State of Artificial
Intelligence in
Consumer Goods
Benchmarking the progress, emerging functions and challenges, with a focus on generative AI
Generative AI use is rapidly closing the usage gap with traditional AI, indicating fast adoption.
Back-office operations emerge as a priority area for AI implementation.
CGs are prioritizing generative AI for client-facing roles, expecting short-term ROI.
AI’s contribution to sustainability is primarily in the goal-setting stages with the potential for broader impact throughout the supply chain.
From boosting customer engagement to enhancing marketing campaigns to creating more efficiencies within operations, artificial intelligence (AI) has already had a widespread impact on the consumer goods (CG) industry.
“Traditional AI” was defined to survey respondents as algorithm-based solutions that reason over structured data to solve specific questions, like making predictions and recognizing patterns (i.e. image recognition, demand forecasting, and NLP).
AI Progress: Adoption and Benefits by Business Function
In the first wave, the top 10 business functions where CGs were already using or planning to deploy generative AI included social media marketing and analytics (82%); sales, marketing, and consumer experiences (81%); customer feedback analysis (72%); and product research (69%). While demand planning and forecasting were among the top 10 current or planned uses of generative AI (60%), marketing, e-commerce, and R&D were progressing faster.
Challenges in adopting generative AI have decreased overall, indicating growing confidence in its implementation with notable improvements in overcoming obstacles related to costs, updates, and expertise (see Figure 3).
Data privacy and cyber-attacks remain primary security concerns, proving that there is more work to be done—to prepare the data and tools—before implementation begins. For example, document protection is a crucial data privacy concern for CG manufacturers, as leaked or breached material can harm competitive status.
Figure 4: Top 5 Benefits of Generative AI in Wave 1 vs. Wave 2
Source: Microsoft + EnsembleIQ B2B AI Study. Wave 1 (Jan. - Feb. 2024): base total respondents, n=33. Wave 2 (Oct. 2024): base total respondents, n=52.
⇩ Statistically significant difference at the 95% confidence level versus 1st Wave.
That said, there are still benefits CGs are already seeing such as employee productivity and personalized customer experience. Overall perceived benefits have decreased slightly, suggesting a more realistic assessment of AI benefits based on a closer evaluation of expectations and ROI (see Figure 4)
Figure 3: Top 5 Challenges of Generative AI Easing from Wave 1 to Wave 2
Rapidly changing nature of land- scape needing frequent updates
Source: Microsoft + EnsembleIQ B2B AI Study. Wave 1 (Jan. - Feb. 2024): base total respondents, n=33. Wave 2 (Oct. 2024): base total respondents, n=52.
⇩ Statistically significant difference at the 95% confidence level versus 1st Wave.
82% 70% 61% 45% 42% 39% 52% 39% 30% 33% 42% 42%
Source: Microsoft + EnsembleIQ B2B AI Study. Wave 1 (Jan. - Feb. 2024): base total respondents, n=33. Wave 2 (Oct. 2024): base total respondents, n=52.
⇧ Statistically significant difference at the 95% confidence level versus 1st Wave.
(Among those currently using or planning to use generative AI tools)
Figure 2: Ranking of Uses of Generative AI
1st Wave
In the second wave results, while use/planning has grown within every business function, generative AI sees a substantial usage increase in back office operations and rapidly gains ground within the optimization of warehouse receiving, both of which have emerged as priority areas (see Figure 2).
The share of respondents with no defined plans to adopt AI in 2024 has dropped, signaling growing relevance and confidence in the technologies. Challenges related to costs, updates and expertise have significantly reduced, and a wider range of business functions continue to adopt generative AI (see Figure 1).
Traditional AI
Source: Microsoft + EnsembleIQ B2B AI Study. Wave 1 (Jan. - Feb. 2024): base total respondents, n=52. Wave 2 (Oct. 2024): base total respondents, n=57. The percentage of respondents who selected “don’t know/not sure” for each of traditional AI and generative AI not shown in the above chart. ⇩⇧ Statistically significant difference at the 95% confidence level versus 1st Wave.
Figure 1: Consumer Goods Organizations' Use of AI Increasing
Introduction
Microsoft commissioned the following benchmark study with EnsembleIQ to better understand CG organizations’ sentiments towards using AI tools and solutions, specifically focusing on generative AI.
In the first wave of the research (conducted in Q1 2024), CGs said they were comfortable deploying traditional AI within their businesses already—such as for personalized product recommendations and chatbots—and they were excited to embrace more generative AI applications. However, they weren’t fully sure how to leverage the technology yet.
Fast-forward to today and new benchmarking data (conducted in Q4 2024) shows that more CGs have moved past the planning and preparation phases, and are actively using generative AI in both back-office operations and client-facing functions. Once again, there were three key areas explored:
While the first wave* of research showed CGs plans for leveraging generative AI, many respondents did not yet feel like they were advanced enough to use it to the fullest potential. Dig into the second wave** of data below and discover how CG companies are already evolving their sentiments around this buzzworthy technology.
Research Key Findings
Data privacy and cyber-attacks remain primary security concerns, potentially hindering AI implementation.
Document protection is a crucial data privacy concern, potentially affecting CGs competitive status.
Managing expectations and ROI as CGs develop a more realistic assessment of AI benefits, potentially leads to adoption hesitation.
Bridging the gap between AI sustainability planning and implementation, where data indicates a slow transition from strategy to action.
Leverage generative AI in finance applications, an emerging back-office focus area.
Capitalize on generative AI’s potential distribution logistics and warehouse receiving optimization, areas showing usage increase.
Develop comprehensive generative AI solutions for marketing applications focusing on enhanced content creation, campaign optimization and personalized client engagement.
Harness the growing confidence in generative AI adoption, as challenges related to costs, updates and expertise have significantly reduced.
“Generative AI” was defined as a category of AI techniques and models that are designed to generate new, original content rather than simply recognizing or analyzing existing data.
The top functions for which CG companies are deploying or planning to deploy generative AI.
Generative AI Potential: Efficiency and Productivity in Sales & Marketing
Wave 1:
Wave 2:
DIVE INTO THE DATA
While CG organizations have prioritized generative AI for improvements in e-commerce and marketing, more recently, finance has emerged as a potential opportunity of focus for implementation—jumping from 15% in wave 1 to 33% in wave 2 (see Figure 5).
Figure 5: CGs’ Business Areas for Generative AI Improvement
When asked “What is the most valuable use of generative AI in the CG industry over the next 2 years?”, the greatest value was expected in marketing at 35%, which breaks down into a focus on enhanced content creation (20%) and personalized client engagement (15%). The next greatest area selected was supply chain at 20%, which breaks down into improved efficiency optimization (13%), and demand forecasting (7%).
Case Study
In addition, CGs are prioritizing generative AI improvements in client-facing roles, particularly marketing (47%) and sales (33%), due to expectations of short-term ROI driven by improvements in content creation, campaign optimization, and automated support (customer service).
Among the CG manufacturers currently using generative AI, the primary areas and business functions where they have seen improvements include:
Building a Collaborative Future
Budgeting AI for Sustainability Goals: Starting with Supply Chain
Nearly half of CG manufacturers report AI's contribution to sustainability is primarily in a goal-setting step, suggesting its use is still in the early stages. Only three out of 10 CG manufacturers have budgets allocated to develop sustainable goals using AI (see Figure 6).
Figure 6: Milestones Attained Using AI for Sustainability Goals
While many CGs are in the planning or goal-setting stages, nearly five out of 10 manufacturers expect AI to impact sustainability throughout the supply chain, offering significant potential to improve core processes (see Figure 7).
Source: Microsoft + EnsembleIQ B2B AI Study, Wave 2 (Oct. 2024): base total respondents, n=57.
Sustainable supply chain overall
Figure 7: Sustainability Challenges to Address Using AI
Source: Microsoft + EnsembleIQ B2B AI Study, Wave 2 (Oct. 2024): base total respondents, n=57.
While the top three areas of improvement listed here—supply chain, plastic waste reduction, and emissions reporting—are progressing, they are not moving as fast as other business functions like sales, marketing, or consumer experiences just yet (as seen in Figure 2).
Measurable Sustainability Progress
Source: Microsoft + EnsembleIQ B2B AI Study. Wave 1 (Jan. - Feb. 2024): base total respondents, n=33. Wave 2 (Oct. 2024): base total respondents, n=52. ⇧ Statistically significant difference at the 95% confidence level versus 1st Wave.
“We have partnered with Sitecore and Microsoft innovation teams over the last year to pilot and leverage the power of generative AI with Nestlé brand guidelines and toolkits. Together, we’ve created an innovative new offering: the Sitecore Brand Assistant as part of Sitecore Content Hub with Sitecore Stream. This solution is specifically designed to facilitate brand and category knowledge within our organization and supercharge our creative partners.”
Technology solutions provider CDW Corporation is embracing generative AI to power employee productivity and accelerate customers’ AI journeys. CDW rolled out Microsoft 365 Copilot to 10,000 employees, resulting in:
improved work quality
Read the full case study
Beiersdorf launched its CARE BEYOND SKIN Sustainability Agenda, which features specific sustainability targets: achieve an absolute reduction of 30% in greenhouse gas emissions by 2025 along the entire value chain versus 2018 and make all packaging refillable, reusable, or recyclable by 2025. By enlisting the help of Microsoft Azure, Power Platform, and a central data corpus, Beiersdorf can:
Make factors affecting emissions transparent through dashboards to check the status of sustainability progress at any time
Leverage a simulator to see how a packaging material change affects a product’s overall carbon footprint
Visualize all factors that influence its carbon footprint in the form of dashboards
Read the full case study
AI-Ready Data:
Preparation and Best Practices
Most manufacturers (six in 10) have completed generative AI exploratory analysis and are now focusing on data quality. However, CG respondents selected deployment infrastructure and API endpoints as the least developed capabilities (see Figure 8).
Figure 8: Generative AI Data Prep Capabilities Fulfilled
Source: Microsoft + EnsembleIQ B2B AI Study, Wave 2 (Oct. 2024): base currently using or planning to use generative AI, n=57.
According to Gartner®, “3 key questions to ask as you develop and prioritize your AI-ready data initiatives are:
Does our organization’s data ALIGN with use-case requirements?
As per the Gartner research, “there is no way to make data AI-ready in general or in advance. The readiness of data for AI depends on how the data will be used. For example, very different datasets would be required to build a predictive maintenance algorithm versus applying GenAI to enterprise data.”
Gartner defines, “AI-ready data means that your data must be representative of the use case, of every pattern, errors, outliers and unexpected emergence that is needed to train or run an AI model for a specific use. It’s a process and a practice based on availability of metadata to align, qualify and govern the data.”1
Data-Ready Transformation
International hygiene, baby, and beauty consumer goods company, PZ Cussons, was heavily reliant on manual processes and offline reports, where data silos were common.
Read the full case study
The company wanted to unify its data and provide a comprehensive analytics solution to improve operations, so it deployed Microsoft Fabric and Azure Databricks to ingest and harmonize data from various sources.
“No matter the role, we’re all working from the same version of truth. We’re pursuing AI readiness, so when each new use case emerges, we’ve already been thinking about it, and we’re ready to get started,” according to Richard Sharkey, head of data analytics and AI for PZ Cussons.
AI Data Security: Building a Foundation of Trust
Trust is at the foundation of security efforts, and how CG companies manage and protect their data is critical to strengthening trust.
However, new solutions are empowering CG companies to build security, privacy, and compliance into their systems, streamlining regulatory adherence and enhancing trust between their partners and end consumers.
Figure 9: Top AI Implementation Security Considerations
Source: Microsoft + EnsembleIQ B2B AI Study, Wave 2 (Oct. 2024): base total respondents, n=57.
Additionally, cyber threats pose challenges due to their unpredictable and evolving nature.
■ Document protection 15% ■ Unauthorized access prevention 7% ■ Data breach concerns 5% ■ Proprietary information protection 2%
■ General cyber attacks 13% ■ AI-specific vulnerabilities 6% ■ Ransomware 2% ■ Phishing 1% ■ Malware 1%
■ Evolving regulations 10% ■ Employee training 3% ■ Budget constraints 2%
■ Role-based access 8% ■ Vendor and supplier access 4%
■ Unknown threats 8% ■ Algorithmic bias 3% ■ Over-reliance on AI 1% ■ AI misuse 1%
■ Resource allocation 3% ■ Incident response planning 2%
Three critical traits that CG organizations should strive for within their security solutions, services, and practices include:
Baked In Data Security
With operations around the globe, Grupo Bimbo tapped the Microsoft Purview family of data security, governance, risk, and compliance solutions, gaining visibility to an array of benefits including:
Improved data security, compliance
Read the full case study
INSIGHTS
CHALLENGES
OPPORTUNITIES
EXPLORE CONTENT
Defining AI
Wave Timing
*1st Wave
January 3, 2024 – February 12, 2024
**2nd Wave
October 4, 2024 – October 18, 2024
The greatest perceived obstacles to the productionization and scaling of generative AI use cases.
The business outcomes or benefits CG companies anticipate achieving through the use of generative AI capabilities.
Not using and no plans to use tools/solutions
Currently using tools/solutions
Preparing to use tools/ solutions in the next 12 mo.
Planning to use tools/ solutions in the next 1+ year
Evaluating tools/solutions, but no defined plans
Generative AI
Not using and no plans to use tools/solutions
Evaluating tools/solutions, but no defined plans
Planning to use tools/ solutions in the next 1+ year
Preparing to use tools/ solutions in the next 12 mo.
Currently using tools/solutions
68%
54%
14%
15%
7%
2%
5%
21%
2%
6%
56%
21%
28%
33%
7%
10%
9%
27%
0%
8%
2nd Wave
1st Wave
2nd Wave
1st Wave
Costs of running AI systems
IP protection concerns (e.g. copyright disputes, data leaks, etc.)
Finding correct expertise to develop capabilities
Difficulty defining/prioritizing what AI tools/systems should do
Rapidly changing nature of landscape needing frequent updates
Costs of running AI systems
IP protection concerns (e.g. copyright disputes, data leaks, etc.)
Potential or existing AI governance (e.g. regulation of AI)
Difficulty defining/prioritizing what AI tools/systems should do
67%
58%
55%
52%
52%
54%
46%
37%
37%
37%
Cost savings
Increased employee productivity
Better data analytics/ performance measurement
Increased profits
Better/more personalized CX
Greater predictability
Increased innovation (e.g. content creation)
Increased employee productivity
Better/more
personalized CX
Cost savings
67%
64%
61%
61%
55%
56%
56%
48%
46%
42%
Wave 1:
Wave 2:
Sales, marketing, and consumer experiences Product research/development/design Demand planning and forecasting Delivery/distribution logistics, disruption management Optimizing warehouse receiving Sustainability Storing/warehousing raw materials, WIP, finished goods Product delivery/distribution to retailers or end consumers Back office operations Transportation and route optimization Manufacturing Sourcing/procurement
85% 77% 69% 54% 69% 56% 54% 54% 69% 50% 63% 60%
3% 7% 9% 8% 27% 16% 2% 14% 39% 17% 21% 17%
2nd Wave
Difference
Goals have been defined
Sustainability is being incorporated into the product development process
Budget has been allocated
Reports are being produced
Don't know/not applicable
Budget has been spent
47%
40%
33%
23%
18%
9%
Plastic waste reduction/circularity improvement
Emissions reporting & addressing regulations
Water use reduction
Food waste reduction
Don't know/not applicable
Reducing deforestation
Human rights
Other
46%
39%
35%
28%
26%
18%
16%
9%
4%
Insights
Business Function
Human Resources
2nd Wave
1st Wave
Finance
Supply Chain
Marketing
E-commerce
62%
58%
60%
52%
44%
58%
33%
15%
19%
15%
19%
24%
19%
33%
6%
6%
Other
Warehouse Management
Merchandising
of tasks completed faster
productivity gains
88%
77%
85%
— Aude Gandon, Global CMO, Nestlé
Data privacy: Securing various documents, files, etc.
Cyber attacks
Unknown/potential threats that AI implementation may uncover
Staying compliant with evolving regulations
Employees accessing data based on roles and permissions
Suppliers, vendors, and/or subsidiaries accessing data they shouldn't
Other (e.g. bias, hallucinations)
65%
51%
42%
39%
30%
19%
4%
How do we QUALIFY data use to meet AI-expected confidence requirements?
How do we GOVERN AI-ready data in the context of the use case?"
1
2
3
Exploratory analysis Analytics to understand data and identify patterns
Quality data High-quality, labeled, and unified data pipelines
Security and governance Systems or platforms to monitor model performance, health, and usage
Training data Diverse, substantial datasets for training generative AI models
Deployment infrastructure Scalable, available, and reliable application platforms
API endpoints Platforms to expose generative AI models as APIs
60%
49%
44%
42%
37%
33%
Use Cases:
Putting Generative AI in Action
How to Capitalize on the Potential of Generative AI
In sum, there are four key areas where CG organizations can capitalize on the opportunities presented in this research:
Third, CGs should leverage generative AI within finance applications—an emerging focus area within back-office operations.
Second, CGs should take advantage of generative AI's potential within distribution logistics and warehouse receiving optimization, two areas showing substantial usage increases.
Power Your AI Transformation with Microsoft for Consumer Goods
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Data Privacy and Security
Cyber Threats
Regulatory Compliance
AI-Specific Concerns
Organizational Challenges
Access Control
28%
22%
14%
12%
5%
13%
Greater efficiency for cybersecurity teams
Increased protection against data loss
CASE STUDY
CASE STUDY
CASE STUDY
CASE STUDY
SPECIAL REPORT
Build brands people love
Design and produce next-gen products
Create an agile, sustainable supply chain
Optimize channels and global routes-to-market
Gain a holistic understanding of consumers and drive brand equity and tailored marketing with powerful insights
Reduce time to market with agile and collaborative design and production that drives efficiency and sustainability
Anticipate disruptions, enhance forecasting accuracy, and optimize operations to ensure sustainable access to the right products at the right time
Improve margins and maximize profitability with data-driven channel strategies
GENAI USE CASES | CONSUMER GOODS
AUDIENCE & IMPACT
Accelerate consumer goods time-to-market with AI-driven insights and design
Extract customer signal for demand sensing; product and packaging development and revenue growth and trade promotion management
Research and development teams spend substantial time refining and optimizing product and packaging designs to address social trends and consumer demand. With AI, they can automate design processes and quickly generate innovative solutions that meet predefined specifications. This enables faster response and execution, as well as quicker and more effective product innovation – and AI is increasingly being leverages in revenue grwoth management and trade promotion.
BUSINESS OUTCOMES
Challenges addressed
Personas
Process & functions
Faster product time-to-market
Increased sales revenue
Lower acquisition cost
Larger LTV
Higher NPS/CSAT
Lower operating costs
Assessing consumer demand
Incorporating social trends
Product and package design delay
VP Research & Development
Chief Revenue Officer/Sales VP
Chief Marketing Officer
Chief Product Officer
Research & Development
Revenue/Sales
Marketing
Product
Retail Execution
Increase productyivity and creativity with AI-powered DTC marketing content creation
Enable marketers to access brand and category knowledge and supercharge their creative partners with streamlined content, image and campaign creation, plus localization and brand assistance
Brands are using Generative AI to transform marketing as it helps marketers find consumer insights. Then, in a fraction of the usual time, create multiple versions of creative, taglines, or images that better match the interests of more personalized segments and loyal brand consumers. This approach enhances consumer engagement through real-time, tailored interactions, leading to faster time-to-market of new campaigns and higher conversion rates. By offering personalized content, interaction and recommendation, brands can improve customer retention and streamline the shopping process, making it more intuitive and user-friendly.
Accelerated time-to-market of new campaigns
Increased conversion rates
Increased worker productivity
Reduced production and labor costs
Customer engagement
Personalization
Agile creative workflows
Manual task load
Chief Marketing Officer
Marketing
Deliver personalized consumer experiences through natural language
Gather information and enable enhanced product discovery by recommending products or services based on individual preferences and conversational interaction
Brands can elevate consumer experience with conversational commerce, targeting online shoppers who value personalized, convenient interactions. This approach enhances engagement through real-time, tailored interactions, leading to higher conversion rates, average order value, and site engagement metrics. By offering personalized recommendation and instant support, brands can streamline the shopping process, making it more intuitive and user-friendly. This not only boosts loyalty but also helps gather data that enhances product discovery by recommending products based on individual preferences and conversational interaction.
Increased conversion rates
Higher average order value and site engagement metrics
Reduced return rates
Customer engagement
Personalization
Abandoned carts
Chief Marketing Officer
Chief Digital Officer
Chief Customer Experience Officer
Marketing
Ecommerce/digital
Customer experience
Transform consumer goods factory ops with intelligent data insights
Unify facility and factory data to enable real-time, intelligent production insights that optimize operations, enable predictive maintenance and improve worker collaboration
Consumer goods manufacturers dedicate substantial time to optimizing their processes to ensure efficiency and streamline operations. AI-enabled facilities can leverage real-time data to predict equipment failures, thus reducing unnecessary downtime and preventing defects that slow production. GenAI can also help provide frontline workers with language chat interfaces, providing quicker, more intelligent insights. This shortens manufacturing cycles, while ensuring higher-quality outputs and faster, more efficient delivery. AI integration helps brands build more agile and responsive operations, driving innovation and competitiveness.
Reduced transportation, warehouse/inventory, distribution and labor costs
Orders delivered in full and on time
Operational inefficiency
Downtime and equipment failures
Frontline worker data access
Chief Operations Officer
Chief Supply Chain Officer
Supply chain
Operations
Generative AI use is rapidly closing the usage gap with traditional AI, indicating fast adoption.
Key Insights
Generative AI use is rapidly closing the usage gap with traditional AI, indicating fast adoption.
Back-office operations emerge as a priority area for AI implementation.
CGs are prioritizing generative AI for client-facing roles, expecting short-term ROI.
AI’s contribution to sustainability is primarily in the goal-setting stages with the potential for broader impact throughout the supply chain.
OPPORTUNITIES
INSIGHTS
CHALLENGES
Data privacy and cyber-attacks remain primary security concerns, potentially hindering AI implementation.
Document protection is a crucial data privacy concern, potentially affecting CGs competitive status.
Managing expectations and ROI as CGs develop a more realistic assessment of AI benefits, potentially leads to adoption hesitation.
Bridging the gap between AI sustainability planning and implementation, where data indicates a slow transition from strategy to action.
Leverage generative AI in finance applications, an emerging back-office focus area.
Capitalize on generative AI’s potential distribution logistics and warehouse receiving optimization, areas showing usage increase.
Develop comprehensive generative AI solutions for marketing applications focusing on enhanced content creation, campaign optimization and personalized client engagement.
Harness the growing confidence in generative AI adoption, as challenges related to costs, updates and expertise have significantly reduced.
Research Key Findings
Back-office operations emerge as a priority area for AI implementation.
CGs are prioritizing generative AI for client-facing roles, expecting short-term ROI.
AI’s contribution to sustainability is primarily in the goal-setting stages with the potential for broader impact throughout the supply chain.
Challenges
Opportunities
Key Challenges
Data privacy and cyber-attacks remain primary security concerns, potentially hindering AI implementation.
Document protection is a crucial data privacy concern, potentially affecting CGs competitive status.
Managing expectations and ROI as CGs develop a more realistic assessment of AI benefits, potentially leads to adoption hesitation.
Bridging the gap between AI sustainability planning & implementation, where data indicates a slow transition from strategy to action.
Key Opportunities
Leverage generative AI in finance applications, an emerging back-office focus area.
Capitalize on generative AI’s potential distribution logistics and warehouse receiving optimization, areas showing usage increase.
Develop comprehensive generative AI solutions for marketing applications focusing on enhanced content creation, campaign optimization, and personalized client engagement.
Harness the growing confidence in generative AI adoption, as challenges related to costs, updates, and expertise have significantly reduced.
Decision-making and insights
Creativity and innovation
Customer relations
Supply chain and operations
Collaboration and communication
Early adoption and exploration
Efficiency and productivity
30%
18%
14%
12%
10%
8%
8%
Enabling secure acquisition integration with information protection and compliance capabilities
Streamlining operations with comprehensive security across the supply chain
Protecting consumer data with advanced threat detection and incident response
Source: Microsoft + EnsembleIQ B2B AI Study. (Jan. - Feb. 2024)
Base currently using or planning to use generative AI tools, n=33
Responses among those currently using or planning to use generative AI tools in Figure 1.
82%
81%
72%
69%
66%
60%
60%
57%
51%
48%
Top 10 Uses of Generative AI Wave 1
Social media marketing/analytics
Sales, marketing, CX
Customer feedback analysis
Online shopping
Personalization for product discovery/reco's/chatbots
Demand planning and forecasting
Personalization for targeted ads
Storing/warehousing raw materials, WIP, finished goods
Paid and programmatic ads strategy
Business Function
Planning (1+ year)
Deploying (12 months)
Currently Using
Currently or Planning to Deploy Generative AI
9%
52%
21%
18%
45%
18%
12%
48%
12%
12%
36%
21%
18%
39%
9%
12%
36%
12%
27%
24%
9%
9%
36%
12%
24%
24%
3%
6%
30%
12%
Product research/ development/design
CLOSE
Click to see
how the top 10
business functions
from wave 1
compare
Microsoft commissioned a quantitative survey with EnsembleIQ targeting consumer goods manufacturer companies in the United States with a focus on individuals who have the responsibility or influence on business decisions related to technology.
Survey Methodology and Demographics
The first analysis wave was administered from January 3, 2024, to February 12, 2024. This second wave of the analysis in this report is presented from online surveys that were administered to the Consumer Goods Technology audience from October 4, 2024, to October 18, 2024:
The top three respondent company types were packaged food and snacks (37%), packaged non-food household products (30%), and beauty and personal care products (26%). The top three business functions were IT/technology (60%), marketing (33%) and operations (30%).
Job Title & Business Function*
Company Size **
Decision-Maker Status
Company Revenue
*May not equal 100% because participants were asked to “select all that apply.”
**May not equal 100% due to rounding.
C-Suite
VP/SVP/EVP
Director
30%
32%
39%
Under 500 employees
500 - 4,999 employees
5,000+ employees
23%
50%
29%
Makes reco's
for decisions
Sole decision maker
Make decisions
with a team
7%
51%
42%
Less than
$100M
$100M to
$4.9B
$5B+
23%
51%
26%
Director
C-Suite
VP/SVP/EVP
5,000+
<500
500-4,999
Makes decisions with team
Makes recos
Sole decision maker
$5B+
<$100M
$100M to $4.9B
Survey Demographics for Second Wave
Source: Gartner, What Is AI-Ready Data? And How to Get Yours There, Rita Sallam, October 21, 2024. Gartner is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.
1
2
3
4
Introduction
AI Progress
Generative AI Potential
Budgeting AI for Sustainability Goals
AI-Ready Data
AI Data Security
Generative AI Use Cases
How to Capitalize on GenAI Potential
Survey Methodology & Demographics
Safeguarding the privacy of associates
Select examples of generative AI use cases we're seeing gain traction in the CG landscape:
Accelerate consumer goods time-to-market with AI-driven insights and design
Research and development teams spend substantial time refining and optimizing product and packaging designs to address social trends and consumer demand. With AI, they can automate design processes and quickly generate innovative solutions that meet predefined specifications. This enables faster response and execution, as well as quicker and more effective product innovation.
Increase productivity and creativity with AI-powered DTC marketing content creation
Brands are using Generative AI to transform marketing as it helps marketers find consumer insights. Then, in a fraction of the usual time, create multiple versions of creative, taglines, or images that better match the interests of more personalized segments and loyal brand consumers. This approach enhances consumer engagement through real-time, tailored interactions, leading to faster time-to-market of new campaigns and higher conversion rates. By offering personalized content, interactions and recommendations, brands can improve customer retention and streamline the shopping process, making it more intuitive and user-friendly.
Deliver personalized consumer experiences through natural language
Brands can elevate consumer experience with conversational commerce, targeting online shoppers who value personalized, convenient interactions. This approach enhances engagement through real-time, tailored interactions, leading to higher conversion rates, average order value, and site engagement metrics. By offering personalized recommendations and instant support, brands can streamline the shopping process, making it more intuitive and user-friendly. This not only boosts loyalty but also helps gather data that enhances product discovery by recommending products based on individual preferences and conversational interaction.
Transform consumer goods factory operations with intelligent data insights
Consumer goods manufacturers dedicate substantial time to optimizing their processes to ensure efficiency and streamline operations. AI-enabled facilities can leverage real-time data to predict equipment failures, thus reducing unnecessary downtime and preventing defects that slow production. GenAI can also help provide frontline workers with natural language chat interfaces, providing quicker, more intelligent insights. This shortens manufacturing cycles, while ensuring higher-quality outputs and faster, more efficient delivery. AI integration helps brands build more agile and responsive operations, driving innovation and competitiveness.
Accelerate consumer goods time-to-market with AI-driven insights and design
Extract customer signal for demand sensing; product and packaging development and revenue growth and trade promotion management
Accelerate consumer goods time-to-market with AI-driven insights and design
Extract customer signal for demand sensing; product and packaging development and revenue growth and trade promotion management
Research and development teams spend substantial time refining and optimizing product and packaging designs to address social trends and consumer demand. With AI, they can automate design processes and quickly generate innovative solutions that meet predefined specifications. This enables faster response and execution, as well as quicker and more effective product innovation.
Transform consumer goods factory ops with intelligent data insights
Unify facility and factory data to enable real-time, intelligent production insights that optimize operations, enable predictive maintenance and improve worker collaboration
Transform consumer goods factory ops with intelligent data insights
Unify facility and factory data to enable real-time, intelligent production insights that optimize operations, enable predictive maintenance and improve worker collaboration
Deliver personalized consumer experiences through natural language
Gather information and enable enhanced product discovery by recommending products or services based on individual preferences and conversational interaction
Deliver personalized consumer experiences through natural language
Gather information and enable enhanced product discovery by recommending products or services based on individual preferences and conversational interaction
Increase productyivity and creativity with AI-powered DTC marketing content creation
Enable marketers to access brand and category knowledge and supercharge their creative partners with streamlined content, image and campaign creation, plus localization and brand assistance
Increase productyivity and creativity with AI-powered DTC marketing content creation
Enable marketers to access brand and category knowledge and supercharge their creative partners with streamlined content, image and campaign creation, plus localization and brand assistance
First, CGs should develop comprehensive generative AI solutions for marketing applications, focusing on enhanced content creation, campaign optimization, and personalized client engagement.
Finally, now is the time to harness the growing confidence in generative AI adoption, as the challenges related to costs, updates, and expertise have significantly reduced, indicating a more favorable implementation environment.
First, CGs should leverage generative AI within finance applications—an emerging focus area within back-office operations.
First, CGs should leverage generative AI within finance applications—an emerging focus area within back-office operations.
First, CGs should leverage generative AI within finance applications—an emerging focus area within back-office operations.
In fact, in Figure 9, internal document and file protection is chosen as the primary data security concern because it can potentially affect competitive status. Cyber threats or attacks came in at a close second due to the challenges associated with their unpredictable and evolving nature.
In fact, in Figure 9, document protection is chosen as the primary data privacy concern because it can potentially affect competitive status. Cyber threats or attacks came in at a close second due to the challenges associated with their unpredictable and evolving nature.
Learn more about Microsoft for consumer goods