2024 research from Accenture predicts the economic impact of Generative AI on businesses.
The research suggests that more than $10.3 trillion in additional economic value can be unlocked by 2038 if organizations adopt Generative AI responsibly and at scale.
Additionally, business leaders surveyed note that Generative AI will ultimately increase their company’s market share, with 17% predicting a market share increase of 10% or more. 95% of employees see value in working with Generative AI – but their main concern is that they don’t trust organizations to ensure positive outcomes for everyone from the introduction of the new technology.
Meanwhile, MuleSoft’s ninth annual Connectivity Exchange Report, based on interviews with 1,050 IT leaders worldwide, reveals that the AI tipping point reinforces the need for a coherent IT strategy. 87% of IT leaders say the nature of digital transformation is changing. AI is further complicating the technology landscape, with 991 applications in the average enterprise. Their budgets need to grow to meet this growing demand.
The survey also found that concerns about integration and security are the biggest barriers to AI adoption. The dilemma is already evident, with over three-quarters of organizations reporting that they are using multiple AI models. 90% say that the difficulty of integrating AI with other systems is a barrier, while 79% cite security concerns .However, perhaps the biggest barrier to AI adoption is a combination of data warehouses and system vulnerabilities that are holding companies back. Almost uniformly, 98% of IT leaders report facing challenges related to digital transformation.
Maintaining data warehouses is cited by 81% of respondents, and the vulnerability of systems that depend on data is cited by 72%. Data warehouses prevent automation projects from being completed within the expected time and budget. Automation continues to be a point of contention between IT and the business. Business users are benefiting significantly from automating their work (1.9 hours per employee per week) and are demanding more flexibility in automation. However, the majority of IT departments have yet to understand how to enable this automation in a secure and regulated manner. Two-thirds (66%) of automation projects have IT as the sole gatekeeper.
To better understand the impact of adopting Generative AI to improve customer experience, we interviewed two of the industry’s leading experts in customer relationship management (CRM), customer experience (CX), and customer service. Michael Maoz is senior vice president and chief innovation officer at Salesforce. Prior to joining Salesforce, Maoz was senior vice president of research and distinguished analyst at Gartner, serving as a research leader for customer service and support strategy. Ed Thompson is senior vice president of market strategy at Salesforce. Prior to joining Salesforce, Thompson was senior vice president of research and distinguished analyst at Gartner, covering customer experience (CX) and CRM strategy and implementation. Maoz and Thompson shared their insights on what businesses should consider and implement before implementing Generative AI solutions into their customer service applications and processes.
Prior to joining Salesforce, Thompson was a senior research analyst and distinguished analyst at Gartner, covering customer experience (CX) and CRM strategy and implementation. Maoz and Thompson shared their insights on what businesses should consider and implement before implementing Generative AI solutions into their customer service applications and processes.
Over 95% of large organizations are testing or already have some form of Generative AI in place. These companies have moved beyond research and evaluating the possibilities and have begun to take action. Mid-sized organizations often have identified over 100 potential use cases, and the largest have over 500. Among the business owners we work with, responsible for marketing, sales, customer support, and overall customer experience, the conversation has moved from “What can Generative AI do?” to “Does Generative AI deliver value for my unit, my business, and my customers?”
Few organizations have made headlines using GEN AI for drug discovery or chip design, as they use specialized internal resources to tune large language models for high-value, innovative use cases. Some organizations have specialized in tuning small language models for specific industries or a single type of high-value process. However, a much larger percentage of organizations have taken a “lower risk” approach and started with internal projects that do not directly expose GEN AI results to customers or suppliers.
