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Making Big Money from AI

According to Accenture research, businesses who utilize generative artificial intelligence (AI) to customer-related projects should see a 25% increase in income after five years compared to those that just concentrate on productivity.

Ninety percent of CMOs believe generative AI will completely change their sector and the way their company engages with clients, according to the research. Businesses utilizing generative AI are reporting up to an 80% decrease in data processing time, which enables a 40% increase in new product and service speed to market.

Given these promising outcomes, generative AI is the technology that IT is under the most pressure to adopt. Nine out of ten IT companies are unable to meet the increasing demand for projects linked to artificial intelligence, nevertheless.

Among the problems is trust. The most recent study from Salesforce on the health of data and analytics shows that a solid data foundation powers AI, and business success and growth depend on trust, data, AI, and automation.

Since AI advances are happening quickly, data management teams are under pressure to provide algorithms with high-quality data. Data management is now a top issue for as many as 87% of IT and analytics executives.

A Salesforce 2024 poll of over 6,000 full-time worldwide knowledge workers found that over six in ten AI users said it’s hard to get what they want out of AI right now, with over half saying they don’t trust the data used to train today’s AI systems (March 20 to April 3, 2024).

According to the report, the absence of data required by AI to provide commercial value causes project rollouts to be delayed. Following are ten salient conclusions from Salesforce’s AI readiness survey:

  • Achieving value from AI is challenging: 56% of AI users report having trouble getting what they want from the technology.
  • More solid data is needed for generative AI solutions: According to 51% of workers, generative AI isn’t enough informational.
  • Insufficiently reliable data is used to train models: Seventy-five percent of those who don’t trust the data AI uses also think AI isn’t knowledgeable enough to be helpful.
  • Adopting AI is being delayed by a lack of trust in the data; 68% of people are reluctant to use the technology.
  • Trustworthy foundational models are not those based on public data: 62% of employees claim that outdated public data will make them lose faith in AI.
  • Customer trust would be either made or broken by generative AI output: according to 71% of employees, continuously erroneous outputs will cause them to lose faith in AI.
  • A key user worry is trust in data; 54% of AI users don’t trust the data used to train AI systems.
  • Quality of the data worries workers as well; 68% of those who don’t trust AI claim that the training data is unreliable.
  • Workers employing AI rank accuracy of data (82%), data security (82%), and holistic/complete data (78%), in that order.
  • It takes grounding data to create reliable AI solutions: 53% of employees claim that using AI to train on extensive client and business data increases their confidence in the tool.

By itself, generative AI won’t enhance the user experience. It won’t work to simply apply generative AI on top of a malfunctioning process or train models on unstable and incomplete data.

Organizations are encountering challenges in implementing and embracing AI due to issues with data silos and system integration. Up to 90% of IT executives claim that integrating AI with other systems is difficult. Thus, as the use of AI has increased dramatically, so has the necessity for a well-thought-out IT strategy; nevertheless, striking that balance is more said than done.

Every AI project starts out as a data project, but getting there is a difficult journey ahead. The benefits and acceptance of AI have been demonstrated by research, but realizing the full potential of data in business has proven to be tricky.

41 percent of line-of-business executives claim that there is either no or very little alignment between their data strategy and corporate goals. Likewise, 37% of IT and analytics executives believe there is still space for development. More than 60% of IT and analytics leaders are ignorant of the data usage and speed to insight of line-of-business teams. Furthermore, just 33% of IT and analytics leaders monitor the value of data monetization.

It takes more than just a technological solution to increase trust in data; adoption and confidence are largely dependent on culture. People who value, practice, and promote the use of data to enhance decision-making processes are known as data culture practitioners.

Everyone is better able to tackle difficult business challenges when there is a proper data culture in place. To improve their data, analytics, and AI capabilities, organizations need to commit funds and resources. Stakeholder success (workers, consumers, partners, and communities) is achieved through trust, data, AI, and automation.

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