OpenAI proudly announced that ChatGPT gained 1 million users in its launch week—achieving this milestone in just 5 days. To put it in perspective, Instagram took approximately 2.5 months to reach 1 million users, and it took Netflix 3.5 years to reach the same number.
The popularity of generative AI tools like ChatGPT, Bard, Claude, or Jasper is undeniable. However, there’s a significant difference between having users and fully adopting a tool.
For instance, the issue becomes complex when platforms as vast as Salesforce integrate generative AI into their CRM solutions and workflows, automating processes, producing content, and handling most of the routine work.
While 45% of Americans are using generative AI, according to Salesforce, it’s not all smooth sailing. There are several barriers that delay the full adoption of these powerful tools, which have the capability to boost efficiency and free up employees’ time for high-value activities.
The tech experts at InclusionCloud detail the key barriers to AI adoption in organizations in relation to three fundamental aspects:
- Organizations not ready for AI due to lack of digitization.
- The skill gap generates concerns, resistance, and delays in initiatives.
- The cultural shift required; it’s not only about technology.
To understand the complete picture, we need to look at how the human and technological nuances that hinder generative AI’s widespread adoption are impacting U.S. businesses.
Three Main Adoption Barriers
A barrier that hinders generative AI often relates to the digital transformation journey of an organization. Essentially, if a company hasn’t fully embraced digital transformation—meaning it hasn’t digitized its operations, data, and processes to a modern, digital-first approach—it may struggle to implement and leverage generative AI effectively. This is because generative AI requires access to vast amounts of quality data, digital tools, and processes to function optimally. Without a solid digital foundation, integrating GenAI becomes a complex challenge, limiting the potential benefits these innovations can offer.
But adopting generative AI is not only a technological endeavor; it’s deeply intertwined with cultural and human aspects. Successful technological change within businesses requires a shift in mindset across the entire organization. This encompasses recognizing the value of AI beyond its technical capabilities, fostering an environment open to learning and adaptation, and ensuring that all company processes are aligned with these digital advances. Cultivating a culture that embraces change, encourages innovation, and supports continuous learning is as crucial as the technology itself for a holistic and sustainable digital transformation.
With these clarifications made, we can now go with the three key barriers to generative AI adoption in Salesforce:
1. Data as the Fuel of AI, So Data Quality Is Key
A crucial barrier is the quality and quantity of data. AI systems heavily rely on data. When the available data is of poor quality or limited, the effectiveness of GenAI significantly diminishes. Salesforce Einstein’s capacity to train AI models using a company’s own data is a critical feature, emphasizing the importance of data quality and richness. To overcome this barrier, data scientists play a vital role in maintaining high data quality standards and conducting thorough data audits.
2. The Talent Gap and the Need for Specialized Skills
Despite Salesforce being a low-code platform, the lack of professionals with expertise in Salesforce and specific industry knowledge can make the adoption process daunting. The talent gap, particularly the shortage of skilled professionals like data engineers and AI modelers, as reported by 35% of respondents in a Salesforce study, is one of the main barriers to the wider adoption of generative AI.
3. Cultural Change and Organizational Resistance
Another major hurdle is the need for a cultural shift within organizations to successfully adopt AI. Skepticism about AI’s decision-making process, fears of AI replacing human jobs, and a general lack of understanding or trust in AI’s capabilities often lead to resistance. Additionally, AI integration demands changes in business processes and workflows. Issues like AI governance and risk management, and concerns about trustworthiness and bias in data, further compound these challenges.
In summary, the biggest barriers to AI adoption in businesses encompass not just technical aspects like data quality and the talent gap but also broader organizational issues such as cultural resistance and concerns over governance, cost, and data ethics. These factors collectively underscore the complexity of integrating AI into business environments.
This leads us to the next question: Is a partner truly necessary to implement AI if my company uses the Salesforce platform?
Do You Really Need a Salesforce Partner to Implement AI?
Many organizations are tempted to handle Salesforce AI implementation in-house, attracted by the perceived control and familiarity with internal processes. However, this approach often reveals hidden complexities. The challenge lies not just in setting up Salesforce AI but in tailoring it to align with unique business processes, integrating it seamlessly with existing systems, and maximizing its capabilities for your team. This level of customization and integration demands expertise and time that in-house teams may not have.
A strategic implementation partner offers much more than technical proficiency.
They bring a deep understanding of business operations and can customize Salesforce AI to meet your strategic goals. They are skilled in handling challenging integrations, overseeing complex data migrations, and ensuring that your team is adept at using the system effectively.
