Forrester Uncovers Key Barriers to Achieving Success with Generative AI

2023 marked a pivotal year as generative AI became mainstream, largely inspired by the success of ChatGPT. Now, as we head into 2024, organizations are eager to integrate generative AI into their workflows to unlock its full potential for the enterprise.

However, a recent Forrester Consulting survey of 220 AI decision-makers at North American companies reveals lingering concerns about the risks of generative AI, alongside barriers that hinder broader adoption.

Addressing Major Roadblocks

The survey highlights significant obstacles to operationalizing generative AI, including common challenges like hallucinations. These issues leave many organizations stuck in exploration or experimentation phases, inhibiting their ability to implement foundational models for planned use cases.

Recognizing Generative AI's Transformative Potential

Despite these challenges, organizations across various sectors recognize the transformative capabilities of generative AI. In the Forrester survey conducted on behalf of Dataiku, 83% of respondents indicated they are exploring or experimenting with generative AI. Notably, over 60% view it as critically or highly important for their business strategy, with plans to increase investments in data and AI initiatives by as much as 10% in the coming year.

Business leaders expressed that they have already identified multiple applications for generative AI, including:

- Enhancing customer experiences (64%)

- Product development (59%)

- Self-service data analytics (58%)

- Knowledge management (56%)

The survey reflects a growing enthusiasm for the diverse applications of generative AI, with respondents anticipating improved offerings and operational efficiencies within the next two years.

Challenges to Implementation Remain

Despite optimism, significant roadblocks to effective generative AI adoption persist. Key challenges include potential violations of data protection and privacy laws (31%) and the need for improved skills and governance (31%) to navigate the complexities of generative AI. Additionally, over 50% of leaders highlighted the risks of biases and hallucinations that could compromise output quality.

Crucially, these risks are exacerbated by insufficient infrastructure for generative AI. The survey identified inadequate data infrastructure as the foremost barrier, with 35% of respondents citing challenges in data consumption, storage, and sharing. Respondents also reported difficulties in integrating with existing systems (35%) and computational limitations (27%).

Other noted barriers include governance mechanisms (35%), AI interpretability and explainability (25%), skill gaps (31%), and model scalability.

Proposed Solutions for Challenges

To overcome these implementation hurdles, organizations should adopt collaborative approaches that leverage AI platforms offering pre-packaged solutions for accelerated development, seamless integration, and robust governance frameworks.

According to McKinsey, generative AI has the potential to add between $2.6 trillion and $4.4 trillion to annual corporate profits globally, with the greatest impact expected in the banking, high-tech, and life sciences sectors.