The State of Generative AI in the Enterprise

1. Excitement about GenAI remains high, but current efforts remain most focused on efficiency, productivity, and cost reduction rather than innovation and growth. Over three-quarters of respondents (79%) expect GenAI to drive substantial organizational transformation in less than three years. Yet the majority report a strong focus on more tactical benefits. A full 56% of respondents are currently targeting improved efficiency and productivity, and 35% are targeting cost reduction. Only 29% are targeting innovation and growth—mirroring what we’ve historically seen with other technologies at the beginning of their adoption curves.
2. There is broad uncertainty about how to manage talent, governance, and risk when it comes to GenAI. Only 22% of leaders believe their organizations are highly or very highly prepared to address talent-related issues related to GenAI adoption, and the vast majority of leaders (72%) expect GenAI to drive changes in their talent strategies within the next two years. Yet, many organizations are not focused on education and reskilling—only 47% agree that they are sufficiently educating their employees on the capabilities, benefits, and value of GenAI. Further, only a quarter of leaders believe their organizations are highly or very highly prepared to address governance and risk issues related to GenAI adoption.
3. Leaders worry that GenAI will drive greater economic inequality and see a need for more global regulation and collaboration. More than half of respondents are concerned that the widespread use of GenAI will centralize global economic power (52%) and increase economic inequality (51%). To address these concerns, the majority of respondents agree there is a need for more global regulation (78%) and collaboration (72%) to manage the responsible widespread adoption of GenAI.
4. Organizations with self-reported very high expertise in GenAI tend to feel more positive about it—but also more pressured and threatened. Leaders of such organizations report higher levels of employee interest, technical preparation, and ongoing transformation associated with GenAI; however, they also feel more pressure to adopt it, and view it as more of a threat to their business and operating models.
5. Most organizations are still primarily relying on off-the-shelf GenAI solutions. The majority of organizations are using off-the-shelf solutions (e.g., publicly available GenAI applications/LLMs, and productivity applications and enterprise platforms with integrated GenAI); relatively few are using more focused and differentiated solutions (e.g., industry-specific GenAI applications and private LLMs).

Creating value

• The percentage of organizations already achieving leaders’ expected benefits from Generative AI (GenAI) to a “large” or “very large” extent is 18% to 36%, depending on the type of benefit being pursued.
• Leaders that report “high” or “very high” levels of GenAI expertise in their organizations are scaling it much more aggressively and achieving their desired benefits to a much greater degree than others.
• Organizations primarily plan to reinvest the savings from GenAI into innovation (45%) and improving operations (43%)—addressing the value equation from both sides.

Scaling up

• Leaders see scaling as essential for creating value, increasing GenAI’s impact on the business, and expanding GenAI’s user base. The scaling phase is when GenAI’s potential benefits are converted into real-world value. It’s also, however, when potential concerns can become real-world barriers to success.
• Common areas of concern include data security and quality, the explainability of GenAI outputs, and worker mistrust or lack of familiarity with GenAI tools.
• Workforce access to approved GenAI tools and applications remains quite low, with nearly half of surveyed organizations (46%) providing approved GenAI access to just a small portion of their workforce (20% or less). However, most workers with internet access will have access to public GenAI tools and could be using them without consent.

Building trust

• Lack of trust remains a major barrier to large-scale GenAI adoption and deployment. Two key aspects of trust we observed are: one, trust in the quality and reliability of GenAI output; and two, trust from workers that GenAI will make their jobs easier without replacing them.
• Trust issues aren’t preventing organizations from rapidly adopting GenAI for experiments and proofs of concept, with 60% of leaders believing they are effectively balancing rapid implementation with risk management. Trust is likely to become a bigger issue, however, as organizations transition to large-scale deployment. Many are currently investing significant time and effort into building GenAI guardrails.
• Leaders that report “high” or “very high” levels of GenAI expertise in their organizations recognize the importance of building trust in GenAI across numerous dimensions, such as input/output quality, transparency, and worker empathy, and are implementing processes to improve it to a much greater extent than other organizations.

Evolving the workforce

• Most leaders (75%) expect GenAI to affect the talent strategies of their organizations within two years; 32% report “very high” levels of GenAI expertise and are already making changes.
• The most expected talent strategy impacts are process redesign (48%) and the need for upskilling or reskilling (47%).
• GenAI is expected to increase the value of some technology-centred skills, such as data analysis, as well as human-centred skills, including critical thinking, creativity, and flexibility), while decreasing the value of other skills.
• In the short term, more leaders expect GenAI to increase headcount (39%) than to decrease headcount (22%) at their organizations—perhaps due to an increased need for skills and knowledge in GenAI and data.

2024 has been a turning point for the relationships Canadian businesses have with Generative AI (GenAI). As investments surge and expectations rise, the pressure is on for GenAI to transform potential into proven, measurable results.

Our latest report reveals how organizations are working together to bridge the gap between innovation and execution. How they’re using collaboration around GenAI to boost productivity, build trust, and capture real value. Canadian organizations must adapt swiftly, whether tightening governance or boosting data-driven decision-making, to ensure they lead in the next wave of innovation.

Key findings from the Q3 global report:

Building on initial success

• 42% of respondents said efficiency, productivity, and cost reduction were the most important gains achieved through GenAI.
• 58%, however, said they’ve realized a more diverse range of important benefits, like enhanced innovation, improved products and services, or better customer relationships.
• Respondents said integrating GenAI into essential business functions and processes is the best way to maximize value from their AI initiatives.

Striving to scale

• Two-thirds of organizations are boosting their GenAI investments after seeing significant early results.
• But nearly 70% said a small portion of their AI experiments have moved into full production, highlighting a stalling ability to scale.

Modernizing data

• 75% of organizations have invested in data lifecycle management to support their GenAI strategies, focusing on data security (54%) and quality (48%).
• 55% said they’re avoiding certain AI use cases due to concerns like using sensitive data and managing privacy and security issues. These data challenges are limiting options for scaling AI initiatives.

Mitigating risk

• Only 23% of organizations feel highly prepared to manage the risks and governance challenges posed by GenAI.
• Risk, regulation (such as the European Union’s AI Act, in effect August 1), and governance issues are three of the four biggest challenges holding organizations back from developing and deploying GenAI tools.
• 50% of organizations surveyed are conducting regulatory forecasts or assessments to navigate regulatory uncertainty.

Maintaining momentum

• Over 40% of respondents find it challenging to define and measure the impact of their GenAI initiatives.
• Under 50% said they are using specific key performance metrics to measure GenAI performance, and many standard measures of success aren’t currently being applied.