Demand and potential are both great. But so are the risks. We\u2019re here to help.<\/p>\n\n\n\n
The employee view<\/h2>\n\n\n\n\n- 56% of workers in a recent survey<\/a> said they believe generative AI (gen AI) will transform their roles. <\/li>\n\n\n\n
- 65% believe gen AI will allow them to focus on more strategic work.<\/li>\n\n\n\n
- Workers believe generative AI will save five hours a week.<\/li>\n<\/ul>\n\n\n\n
The exec summary<\/h2>\n\n\n\n\n- Gen AI will add up to $4.4 trillion<\/a> to the global economy annually. <\/li>\n\n\n\n
- 75% of the potential value from gen AI is concentrated in four functions: customer operations, marketing and sales, software engineering, and R&D.<\/li>\n\n\n\n
- Gen AI, with other automation technologies, could add up to 3.3 percentage points annually to productivity growth, but companies will need to support employees along the way.<\/li>\n<\/ul>\n\n\n\n\n\t\n\n\t\t\n\t\t\t
Everything you need to know about AI<\/h2>\n\t\t\t\t\t\t\t
Our AI Hub is filled with content to help you understand AI better. Glossaries, customer stories, use cases, how-to\u2019s, and more, it\u2019s all here.<\/p>\n\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t
\n\t\t\t\t\tHelp me Learn About AI<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\n\t\t\n\t\t\t\t\t<\/div>\n\t<\/div>\n\n\t\t\t<\/div>\n\t\n\t\t\t\n\t\t![\"\"](\"https:\/\/www.salesforce.com\/eu\/blog\/wp-content\/themes\/salesforce-blog\/dist\/images\/offer-block\/offer-illustration-layout-one.png\")
\n\n\t\t\n\t\t\t\t\t![\"\"](\"https:\/\/www.salesforce.com\/eu\/blog\/wp-content\/themes\/salesforce-blog\/dist\/images\/offer-block\/offer-accent-layout-one.png\")
\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\t\t\t![\"\"](\"https:\/\/www.salesforce.com\/eu\/blog\/wp-content\/themes\/salesforce-blog\/dist\/images\/offer-block\/offer-cloud-layout-one.png\")
\n\t\t\n\t<\/div>\n\n\n\nYour next move<\/h2>\n\n\n\n\n- Ensure your AI technology meets company guidelines and industry regulations. <\/li>\n\n\n\n
- Develop a strategic plan with specific use cases. <\/li>\n\n\n\n
- Ask the right questions about AI, starting with\u2026<\/li>\n<\/ul>\n\n\n\n
How good is our data?<\/h2>\n\n\n\n
Generative AI promises to significantly reshape how you manage your customer relationships<\/a>, but it requires data that is accurate, updated, accessible, and complete. Why is this important? You may do something differently this quarter than you did last quarter, based on the latest data. But if your data is outdated or incorrect, that\u2019s what the AI will use. <\/p>\n\n\n\nWhen training your models for generative AI, you should first ensure data excellence from top to bottom. To get your data house in order<\/a>, remove duplicates, outliers, errors, and other things that can negatively affect how you make decisions. Then connect your data sources<\/a> \u2014 marketing, sales, service, commerce \u2013 into a single record, updated in real-time, so the AI can make the best recommendations. <\/p>\n\n\n\nMcKinsey recently wrote<\/a>, \u201cCompanies that have not yet found ways to harmonise and provide ready access to their data will be unable to unlock much of generative AI\u2019s potentially transformative power.\u201d<\/p>\n\n\n\nHow do we establish trust?<\/h2>\n\n\n\n
Trust \u2013 that you will protect customer data and use AI ethically \u2013 is at the heart of how widely and successfully businesses and customers will embrace generative AI.<\/p>\n\n\n\n
Ask yourself, is your technology partner building AI safeguards into the fabric of their systems and apps? Large language models (LLMs), the computer programs upon which AI algorithms are based, contain huge amounts of data but they lack safeguards, controls and privacy features. Companies can leverage AI\u2019s productivity gains without giving away data. <\/p>\n\n\n\n
Trust in AI data privacy requires special safeguards<\/a> including data masking, toxicity detection, data grounding, zero retention, and more. These guardrails protect data and help ensure ethical use, boosting the chances of AI success. <\/p>\n\n\n\nDo we need to reorganise our company around AI?<\/h2>\n\n\n\n
Research<\/a> featured in Harvard Business Review found that AI initiatives face formidable cultural and organisational barriers and that companies need to align their culture, structure, and ways of working to support and scale AI. Ask yourself, do all your stakeholders share that responsibility?<\/p>\n\n\n\nGenerative AI is such a game changer that some companies are establishing cross-functional AI task forces<\/a> to determine how to proceed. Experts suggest<\/a> establishing an AI governance committee to, among other things, guide development teams and set standards for explainability. That is, determining how and why AI makes the recommendations it does. <\/p>\n\n\n\nSchneider Electric, for one, has formalised an AI program<\/a> under a chief AI officer and scaled it to every corner of the company. It implemented a global hub-and-spoke AI operating model. Each business function \u201cspoke\u201d (marketing, sales, service, etc.) has an AI product owner and change agent who works with the tech competency centre \u201chub\u201d to find new uses for AI, deliver the technology, and ensure employee adoption. <\/p>\n\n\n\n\u201cEvery use case \u2014 and we have AI use cases in almost every function \u2014 has people from both the AI Hub and business,\u201d said Madhu Hosadurga, global vice president of enterprise AI at Schneider.<\/p>\n\n\n\n
Do we have the right skills?<\/h2>\n\n\n\n
AI is evolving at such a furious pace, that the answer to this question (for most companies) is probably no. A recent survey<\/a> found that 67% of global business leaders are considering using generative AI, but roughly the same number of IT leaders say their employees don\u2019t have the skills to use it.<\/p>\n\n\n\nBeing an AI-first company (like being digital-first or mobile-first before) requires a close evaluation of your talent. First, you need to determine your current capabilities relative to what you want to accomplish. Identify the gaps, then prioritise building those AI skills. Of course, these will vary by your company\u2019s industry and specific needs. <\/p>\n\n\n\n
You will likely have to develop a hiring plan to acquire talent, and train workers to use generative AI. You can do the latter by providing access to on-demand learning<\/a> for critical skills, incentivizing workers to learn new skills and prioritising upskilling as part of the job. <\/p>\n\n\n\n
The exec summary<\/h2>\n\n\n\n\n- Gen AI will add up to $4.4 trillion<\/a> to the global economy annually. <\/li>\n\n\n\n
- 75% of the potential value from gen AI is concentrated in four functions: customer operations, marketing and sales, software engineering, and R&D.<\/li>\n\n\n\n
- Gen AI, with other automation technologies, could add up to 3.3 percentage points annually to productivity growth, but companies will need to support employees along the way.<\/li>\n<\/ul>\n\n\n\n\n\t\n\n\t\t\n\t\t\t
Everything you need to know about AI<\/h2>\n\t\t\t\t\t\t\t
Our AI Hub is filled with content to help you understand AI better. Glossaries, customer stories, use cases, how-to\u2019s, and more, it\u2019s all here.<\/p>\n\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t
\n\t\t\t\t\tHelp me Learn About AI<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\n\t\t\n\t\t\t\t\t<\/div>\n\t<\/div>\n\n\t\t\t<\/div>\n\t\n\t\t\t\n\t\t\n\n\t\t\n\t\t\t\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\t\t\t\n\t\t\n\t<\/div>\n\n\n\nYour next move<\/h2>\n\n\n\n\n- Ensure your AI technology meets company guidelines and industry regulations. <\/li>\n\n\n\n
- Develop a strategic plan with specific use cases. <\/li>\n\n\n\n
- Ask the right questions about AI, starting with\u2026<\/li>\n<\/ul>\n\n\n\n
How good is our data?<\/h2>\n\n\n\n
Generative AI promises to significantly reshape how you manage your customer relationships<\/a>, but it requires data that is accurate, updated, accessible, and complete. Why is this important? You may do something differently this quarter than you did last quarter, based on the latest data. But if your data is outdated or incorrect, that\u2019s what the AI will use. <\/p>\n\n\n\nWhen training your models for generative AI, you should first ensure data excellence from top to bottom. To get your data house in order<\/a>, remove duplicates, outliers, errors, and other things that can negatively affect how you make decisions. Then connect your data sources<\/a> \u2014 marketing, sales, service, commerce \u2013 into a single record, updated in real-time, so the AI can make the best recommendations. <\/p>\n\n\n\nMcKinsey recently wrote<\/a>, \u201cCompanies that have not yet found ways to harmonise and provide ready access to their data will be unable to unlock much of generative AI\u2019s potentially transformative power.\u201d<\/p>\n\n\n\nHow do we establish trust?<\/h2>\n\n\n\n
Trust \u2013 that you will protect customer data and use AI ethically \u2013 is at the heart of how widely and successfully businesses and customers will embrace generative AI.<\/p>\n\n\n\n
Ask yourself, is your technology partner building AI safeguards into the fabric of their systems and apps? Large language models (LLMs), the computer programs upon which AI algorithms are based, contain huge amounts of data but they lack safeguards, controls and privacy features. Companies can leverage AI\u2019s productivity gains without giving away data. <\/p>\n\n\n\n
Trust in AI data privacy requires special safeguards<\/a> including data masking, toxicity detection, data grounding, zero retention, and more. These guardrails protect data and help ensure ethical use, boosting the chances of AI success. <\/p>\n\n\n\nDo we need to reorganise our company around AI?<\/h2>\n\n\n\n
Research<\/a> featured in Harvard Business Review found that AI initiatives face formidable cultural and organisational barriers and that companies need to align their culture, structure, and ways of working to support and scale AI. Ask yourself, do all your stakeholders share that responsibility?<\/p>\n\n\n\nGenerative AI is such a game changer that some companies are establishing cross-functional AI task forces<\/a> to determine how to proceed. Experts suggest<\/a> establishing an AI governance committee to, among other things, guide development teams and set standards for explainability. That is, determining how and why AI makes the recommendations it does. <\/p>\n\n\n\nSchneider Electric, for one, has formalised an AI program<\/a> under a chief AI officer and scaled it to every corner of the company. It implemented a global hub-and-spoke AI operating model. Each business function \u201cspoke\u201d (marketing, sales, service, etc.) has an AI product owner and change agent who works with the tech competency centre \u201chub\u201d to find new uses for AI, deliver the technology, and ensure employee adoption. <\/p>\n\n\n\n\u201cEvery use case \u2014 and we have AI use cases in almost every function \u2014 has people from both the AI Hub and business,\u201d said Madhu Hosadurga, global vice president of enterprise AI at Schneider.<\/p>\n\n\n\n
Do we have the right skills?<\/h2>\n\n\n\n
AI is evolving at such a furious pace, that the answer to this question (for most companies) is probably no. A recent survey<\/a> found that 67% of global business leaders are considering using generative AI, but roughly the same number of IT leaders say their employees don\u2019t have the skills to use it.<\/p>\n\n\n\nBeing an AI-first company (like being digital-first or mobile-first before) requires a close evaluation of your talent. First, you need to determine your current capabilities relative to what you want to accomplish. Identify the gaps, then prioritise building those AI skills. Of course, these will vary by your company\u2019s industry and specific needs. <\/p>\n\n\n\n
You will likely have to develop a hiring plan to acquire talent, and train workers to use generative AI. You can do the latter by providing access to on-demand learning<\/a> for critical skills, incentivizing workers to learn new skills and prioritising upskilling as part of the job. <\/p>\n\n\n\n
Everything you need to know about AI<\/h2>\n\t\t\t\t\t\t\t
Our AI Hub is filled with content to help you understand AI better. Glossaries, customer stories, use cases, how-to\u2019s, and more, it\u2019s all here.<\/p>\n\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t
\n\n\t\t\n\t\t\t\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\t\t\t\n\t\t\n\t<\/div>\n\n\n\nYour next move<\/h2>\n\n\n\n\n- Ensure your AI technology meets company guidelines and industry regulations. <\/li>\n\n\n\n
- Develop a strategic plan with specific use cases. <\/li>\n\n\n\n
- Ask the right questions about AI, starting with\u2026<\/li>\n<\/ul>\n\n\n\n
How good is our data?<\/h2>\n\n\n\n
Generative AI promises to significantly reshape how you manage your customer relationships<\/a>, but it requires data that is accurate, updated, accessible, and complete. Why is this important? You may do something differently this quarter than you did last quarter, based on the latest data. But if your data is outdated or incorrect, that\u2019s what the AI will use. <\/p>\n\n\n\nWhen training your models for generative AI, you should first ensure data excellence from top to bottom. To get your data house in order<\/a>, remove duplicates, outliers, errors, and other things that can negatively affect how you make decisions. Then connect your data sources<\/a> \u2014 marketing, sales, service, commerce \u2013 into a single record, updated in real-time, so the AI can make the best recommendations. <\/p>\n\n\n\nMcKinsey recently wrote<\/a>, \u201cCompanies that have not yet found ways to harmonise and provide ready access to their data will be unable to unlock much of generative AI\u2019s potentially transformative power.\u201d<\/p>\n\n\n\nHow do we establish trust?<\/h2>\n\n\n\n
Trust \u2013 that you will protect customer data and use AI ethically \u2013 is at the heart of how widely and successfully businesses and customers will embrace generative AI.<\/p>\n\n\n\n
Ask yourself, is your technology partner building AI safeguards into the fabric of their systems and apps? Large language models (LLMs), the computer programs upon which AI algorithms are based, contain huge amounts of data but they lack safeguards, controls and privacy features. Companies can leverage AI\u2019s productivity gains without giving away data. <\/p>\n\n\n\n
Trust in AI data privacy requires special safeguards<\/a> including data masking, toxicity detection, data grounding, zero retention, and more. These guardrails protect data and help ensure ethical use, boosting the chances of AI success. <\/p>\n\n\n\nDo we need to reorganise our company around AI?<\/h2>\n\n\n\n
Research<\/a> featured in Harvard Business Review found that AI initiatives face formidable cultural and organisational barriers and that companies need to align their culture, structure, and ways of working to support and scale AI. Ask yourself, do all your stakeholders share that responsibility?<\/p>\n\n\n\nGenerative AI is such a game changer that some companies are establishing cross-functional AI task forces<\/a> to determine how to proceed. Experts suggest<\/a> establishing an AI governance committee to, among other things, guide development teams and set standards for explainability. That is, determining how and why AI makes the recommendations it does. <\/p>\n\n\n\nSchneider Electric, for one, has formalised an AI program<\/a> under a chief AI officer and scaled it to every corner of the company. It implemented a global hub-and-spoke AI operating model. Each business function \u201cspoke\u201d (marketing, sales, service, etc.) has an AI product owner and change agent who works with the tech competency centre \u201chub\u201d to find new uses for AI, deliver the technology, and ensure employee adoption. <\/p>\n\n\n\n\u201cEvery use case \u2014 and we have AI use cases in almost every function \u2014 has people from both the AI Hub and business,\u201d said Madhu Hosadurga, global vice president of enterprise AI at Schneider.<\/p>\n\n\n\n
Do we have the right skills?<\/h2>\n\n\n\n
AI is evolving at such a furious pace, that the answer to this question (for most companies) is probably no. A recent survey<\/a> found that 67% of global business leaders are considering using generative AI, but roughly the same number of IT leaders say their employees don\u2019t have the skills to use it.<\/p>\n\n\n\nBeing an AI-first company (like being digital-first or mobile-first before) requires a close evaluation of your talent. First, you need to determine your current capabilities relative to what you want to accomplish. Identify the gaps, then prioritise building those AI skills. Of course, these will vary by your company\u2019s industry and specific needs. <\/p>\n\n\n\n
You will likely have to develop a hiring plan to acquire talent, and train workers to use generative AI. You can do the latter by providing access to on-demand learning<\/a> for critical skills, incentivizing workers to learn new skills and prioritising upskilling as part of the job. <\/p>\n\n\n\n
How good is our data?<\/h2>\n\n\n\n
Generative AI promises to significantly reshape how you manage your customer relationships<\/a>, but it requires data that is accurate, updated, accessible, and complete. Why is this important? You may do something differently this quarter than you did last quarter, based on the latest data. But if your data is outdated or incorrect, that\u2019s what the AI will use. <\/p>\n\n\n\n When training your models for generative AI, you should first ensure data excellence from top to bottom. To get your data house in order<\/a>, remove duplicates, outliers, errors, and other things that can negatively affect how you make decisions. Then connect your data sources<\/a> \u2014 marketing, sales, service, commerce \u2013 into a single record, updated in real-time, so the AI can make the best recommendations. <\/p>\n\n\n\n McKinsey recently wrote<\/a>, \u201cCompanies that have not yet found ways to harmonise and provide ready access to their data will be unable to unlock much of generative AI\u2019s potentially transformative power.\u201d<\/p>\n\n\n\n Trust \u2013 that you will protect customer data and use AI ethically \u2013 is at the heart of how widely and successfully businesses and customers will embrace generative AI.<\/p>\n\n\n\n Ask yourself, is your technology partner building AI safeguards into the fabric of their systems and apps? Large language models (LLMs), the computer programs upon which AI algorithms are based, contain huge amounts of data but they lack safeguards, controls and privacy features. Companies can leverage AI\u2019s productivity gains without giving away data. <\/p>\n\n\n\n Trust in AI data privacy requires special safeguards<\/a> including data masking, toxicity detection, data grounding, zero retention, and more. These guardrails protect data and help ensure ethical use, boosting the chances of AI success. <\/p>\n\n\n\n Research<\/a> featured in Harvard Business Review found that AI initiatives face formidable cultural and organisational barriers and that companies need to align their culture, structure, and ways of working to support and scale AI. Ask yourself, do all your stakeholders share that responsibility?<\/p>\n\n\n\n Generative AI is such a game changer that some companies are establishing cross-functional AI task forces<\/a> to determine how to proceed. Experts suggest<\/a> establishing an AI governance committee to, among other things, guide development teams and set standards for explainability. That is, determining how and why AI makes the recommendations it does. <\/p>\n\n\n\n Schneider Electric, for one, has formalised an AI program<\/a> under a chief AI officer and scaled it to every corner of the company. It implemented a global hub-and-spoke AI operating model. Each business function \u201cspoke\u201d (marketing, sales, service, etc.) has an AI product owner and change agent who works with the tech competency centre \u201chub\u201d to find new uses for AI, deliver the technology, and ensure employee adoption. <\/p>\n\n\n\n \u201cEvery use case \u2014 and we have AI use cases in almost every function \u2014 has people from both the AI Hub and business,\u201d said Madhu Hosadurga, global vice president of enterprise AI at Schneider.<\/p>\n\n\n\n AI is evolving at such a furious pace, that the answer to this question (for most companies) is probably no. A recent survey<\/a> found that 67% of global business leaders are considering using generative AI, but roughly the same number of IT leaders say their employees don\u2019t have the skills to use it.<\/p>\n\n\n\n Being an AI-first company (like being digital-first or mobile-first before) requires a close evaluation of your talent. First, you need to determine your current capabilities relative to what you want to accomplish. Identify the gaps, then prioritise building those AI skills. Of course, these will vary by your company\u2019s industry and specific needs. <\/p>\n\n\n\n You will likely have to develop a hiring plan to acquire talent, and train workers to use generative AI. You can do the latter by providing access to on-demand learning<\/a> for critical skills, incentivizing workers to learn new skills and prioritising upskilling as part of the job. <\/p>\n\n\n\nHow do we establish trust?<\/h2>\n\n\n\n
Do we need to reorganise our company around AI?<\/h2>\n\n\n\n
Do we have the right skills?<\/h2>\n\n\n\n
![\"blog-offer-trailhead\"](\"https:\/\/www.salesforce.com\/eu\/blog\/wp-content\/uploads\/sites\/14\/2022\/11\/blog-offer-trailhead.jpg\")
\t\t<\/div>\n\t<\/div>\n\n\t\n\t<\/div>\n","protected":false},"excerpt":{"rendered":"