Tech Search Advisory
Artificial Intelligence
What do business leaders expect from an AI Deployment?
Executives of mid-sized companies are increasingly interested in generative AI due to its potential to enhance business operations and drive innovation. Here are some key areas of interest:
Improved Efficiency and Productivity
Streamlining processes and automating repetitive tasks can significantly increase productivity. Data-driven insights and recommendations enable enhanced decision-making. Additionally, AI can generate high-quality content quickly and efficiently.
Enhanced Customer Experience
AI empowers us to deliver tailored experiences based on individual customer preferences. We can provide faster and more accurate responses to customer inquiries, and create new products and services that meet evolving customer needs.
Competitive Advantage
AI can help us differentiate from competitors by enabling the development of unique and innovative products or services. It can also identify opportunities to reduce costs and improve profitability, as well as generate new revenue streams through innovative applications.
Risk Mitigation
Predictive analytics can forecast future trends and identify potential risks. AI-powered fraud detection can help prevent financial losses, and enhanced cybersecurity measures can protect sensitive data.
Innovation and Growth
AI can generate new product ideas and concepts, as well as identify new markets and opportunities for growth. By continuously driving innovation, we can stay ahead of the competition.
Here are just a few cross-industry use cases that are easy places to start with an AI deployment
Customer / Member Experience
● Outbound customer surveys
● Self-service payment mgt.
● Personalized greetings
● Check balances
● Fraud Alerts
● Phone / digital payment processing
● Omnichannel
● Upsell / Cross sell
● Sentiment Analysis
● Customer segmentation
Appointment Assistance
● Patient forms
● Pre-screen
● Check in – forms check
● Data integration
● Results delivery
● Prescription notify
● Self-serve claims
Employee Engagement
● Satisfaction Survey
● HR self-service
● Enhanced portal experiences
Authentication, Identity Management
● Verification
● User Authentication before getting to an agent
● Biometrics
● Security questions
Finance / Fraud
● Balance Info
● Payment processing
● Payment status
● Fraud scoring
● Credit approvals
● Payment plans
● Collections
Notifications, Reminders
● Holiday/Weather-related closures
● Delivery notices
● Fraud
● Deposits, Transactions, ad Payments
● Appointments, Reschedules
● Ready for pickup
● Emergencies
● Abandon Carts
Content Creation and Personalization
● Personalized email campaigns
● Custom product descriptions
● Social media content
● Design and Branding
● Concept generation
● Market Research
● Data analysis
● Competitor analysis
Consider This
Do you acquire a standalone IA Platform, or would you be best served to upgrade existing applications to new, AI-enabled solutions?
The decision between acquiring a standalone AI platform or upgrading existing applications to AI-enabled solutions depends on several factors. If your existing applications provide a solid foundation and can be effectively integrated with AI capabilities, upgrading may be the most cost-effective and efficient approach. However, if your current applications are outdated or lack the necessary infrastructure to support AI, a standalone platform might be the better choice.
Industry Fact: It is estimated that there will be 250,000 AI enabled applications by 2026
Boston Consulting Group (BCG) suggests that even modest investments in specific AI use cases can generate up to 6% more revenue, and with rising investments, the revenue impact from AI can triple to 20% or more.
More Decision Criteria
Stand-Alone AI Solution
● New functionality: When the AI solution provides entirely new capabilities that the existing application cannot offer. For example, if a company wants to implement predictive analytics for customer churn, and the existing CRM system doesn't have this functionality, a stand-alone AI solution might be more suitable.
● Scalability and performance: If the existing application is struggling to handle increased data volumes or complex tasks, a stand-alone AI solution with better scalability and performance characteristics might be necessary.
● Integration challenges: If integrating AI capabilities into the existing application would be technically complex or time-consuming, a stand-alone solution might be a more efficient option.
● Vendor lock-in: If the existing application is tied to a specific vendor or technology stack, a stand-alone AI solution could provide more flexibility and avoid vendor lock-in.
Replacing an Existing Application with an AI-Enhanced Version
● Existing application foundation: If the existing application provides a solid foundation and can be enhanced with AI capabilities, it might be more cost-effective and efficient to replace it rather than build a new solution from scratch.
● Integration benefits: If the AI capabilities can be seamlessly integrated into the existing application, providing a more unified user experience and reducing the need for additional training.
● Leveraging existing data: If the existing application already has access to relevant data that can be used to train and improve the AI model.
● Risk mitigation: Replacing the existing application with an AI-enhanced version can help mitigate risks associated with legacy systems and improve security.
Ultimately, the best approach will depend on the specific needs and circumstances of the business. It's often beneficial to conduct a thorough evaluation of both options to determine the most suitable path forward.
A successful project is all about the data.
Here’s a few questions you should discuss with your staff
● Who has access to your data today? Do you have a Least Privilege Access policy protecting against employee theft or damage?
● What tools do you have in place to administer LPA?
● Are your Identity and Access Management tools up to date?
● Is your data properly labeled?
● How will future sensitive data that is created using AI be protected?
Other data questions
● Where is your sensitive data?
○ Company controlled environment like your server room or secure COLO?
○ Is it in one of the hyperscalers or passed back and forth?
● What are your Retention policies?
○ Is Data being kept too long or not long enough?
○ Are your retention policies compliant with applicable standards?
● Can you identify unusual patterns in data access?
○ Do you have security tools in place to detect anomalies in exfiltration, sharing and access?
Protecting Data from LLM Import: A Guide for Midsize Companies
US-based midsize companies can take several proactive steps to prevent their data from being inadvertently imported into a large language model (LLM) once they deploy an AI application:
- Data Anonymization and Masking:
● Remove PII: Eliminate personally identifiable information (PII) such as names, addresses, and social security numbers.
● Data masking: Use techniques like data perturbation or synthetic data generation to obscure sensitive information while preserving data utility.
- Data Isolation:
● Separate environments: Keep sensitive data and the LLM in distinct environments to prevent unauthorized access.
● Limit data access: Grant only necessary personnel access to sensitive data and ensure they follow strict protocols.
- Secure Data Transfer:
● Encryption: Use HTTPS or other secure protocols to protect data during transmission.
● Data integrity: Implement checksums or other verification methods to ensure data hasn't been tampered with.
- Model Access Controls:
● Restrict usage: Limit the LLM's access to sensitive data or functions.
● Monitor behavior: Continuously monitor the LLM's interactions with data to detect any unauthorized or suspicious activity.
- Data Retention and Deletion Policies:
● Set guidelines: Establish clear policies for data retention and deletion to prevent unnecessary storage of sensitive information.
● Regular review: Periodically review and update these policies to align with evolving security best practices.
- Third-Party Vendor Oversight:
● Evaluate security: Carefully assess the security practices and certifications of third-party LLMs or data services.
● Contractual clauses: Incorporate strong security clauses into contracts with third-party vendors.
- 7. Regular Security Audits and Assessments:
● Identify vulnerabilities: Conduct regular security audits and assessments to identify and address potential vulnerabilities.
● Stay updated: Keep security measures up-to-date with the latest industry standards and best practices.
The Bottom Line
The bottom line is that US-based mid-sized companies should prioritize data security and privacy when preparing for an AI project. This involves implementing robust access controls, encryption, and data anonymization techniques. Additionally, companies should establish clear data governance policies to ensure data quality and consistency. By proactively addressing data security, mid-sized businesses can mitigate risks and maximize the benefits of Gen AI while protecting sensitive information.
By implementing these strategies, US-based midsize companies can significantly reduce the risk of their data being misused in imported LLMs and protect their sensitive information.
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