Artificial intelligence (AI) technologies are already transforming various industries, helping professionals become more productive. Whether it is finding the right information from your database or generating customized reports from vast datasets, these models have simplified many processes and sped up different administrative tasks.
However, integrating these tools can be challenging.
Businesses need to learn about these evolving tools continually and put effort into them over time to use them effectively. Moreover, enterprise-grade AI solutions can be expensive, and getting used to a new workflow takes time.
In this article, let’s look at four best practices from experts that can help businesses and enterprises ease into their new AI-augmented workflow.
1. Data accuracy and security for reliability
It is quite natural that experts across industries scrutinize the output of AI models due to issues like hallucination and incorrect data retrieval. Additionally, organizations also find it difficult to trust these tools with business and customer data, considering jailbreak prompts can be used to steal that information (at least partially).
Philip Snow, CEO of FactSet, a leader in the fintech industry, recognized the gravity of this challenge. FactSet’s clients use the tool to manage portfolios, generate reports, analyze risks, etc., which made it crucial to deliver an AI solution that is accurate and secure enough.
In an interview with CNBC, Snow revealed that the FactSet team has been working for years to ensure a robust technical infrastructure that provides the right data to these AI models for all applications while protecting sensitive information from malicious attacks.
He added how this had boosted the stakeholders’ confidence in FactSet to deliver reliable AI technologies which was reflected in the increased adoption of the relevant features.
Organizations looking to integrate AI into their business operations should, therefore, begin with building a safe technical architecture and testing the models thoroughly. This is important for shipping intelligent solutions and building a future-proof infrastructure to accommodate new AI upgrades.
2. Run industry-relevant standardized tests
After building a secure data infrastructure for your AI models and workflows, organizations need to thoroughly test all the moving parts. These tests should not only test the general use cases but also edge cases where the likelihood of failure (hallucination, incorrect output, etc.) is high.
Moreover, the results from these comprehensive evaluations can be used to train and fine-tune AI workflows even further through techniques like Reinforcement Learning from Human Feedback (RHLF).
Arvind Krishna, chairman and CEO of IBM, talked about the importance of establishing a series of standardized tests for AI models in the Think 2024 event. He emphasized that the leaders in the AI space should come together to formulate thorough and unbiased evaluation criteria.
IBM, in collaboration with Meta and 100+ organizations, has formed the Open AI Alliance to create such tests. These organizations range from different industries including corporate and academia and are working together to help businesses integrate ethical AI into their workflows.
Keep in mind that these tests are continuously evolving to help AI-enabled organizations assess the efficacy of their custom models accurately. Following that closely can get challenging for organizations that are already undergoing AI transformation.
Moreover, testing and iterating the AI models until they are reliable enough to be used can be costly when done sub-optimally. Too many moving parts, coupled with operational disruptions can delay AI integration.
An effective way to navigate this problem is by partnering with AI consulting firms and getting a few things done externally. For instance, in this case, these firms can take your data to build a model and iterate it based on your feedback until it is ready for adoption.
3. Focus on long-term AI governance
AI governance is an evolving set of values, guidelines, and action items that facilitate the responsible adoption of AI tools and technologies within organizations. This ensures the whole process is ethical and unbiased while respecting the data rights of all the stakeholders involved.
If the last few years have told us anything about AI, it is that AI is a rapidly evolving technology with growing capabilities. This necessitates a governance framework that is simultaneously iterated to manage the relevant tools and processes.
Samta Kapoor, partner and principal at Ernst and Young (E&Y), recommends that companies looking to integrate AI into their business operations think about governance right from the design stage. She added that such a structured approach to AI integration will continuously correct bias and retain its utility for the organization over time.
Simply put, AI governance requires continuous attention from various stakeholders and an agile approach that allows businesses to remain proactive. This is crucial to make relevant enhancements in how AI is being used within the organization in the long term.
4. Democratization is key
Democratization enables every stakeholder to participate in the AI transformation in a business through education, training, and careful design.
Education and training refer to providing learning resources to employees and helping them through it. Careful design is all about considering the needs and capabilities of the end user while making operational changes.
Enhancing workflows is a sensitive period for any team or organization in any industry. Companies should maintain a culture of transparency, collaboration, and continuous learning to ensure a smooth transition.
Param Kahlon, Chief Product Officer at UiPath, agrees that unlocking the true potential of AI in business operations relies upon empowering every stakeholder to use AI by themselves, regardless of technical expertise or experience.
Businesses, while augmenting their operations with AI, should discuss the upcoming changes with all the team members and help them with the transition. Creating an internal knowledge base where all the updates and relevant information can be pooled and accessed together can be valuable.
Key Challenges in AI Implementation for Businesses and Enterprises
Looking forward: Next steps for businesses
It has become essential for businesses to integrate AI into their workflows on a day-to-day operational level. Experts throughout this space have given the following valuable insights that can make this process easier:
- Ensure you collect accurate data which is stored securely
- Test your AI models thoroughly before integrating
- Create an evolving framework to manage AI usage
- Empower every stakeholder to participate
However, putting the above best practices into action can be challenging. For starters, there is a learning curve, and building the foundations for AI transformation can take a while. Additionally, a scarcity of experienced professionals who can manage all this seamlessly adds to the difficulty.
These challenges make things slow, expensive, and error-prone.
Companies need to move fast on the path to AI integration while minimizing costly mistakes along the way to reap its full potential. An effective, easy, and affordable way to achieve that is by partnering with experienced AI consulting company.
GrowExx specializes in helping companies adopt AI into their workflows to unlock productivity and efficiency. We develop and implement custom AI solutions and systems for organizations in healthcare, finance, education, transportation, real estate, and more.
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