No Match Found
In recent months, generative artificial intelligence has captured the imagination of individuals and organizations alike. Due in large part to its novelty and accessibility, it has achieved usage milestones exponentially faster than previous breakthrough platforms and technologies.
While recent developments are, in practical terms, simply another marker in what has been a long evolutionary journey for artificial intelligence, it’s clear that the era of generative AI is here. As organizations race to embark on generative AI journeys, we see the potential for this technology to accelerate profoundly transformative forces across companies, the economy and society.
With Canadian organizations eager to understand what this means for them, many are looking for answers to key questions, such as:
What makes the latest developments different from prior advances in AI?
What impacts will this technology have on their businesses?
What key risks and considerations do they need to address?
How can they choose where to apply generative AI for maximum value from among the many options and use cases available?
While generative AI raises many important issues, building and maintaining trust will need to be a foremost consideration for those looking to make the most of the opportunities this technology creates. Read below to learn more and find out what generative AI could mean for your organization as you assess your approach to this disruptive technology.
While there are many questions and uncertainties surrounding the stability, security and reliability of generative AI, it marks the beginning of a period of experimentation, integration and adoption that will play out across the economy. Compared to AI-oriented innovations that have emerged in the past, we believe the conditions are in place for this technology to grow at a bigger scale and a significantly faster pace than before. Key reasons include:
Cloud and digital maturity, combined with widespread access to the computing power needed to enable large data models, is key to the rapid growth of generative AI.
Generative AI has reached a tipping point in creating tangible value for employees from applications they can easily incorporate into their workflows. For example, developers are using generative AI-powered tools to co-pilot their coding activities.
Organizations have direct visibility into use cases applicable to end users, such as customers.
From email auto-complete features to chatbots and curated news feeds, there’s already a baseline of acceptance of the role computer-generated or augmented content can play.
Contextualization: The ability to understand and contextualize language gives generative AI broad applicability, bringing it closer to many of the cognitive tasks we all engage in.
Synthesis: Generative AI’s ability to replicate mid-level cognitive tasks (such as summarization, contextualization, basic synthesis and inference) places it squarely in the domain of knowledge work that has historically been out of scope for early AI and automation activities.
As newer and more powerful generative AI models emerge at an unprecedented pace, organizations that don’t pay attention risk falling behind. But successfully capturing the opportunities depends on an organization's ability to manage several key impacts of generative AI:
As organizations assess their approach to generative AI, they’ll need to very quickly deal with a number of key considerations for what it means to operate in an economy enabled by this transformative technology:
adhering to responsible AI principles by addressing critical risks related to transparency, privacy and consent;
developing a strategy for determining which opportunities to prioritize for maximum value, advantage and results;
rapidly building their capability as a partner of partners operating in collaborative ecosystems;
ensuring they can support people through change and help them incorporate new tools and technologies into ways of working and interacting; and
building a robust technology strategy that includes foundational cloud and data analytics capabilities that underpin the successful deployment of generative AI.
A key issue running through these considerations is trust, which will be paramount as organizations embark on the generative AI journey. This is because a technology that can contextualize, summarize, and, in limited situations, interact autonomously with customers and colleagues using insights from large bodies of individualized data raises many questions about how organizations can build and nurture trust with key stakeholder groups.
Employees, for example, will need to learn to work alongside the technology to scale their productivity but will also expect their authenticity and autonomy will be preserved.
Customers who will come to rely on higher velocity and lower latency of interactions with organizations will expect authenticity and explainability of what’s presented. They’ll also want control over the data they share and protection of their privacy.
Institutions will need to build deeper, more collaborative relationships with ecosystem partners by creating, structuring and fairly distributing value generated through shared insights, platforms and data.
Governments and regulators will need to balance innovation and regulation to ensure ongoing competitiveness across Canada’s economy while preserving fundamental protections for users and competitors. They’ll need to create an orderly transitional framework that creates certainty in the investment climate while putting in place the appropriate safeguards to avoid misuse of information, reinforcement of biases and dislocations in the market.