a dynamic engine of insight, powered by clean data and AI, will create more value for their customers and more growth for their business. Their salespeople will be more productive and engaged, armed with predictive guidance and freed from menial tasks.
On the other hand, teams that fail to embrace these changes risk declining performance and relevance, as they’ll be operating with blurred vision in a market that demands clarity and speed.
The two futures are already visible today. But every organization still has the opportunity to choose its path.
The imperative for CPG sales leaders is to act decisively: champion user-friendly design, double down on data quality, and aggressively pilot and scale AI in the sales process. By doing so, they can ensure their team is among those ready for AI-driven growth, and not one of those left behind.