There is no doubt that artificial intelligence exploded onto the business scene last year in a big way; disrupting processes, enhancing data analysis and innovation, as well as raising concerns around privacy and ownership. It certainly seems like AI was everywhere and we invited business experts to ask some important questions about what impacts it might have on business operations as we know them.
To wrap up the year, Stratford hosted a panel event this past November with several industry experts to discuss how business leaders and organizations can make strides towards integrating AI into their operations while mitigating the inherent risks.
Sitting on the panel moderated by Stratford’s AJ Harris were:
- Geoffrey North - VP Intellectual Property at Intellectual Property Ontario (IPON),
- Chantal Bisson-Kroi – VP AI & ML Solutions at Kinaxis,
- Matt Lemay – CEO of Lemay AI, and
- Rana Chreyh – Practice Leader of Management Consulting at Stratford Group.
Thanks to their collective expertise, one thing became abundantly clear: AI is redefining business landscapes with unrivalled speed and scope.
Are you ready the explore the art of the possible when it comes to transforming your business with AI? Here, we distill the expertise shared by our panelists into actionable knowledge for today's business leaders.
The Speed of AI Adoption: A Disruptive Force
When asked what their thoughts were on the speed with which AI seems to have entered the collective business landscape, our panel concurred that AI has permeated business with unprecedented velocity, outpacing (for the most part) even the internet's transformative influence. This swift adoption stems from a confluence of factors: a global workforce dedicated to AI development, improved communication channels, and a lower barrier to entry. The accessibility of AI not just as a business tool but as a universal utility stands as a pillar of its rapid disruption.
Implementing AI: A Strategic Blueprint
Business leaders seeking to implement AI should start by identifying how AI aligns with their core business imperatives. AI solutions range from specialized applications, like predictive analytics, to broader customer experience enhancements through affective computing. The key is not the adoption of technology for its own sake but its potential to add value and enhance capabilities within the organization.
It can be helpful to understand the various AI applications to see how they can be specifically the applied to your unique business case. This can assist you in strategically deciding where to make investments depending on your objectives.
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- Specialized AI: Solves specific problems, focuses on optimizations and predictive analytics.
- Generative AI for Customer Experience: Explores affective computing for understanding sentiment, enhancing customer experience.
- Administrative Efficiencies: Targets automating business processes, including notetaking and data extraction, for improved efficiency.
- Product or Service Enhancement: Injects AI into existing products or services to reimagine and differentiate, providing a competitive advantage.
The Value of AI: Defining Success
Once you’ve identified how you would like to apply AI, how do you go about defining the sort of value it can add? We recommend starting with these key questions:
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- What does success look like?: Organizations need to clearly define success metrics, measure progress, and ensure alignment of cultural values across the organization. It is crucial to have a shared understanding of the problem to be solved.
- Ownership and accountability: Identify who will ultimately own the AI-related challenges. This includes managing the system, realigning it with key performance indicators (KPIs), and making decisions in case of experiment failure.
AI in Supply Chain: Optimizing Efficiency
With a supply chain expert on the panel, we couldn’t let the evening go by without addressing AI’s applications in supply chain management.
AI can significantly improve supply chain management in various ways:
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- Demand Prediction: AI can be employed to predict demand in supply chain planning, with an emphasis on explainable AI to understand the factors influencing predictions.
- Data Utilization: AI excels in leveraging diverse data sources, such as events, weather, and sentiment, to enhance decision-making. It allows for the efficient analysis of complex data sets that may be challenging for humans to handle.
- Cost Reduction: Organizations can harness AI to identify innovative ways to reduce costs. For instance, American Airlines utilized AI for taxiing on runways, resulting in substantial savings by optimizing plane movements.
- Risk Management: AI aids in efficiently managing risks associated with a large number of vendors. For example, a company with 30,000 vendors successfully used AI to identify vendors compliant with insurance requirements, streamlining the vendor onboarding and management process.
From optimizing plane taxiing to streamlining vendor compliance, AI's applications are as varied as they are impactful. The key message is to embrace AI without fear, acknowledging its diverse applications and potential to bring about significant improvements in supply chain efficiency, cost savings, and risk management.
Intellectual Property: Navigating AI's Legal Landscape
Also sitting on the panel was an IP expert, so our next question focused on addressing the intricacies of data ownership, privacy concerns, bias, and regulatory compliance require thoughtful navigation and often expert or legal counsel.
Any discussion about AI implementation should strongly consider Intellectual Property (IP) due to various risks:
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- Ownership Issues: Understanding ownership of both input and output is crucial. Knowing the terms and conditions related to data usage is essential. The issue of ownership extends beyond input data to the output generated by AI tools. Ownership may depend on factors such as modification, input, and the extent of humanization of the output. As AI-related IP issues are evolving and can vary case by case, it's crucial to seek legal advice and stay informed about the ongoing developments in this field.
- Security and Privacy Concerns: Organizations should address security and privacy concerns related to the data they provide for AI training. Ensuring that data is protected is crucial, especially in the context of sensitive information.
- IP Protection: AI-related innovations are being protected through patents, and businesses must be aware of potential infringement issues. Conducting landscape reports and consulting with IP specialists can help navigate these complexities.
Timing and Strategy: When to Introduce AI
Integrating AI isn't just about technology—it's about readiness, competitive advantage, and strategic timing. Build foundational AI skills, align initiatives with business imperatives, and ensure data readiness. Starting early can afford a crucial competitive edge.
Data: The Foundation of AI
Let’s expand a bit on that final point about data readiness, as our panellists emphasized that every AI project is fundamentally a data project, which highlights the need for good-quality data as the bedrock of AI. Although initial data limitations are common, starting with what you have and improving iteratively is a recommended path to AI maturity. It helps if organizations view it as more of a continuous journey rather than a fixed destination.
Developing an AI Strategy: A Multifaceted Approach
The next question we posed to our panellists revolved around AI and business strategy, specifically, how to incorporate AI into strategic planning.
An effective AI strategy involves a comprehensive approach that aligns with business goals, leverages existing resources, seeks external input, ensures leadership commitment, develops necessary skills, prioritizes data quality, embraces agile methodologies, manages risks effectively, and fosters a culture of experimentation and continuous improvement.
AI Project Launch
Before launching an AI project, consider the broader context. Key points to know before starting an AI project include:
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- Build vs. Buy Decision: Organizations need to decide whether to build the AI solution in-house or buy it in parts or pieces, depending on their business nature and requirements. This decision involves assessing the organization's capabilities and determining the most efficient and effective approach.
- Understanding the AI Landscape: Recognize that AI is a vast and diverse field, similar to medicine, with specialists in various areas. Understand the specific use cases the organization is targeting and hire or work with professionals accordingly.
- Start with Use Cases: Begin the AI project by identifying and understanding the use cases. Determine the specific problem areas that AI can address and evaluate whether the necessary data is available.
- Learning Enough to Ask the Right Questions: Develop a sufficient understanding of AI to ask the right questions and interpret the answers. Make sure there is someone in your organization or that you have access to who can act as a translator - a person who can convert technology to business, business to technology. Somebody who can marry the use cases to what the art of the possible is.
Final Thoughts: Embracing AI with Strategic Intent
In conclusion, the panelists stress the importance of approaching AI with a strategic mindset, aligning it with overall business goals, and considering the broader implications of AI in various fields. The integration of AI should be accompanied by thoughtful planning, risk management, and a focus on hiring and developing the necessary talent.
Myriam Davidson captured the essence of our AI discussion with a resonant takeaway: "Be open to the art of the possible but maintain focus." This principle reflects the expansive potential of AI when applied with strategic intent, ensuring AI is a tool not just for innovation, but for responsible, value-driven business transformation.