USING THE WRONG AI CAN CREATE PROBLEMS. IT'S IMPORTANT TO STUDY DIFFERENT MODELS AND MAKE THE RIGHT CHOICE FOR FOR YOUR BUSINESS

Incorporating artificial intelligence into daily operations can set a business apart. However, recent developments suggest both IBM's Watson and Amazon's Alexa have encountered challenges, underscoring the importance of a cautious approach to integrating AI into your business strategies.

Here's a closer look at the future of conversational AI in business analytics and why you must carefully evaluate its integration into these processes.

The revolution of conversational AI in business analytics

The underlying natural language processing in conversational AI allows businesses to interact with data and customers in a more intuitive and human-like manner. The upsides are obvious: from enhancing customer service with AI-driven chatbots to enabling more accessible data analytics through conversational interfaces.

As far back as 2016, IBM's Watson promised incredible leaps ahead in analyzing complex data sets, notably through initiatives like Watson Oncology at the Memorial Sloan Kettering Cancer Center. But although there was much promise and millions of dollars poured into its further development, it came up short. Watson was discontinued in several Genomic projects, as shared in a New York Times report in 2021.

As a recent CNBC article reports, IBM decided to bring Watson back, marketing it as a platform for training other machine learning models. With any evolving technology, there will be highs and lows, and this is a great example of the long arc of innovation when it comes to era-defining technologies like AI.

Similarly, Amazon's Alexa has been a leader in incorporating voice-activated technology into daily business and consumer environments through its incubator programs since 2020, offering a user-friendly interface that enhances operational efficiency and customer experience. More recently, Amazon announced it's combining Alexa with LLM technology to create a more compelling chatbot experience.

IBM's Watson and Amazon's Alexa exemplify the evolving impact of conversational AI in business and how it changes over time.

A reality check on conversational AI

Despite their advanced capabilities, potential benefits, and recent developments, both Watson and Alexa have faced their share of challenges. A 2024 Business Insider report reveals that Amazon has been thinking about introducing a subscription model for Alexa, an idea that signals possible difficulties in monetizing the technology source. Such developments raise questions about the long-term viability and cost implications of incorporating conversational AI systems into business operations. For entrepreneurs and startups operating on tight budgets, the prospect of additional subscriptions or unforeseen expenses can be a significant concern.

Businesses attracted by the promise of conversational AI and decision-making tools must navigate the gap between expectations and reality.

The lessons here are twofold: First, the importance of tempering enthusiasm for new technologies with a critical assessment of their current capabilities and limitations, and second, the need for ongoing evaluation of these tools as they evolve.

Navigating the pitfalls by overcoming AI challenges

The allure of conversational AI technologies like Watson and Alexa is undeniable. Many new NLPs are coming into the market all the time. At my company, Aigo, where we provide cognitive AI solutions, I preach the importance of careful implementation with thorough use cases that provide a foundation that can be leveraged for faster internal development and activation.

It's essential to approach integration with caution. You should keep several key factors in mind:

1. Cost Versus Benefit Analysis: Understand the full scope of costs involved, including any potential subscriptions or additional fees. Evaluate whether the benefits of integrating AI technologies outweigh these costs, especially in the early stages of your business.
2. Realistic Expectations: Set realistic expectations for what these technologies can achieve. While AI can offer valuable insights and efficiencies, it is not a silver bullet for all business challenges.
3. Ethical and Privacy Concerns: Conversational AI involves the collection and processing of vast amounts of data, including potentially sensitive information. Businesses must ensure they adhere to data protection regulations and ethical standards, safeguarding customer privacy and trust.
4. Technical Support and Expertise: The integration of AI technologies requires a certain level of technical know-how. Consider the availability of technical support and the need for in-house expertise to manage and optimize the use of these tools effectively.
5. Scalability and Flexibility: As your business grows, your needs will change. Assess whether the AI solutions you're considering can scale with your business and if they offer the flexibility to adapt to evolving requirements.

The opportunities and challenges are multiplying

The journey of integrating conversational AI into business operations is fraught with both opportunities and challenges. By conducting thorough due diligence and maintaining a critical eye, businesses can harness the power of AI to drive innovation and growth while avoiding the pitfalls that have trapped others. In doing so, they can move closer to realizing the full potential of conversational AI in shaping the future of business analytics and customer engagement.

EXPERT OPINION BY SRINI PAGIDYALA, CO-FOUNDER, AIGO.AI @SRINI_PA