Companies that apply generation artificial intelligence (AI) to customer-related initiatives can expect to achieve 25% higher revenue after five years than companies focused solely on productivity, according to a study by a consulting firm. Accenture.
The study reveals that 90% of CMOs expect Generative AI to revolutionize their industry and the way their business interacts with customers. Companies using generative AI see an 80% reduction in data processing time, leading to a 40% improvement in the speed to market of new products and services.
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These positive results make generative AI the technology IT feels the greatest pressure to operate. However, nine out of ten IT organizations cannot meet the growing demand for AI-related projects.
One problem is trust. Business success and growth depends trust, data, AI and automationand the latest research on data status and analyzes from Salesforce reveals that a strong database powers AI.
Advances in AI are evolving rapidly, putting pressure on data management teams to provide algorithms with high-quality data. Nearly 87% of analytics and IT leaders say advances in AI make data management a high priority.
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Yet nearly six in ten AI users say it’s currently difficult to get what they want from AI, and more than half say they don’t trust the data used to train AI systems. Current AI. according to a Salesforce 2024 survey (March 20 to April 3, 2024) of nearly 6,000 full-time knowledge workers around the world.
Research suggests that AI lacks the data needed to drive business value, delaying project deployment. Here are 10 key findings from Salesforce’s AI Readiness Survey:
- The value of AI is difficult to obtain: 56% of AI users say it is difficult to get what they want from AI.
- Generative AI solutions need more grounded data: 51% of workers say generative AI lacks the information to be useful.
- The data used to train models is not trustworthy enough: 75% of those who don’t trust the data that trains AI also believe that AI lacks the information it needs to be useful.
- Trust in data delays AI adoption: 68% of those who do not trust the data that drives AI are hesitant to adopt the technology.
- Fundamental models based on public data are unreliable: 62% of workers say outdated public data would break their trust in AI.
- Generative AI results will either build or break customer trust: 71% of employees say consistently inaccurate results would break their trust in AI.
- Trust in data is a top user concern: 54% of AI users do not trust the data used to train AI systems.
- Workers are also concerned about data quality: 68% of workers who don’t trust AI say training data is unreliable.
- Top three priorities for workers using AI: data accuracy (82%), data security (82%), and holistic/comprehensive data (78%)
- The data foundation is key to building trustworthy AI solutions: 53% of employees say training AI on comprehensive customer/business data increases their confidence in the tool.
Generative AI Alone Will Not Improve customer experience. Simply layering generative AI on top of a failing process or using unreliable and incomplete data to train models will not be a magic bullet.
Businesses also face challenges in implementing and adopting AI due to data silos and systems integration obstacles. Up to 90% of IT leaders say it is difficult to integrate AI with other systems. So while AI adoption has exploded and amplified the need for a cohesive IT strategy, achieving that balance is easier said than done.
Every AI project starts as a data project, but success is a long and winding road. Research has shown us the need for a strong database to power the adoption and benefits of AI – and the full potential of data remains elusive in the business world.
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Forty-one percent of line-of-business leaders say their data strategy is only partially or not at all aligned with business goals. Similarly, 37% of analytics and IT leaders believe there is room for improvement. More than six in ten analytics and IT leaders are unaware of how business teams use data or how quickly information can be obtained. Additionally, less than a third of analytics and IT leaders track the value of data monetization.
Improving trust in data is more than a technical solution; culture is key to driving trust and adoption. Data culture refers to the collective behaviors and beliefs of people who value, practice, and encourage the use of data to improve decision-making processes.
Good data literacy gives everyone the insights they need to tackle complex business challenges. Organizations need to dedicate budgets and resources to improve their data, analytics and AI skills. Trust + data + AI + automation = stakeholder success (employees, customers, partners and communities).
