By Christine DeFelice
Developing an Artificial Intelligence (AI) strategy shares many elements with a Customer Experience (CX) strategy. Both involve capturing areas for improvement, effective communication with employees and customers, training, and reliable security measures.
What is AI strategy? It’s a high-level roadmap to blend AI into an organization, combining it with broader business and automation goals for full impact.
Guiding a successful AI strategy can reveal a variety of benefits for organizations. It empowers organizations to include AI with business goals, streamline processes (automate repetitive tasks, personalized experiences, predictive recommendations and responsive support services) and drive business transformation. In addition to this, it ensures an organized integration of AI for the success of the organization. Maximize AI’s benefits by joining it with your business model.
Some of the finding that we see today is that data analytics and AI can greatly facilitate process-based innovation.
Firms that use AI tools are more oriented toward process improvement experience greater productivity gains. Just one standard deviation increase in investment in AI tools are associated with 7% more productivity. What that means, if a firm invested in AI, but not interested in improving their processes, they would not experience that 7% productivity gain.
Only when you have both the investment in AI tools and orientation toward process improvements do you see the productivity gains. In fact, the more a company is oriented toward product and service innovation, the less likely they are to invest in AI.
AI is a powerful tool. It’s important to be aware of the risks when incorporating AI into your businesses. AI risk management identifies, analyzes and faces any potential risk while using AI. Be aware of some of these risks when integrating AI into your organization, these include:
- Model Training
- Data privacy & Personal Information
- Bias
- Fair and ethical use
Model Training
Training data sets are the framework of an AI model, making accuracy crucial. When training AI models, be aware not to use sensitive personal information (SPI).
Sensitive Personal Information
This type of information includes details such as social security numbers, financial information, government ID’s, health records, biometric data, and information about an individual’s race, ethnicity, religion, sexual orientation or political beliefs.
The other common elements of model training for AI includes:
- Bias – Monitor and audit your systems regularly to detect vulnerabilities. Be aware of any biased results that would be considered discrimination.
- Fair and Ethical Use – AI risk management focuses on reducing ethical risks like discrimination. Without in-depth oversight, AI in lending or hiring could unintentionally harm customers by acting unfairly. As an organization be aware of the laws around AI compliance. Noncompliance could mean fines and legal trouble.
To reduce AI risk: examine each potential risk upon discovery, prioritize them, and use a decision tree guide for assessment. Establish monitoring programs and alarm systems to detect issues early.
Take it a step further by implementing AI data governance to oversee data use and management. Use change management to guide your organization through transitions.
Skill Development
An integral part of AI strategy is upskilling your employees with AI skills that’ll be in-demand. Explore the following AI skills that would be an asset to your employees:
- AI Integration in Business Processes – Train your employees to notice where AI can be used in current work flows to be more productive.
- Basic Prompt Engineering – Educate your employees with a basic understanding of how prompt engineering works. Prompt engineering is a critical aspect of working with AI language models, like OpenAI ChatGPT. It involves designing and refining the input prompts given to these models to produce the desired output effectively and efficiently.
Data Literacy – Being familiar with data sources, quality, and biases will help your employees make informed decisions from AI outputs.
- Ethical Implications of AI – At a minimum, publish a high-level principles guide to your organizations AI strategy. For example, providing a principle for transparency and explainability would help guide your strategy. Take a look at this white paper “The Ethics of AI Ethics: An Evaluation of guidelines” for more details on ethics and AI.
AI Tool Familiarity – Educate your employees on popular AI platforms and tools. Focus on those that are designed for your industry. This will build your employees confidence in both using and interacting with AI solutions.
AI security and compliance – Recognize potential security risks and practice compliance with regulations.
Natural Language Processing (NLP) – Becoming skilled in NLP is crucial when interacting with customers through AI. (chatbots and voice assistants.)
In summary here are some AI upskill guidelines:
- Identify where skill gaps exist within your organization.
- Explore the types of training and development programs you need.
- Survey your employees.
- Find courses, lectures and trainings.
- Keep trainings brief to about 45 minutes long to allow for questions and answers.
- Track the effectiveness of these AI trainings with testing, surveying, or polling.
- Give the employees who attend a certain number of AI trainings credentials.
In conclusion, a strategic AI plan focused on value creation, risk mitigation, and organizational readiness is crucial for successful AI implementation. By adopting risk management, prioritizing ethics, and investing in organizational change, businesses can drive innovation, boost efficiency, and achieve sustainable growth with AI.