Identify the best opportunities, accelerate your sales process and make better closing decisions with predictive insights.
Real cases where AI empowers your decisions
Narrative: A videoconferencing company implemented AI coaching tools accelerating the training of new salespeople with 20% increase in sales after onboarding, while a software company used AI increasing its close rate by 7.4% and reducing the time to reach quota by 3 weeks.
Challenge: Sales teams do not reach their full potential, training of new salespeople is slow and costly, and lack of personalized coaching limits sales growth.
Benefit: 20% increase in sales after onboarding new salespeople, 7.4% increase in close rate, 3-week reduction in time to quota, additional $45,000 per new salesperson due to faster sales, and improved team utilization through actionable insights.
Narrative: A B2B services company implemented lead scoring with machine learning achieving 20% increase in conversion in one month, while an insurer used AI to segment leads showing that high scoring leads converted 3.5 times more.
Challenge: Sales teams waste time on low-potential leads, lack of efficient prioritization reduces sales productivity, and conversion is limited by not identifying real opportunities.
Benefit: 20% increase in conversion rate by focusing efforts on higher value leads, 3.5 times higher conversion on high scoring leads, 80% reduction in conversion of low scoring leads allowing to focus resources, and tripling of conversions by concentrating on leads with more potential.
Narrative: A freelance platform incorporated AI, achieving 95% of forecast accuracy even in changing environments, while a $31M software company improved forecast accuracy by 25% and reduced time-to-revenue by 20%.
Challenge: Inaccurate sales forecasts affect operational and financial planning, uncertainty in forecasts complicates resource allocation, and unexpected deviations impact quarterly results.
Benefit: Achievement of 95% forecast accuracy even in changing markets, 25% improvement in forecast accuracy, 20% reduction in time-to-revenue, improved pipeline visibility, and optimization of inventory, resources and quarterly strategies.
Narrative: A British retail chain used a predictive churn model reducing its churn rate by 54% with customized campaigns, while a Polish telecom operator decreased churn by 20% with ROI of more than 10 times.
Challenge: Customers leave without clear signals beforehand, the loss of valuable customers directly impacts revenue, and the lack of predictability prevents preventive retention actions.
Benefit: 54% reduction in churn rate, 34% increase in 12-month customer lifetime value, 20% decrease in churn with ROI of more than 10x, savings of ~$39,000 per month in avoided losses, and doubling of conversion rate in retention campaigns.
Narrative: A database company implemented an intelligent chatbot increasing new leads 70% and qualified opportunities 170% in three months, while a motorcycle dealership boosted its leads 2,930% by replacing traditional marketing with AI.
Challenge: Hot leads are lost due to lack of immediate response, delay in initial contact drastically reduces success rates, and prospects with high intent go unnoticed.
Benefit: 70% increase in new leads and 170% increase in qualified opportunities in three months, trigger leads at 2,930% with significant reduction in cost per lead, contact within 5 minutes increases probability up to 100 times vs. waiting 30 minutes, and capture previously invisible high intent buyers.
Narrative: A CRM company accelerated 33% of meeting preparation and gained 10% more success rate in closings using AI, while a team at a Fortune 100 technology company saved 41% of research time and generated $26 million in new pipeline in 4 weeks.
Challenge: Salespeople arrive at meetings underprepared, lack of prospect insights reduces success rates, and manual research time consumes valuable resources.
Benefit: 33% acceleration in meeting preparation, 10% increase in closing success rate, 41% savings in lead research time, generation of $26 million in new pipeline in 4 weeks, and hyper-personalized preparation that improves quality of interactions.
Narrative: A major restaurant chain implemented AI for intelligent staff scheduling, while a customer service center used AI to forecast volumes and optimally assign agents.
Challenge: Manual shift assignment generates costly overtime, peak demand creates staff overloads, and inefficient distribution causes idle time and cost overruns.
Benefit: 25% reduction in overtime and idle time combined, 50% cut in overtime costs, full peak demand coverage, and freeing up management time for strategic tasks.
Narrative: An automotive company incorporated vision and AI systems into its assembly lines to detect more defects than human inspectors, while a financial services company applies AI to keep error rates below 0.1%.
Challenge: Quality errors generate rework costs, manual inspections have consistency and coverage limitations, and undetected defects affect customer satisfaction.
Benefit: Up to 90% increase in defect detection rates, significant reduction in rework required, 24/7 fatigue-free inspections, 99% identification of defects vs. 80% manual, and 20% reduction in false positives.
Narrative: Financial organizations implement AI to review transactions in real time, while a global bank developed a check verification system that automatically identifies fraud.
Challenge: Manual audits are slow and error-prone, compliance is resource-intensive, and late detection of non-compliance results in costly penalties.
Benefit: 60% reduction in documentation errors, 50% fewer incidences of non-compliance, $1.2 billion in avoided penalties, $20 million annual fraud prevention, 170% ROI, and 67% less manual audit effort.
Narrative: A consumer goods company implemented digital twins of its factories to test decisions before executing them in 8 plants, while an automotive company introduced digital twins in stamping presses to optimize operations.
Challenge: Strategic decisions carry significant risks, lack of simulation can lead to miscalibrated investments, and operational adjustments without prior testing can cause costly disruptions.
Benefit: 65% less unplanned downtime, 20% energy savings, 15% less scrap, $52 million annually in net savings, 25% reduction in unplanned downtime, and 20% increase in overall equipment efficiency.
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Narrative: A multinational food company implemented machine learning to refine its demand forecasts, while several industry studies demonstrate the transformative impact of AI on inventory management.
Challenge: Companies face constant stock-outs that result in lost sales, product obsolescence due to excess inventory, forecasting errors that affect planning, and overworked planning teams.
Benefit: 30% reduction in lost sales due to stock-outs, 30% less product obsolescence, 20% less forecast errors, up to 50% less planning team workload, and decreased demand errors by 30-50% with logistics costs reduced by 10-40%.