Companies that apply generative artificial intelligence (AI) to customer-related initiatives can expect to achieve 25% higher revenue after five years than companies only focused on productivity, according to research from consultancy Accenture. 

The research reveals that 90% of CMOs expect generative AI to revolutionize their industry and how their business interacts with customers. Companies using generative AI are seeing as much as an 80% reduction in data-processing time that supports a 40% improvement in speed to market with new products and services. 

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These positive results mean generative AI is the technology IT feels the most pressure to exploit. However, nine in 10 IT organizations can't support the growing demand for AI-related projects.

One issue is trust. Business success and growth depend on trust, data, AI and automation, and the latest research on the state of data and analytics from Salesforce reveals that a strong data foundation fuels AI. 

Advances in AI are fast-moving, which puts pressure on data management teams to supply algorithms with high-quality data. As much as 87% of analytics and IT leaders say advances in AI make data management a high priority.

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Yet nearly six in 10 AI users say it's difficult to get what they want out of AI right now, with over half claiming they don't trust the data used to train today's AI systems, according to a Salesforce 2024 survey (March 20 to April 3, 2024) of almost 6,000 full-time global knowledge workers.

The research suggests AI lacks the data needed to deliver business value, which delays the rollout of projects. Here are 10 key findings of Salesforce's AI readiness survey: 

1. Value from AI is difficult to attain: 56% of AI users say it's difficult to get what they want out of AI.
2. Generative AI solutions need more grounded data: 51% of workers say generative AI lacks the information to be useful.
3. 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 needed to be useful.
4. Trust in data is delaying AI adoption: 68% of those who don't trust the data that trains AI are hesitant to adopt the technology.
5. Foundational models based on public data are not trustworthy: 62% of workers say out-of-date public data would break their trust in AI. 
6. Generative AI output will make or break customer trust: 71% of workers say consistently inaccurate outputs would break their trust in AI.
7. Trust in data is a major user concern: 54% of AI users don't trust the data used to train AI systems.
8. Workers are also concerned about data quality: 68% of workers who don't trust AI say the training data is unreliable.
9. The top three priorities for workers using AI: accuracy of data (82%), data security (82%), and holistic/complete data (78%)
10. Grounding data is key to building trustworthy AI solutions: 53% of workers say training AI on comprehensive customer/company data builds their trust in the tool.

Generative AI on its own will not improve the customer experience. Simply layering generative AI on a broken process, or using untrustworthy and incomplete data to train models, will not provide a magical Band-Aid. 

Businesses are also having difficulties with AI implementation and adoption because of data silos and system integration obstacles. As many as 90% of IT leaders say it's tough to integrate AI with other systems. So, while AI adoption has exploded and amplified the need for a coherent IT strategy, achieving that balance is easier said than done.

Every AI project begins as a data project, but success is a long, winding road. Research has shown us the need for a strong data foundation to fuel AI adoption and benefits -- and data's full potential has been elusive in business. 

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Forty-one percent of line-of-business leaders say their data strategy has only partial or no alignment with business objectives. Similarly, 37% of analytics and IT leaders see room for improvement. Over six in 10 analytics and IT leaders are in the dark about line-of-business teams' data utilization or speed to insight. Furthermore, fewer than one-third of analytics and IT leaders track the value of data monetization. 

Improving trust in data is more than a technical fix; culture is critical to driving confidence and adoption. Data culture is the collective behaviors and beliefs of people who value, practice, and encourage data use to improve decision-making processes. 

The right data culture equips everyone with insights for tackling complex business challenges. Organizations must devote budgets and resources to enhance their data, analytics, and AI skills. Trust + data + AI + automation = stakeholder success (employees, customers, partners, and communities). 

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• The best free AI courses (and whether AI 'micro-degrees' and certificates are worth it)
• I tested the AI gadget that got the internet buzzing and it left me wanting more