How AI is Impacting Product Transformation for Enterprise Customers

We are at a moment in time when everyone has sold their treasured NFTs (non-fungible tokens, like cryptocurrencies), used their Bitcoins, and big data is a thing of the past and a new technology has emerged.

Artificial Intelligence (AI) and emerging technologies under the AI umbrella (like ChatGPT) are hot topics right now. Everyone knew that the age of AI was upon us, yet somehow we all were taken by surprise and now it’s all anyone can talk about.

GPT (Generative Pre-Trained Transformer) is a type of neural network that can perform various natural language processing tasks such as summarizing text, answering questions, and even generating code. The release of ChatGPT sent a shock wave throughout the world. Seemingly overnight, businesses are being forced to find ways to adapt and change to the new reality. Product and Engineering organizations find themselves at the forefront of this transformation. No one knows all of the answers, but some insightful guiding principles have begun to emerge as we all absorb and digest the risks and opportunities.

In this article, we’ll share some insights on how a fast-growing, data-centered organization, such as Loopio, is choosing to deal with this disruption through product functionality.

Approach Innovation With An Eye on Tradeoffs

Product transformation in the face of GPTs is necessary and inevitable. We don’t have the option of staying still when disruption of this magnitude occurs. The main questions are “what to do” and “how to do it” since this change comes with risks and tradeoffs. As technology leaders, it is our responsibility to weigh these with caution and pave the path forward. To a large extent, the actions taken will be a function of the market and customer demand. By nature, large Enterprises are risk averse and their needs have to be considered carefully. In contrast, smaller companies are more risk-seeking and willing to adopt new technologies faster. A strategy that targets large Enterprises calls for action that takes a well-rounded and deliberate approach.

At Loopio, our view is that an organization’s GPT/AI strategy, vision, and positioning are more important than hastily introducing GPT-powered features. Innovation is the lifeblood of fast-growing companies. There are no shades of gray — without innovation, a company risks becoming obsolete. Introducing new GPT-enabled features is much needed, but Enterprise-focus calls for balance and pace. It is important to evaluate security risks, and data privacy concerns, and decide on a mitigation plan. There have already been a few well-publicized security-related incidents that made us all take pause and think deeply about the way we appraise these threats while capturing the opportunity in front of us. A well-thought-out feature launch, data protection, and mitigation plan are a must.

A GPT is Just One Tool in the AI Toolkit

While GPTs is the latest AI tool, it should not be the only tool used exclusively to address all product needs. Consider all of the technologies in your arsenal when crafting an AI strategy. Some good suggestions are:

Machine Learning (ML) Libraries and Frameworks: Numerous open-source libraries and frameworks (such as TensorFlow, PyTorch, and sci-kit-learn) provide pre-built functions and modules for implementing AI solutions. These resources can help accelerate the development of innovative AI-powered products.

Natural Language Processing (NLP): NLP helps computers understand, interpret, and generate human language. Tools such as sentiment analysis, chatbots, and language translation services can be used to create innovative products and enhance user experience. This is the obvious space that ChatGPT disrupts but there are other simpler tools that can be implemented.

Targeted search solutions: Semantic search which goes beyond simple keyword matching and considers the context and meaning of words in a query, personalized search using algorithms that analyze a user’s search history, preferences, and behavior to deliver personalized search results, query understanding to identify key phrases.

This is by no means a comprehensive list, but they all have a place in creating a positive customer experience and comprehensive solution. Customers will embrace the vision only if it aligns with their values and priorities. Being reliable, predictable, and thorough constitutes a good partner for big corporations.

Content is Gold

LLMs and GPTs are valuable only if they provide the correct answers. Anyone can produce a quick response, but having the right information is what leads to value creation.

We are now in a world of information without attribution. When an answer is provided, there is no list of sources associated with it that builds confidence. Generated replies stand on their own and the user is left to their own devices to judge their accuracy. In order to ensure validated output, it is important to lean heavily on having reliable training data on which to tune the models. The model learns from anything and everything it is supplied with, without knowledge of the sources or content attribution provided along with the answers.

Create an implementation framework where suggested answers carry along with it a confidence score, and spend the time to define the confidence threshold above which accuracy is deemed sufficiently high. A GPT becomes very powerful when it is trained on dependable data. Valid, vetted data will prevent the danger of hallucination and will build trust among customers.

The Importance of Taking a Thoughtful Approach to AI

Loopio employs a team of data scientists and machine-learning engineers constantly evaluating and developing AI/ML features across the Response Management workflow — including features that leverage LLMs such as ChatGPT.

Irrespective of the specific nature of the AI technology, Loopio evaluates the viability of incorporating these solutions against what we consider the three Trust Pillars for Enterprise-ready AI:

• secure and private access to customer data
• transparency of technology, data transmissions, and model training data
• explainable outcomes and sources of data

Furthermore, sustainable, long-term trust in AI solutions requires a thoughtful approach to the human experience. In Loopio, this starts with the customer’s trusted content library as the source of truth for the machine to learn and generate responses. It further requires intuitive interfaces for subject matter experts to vet and opt-in to AI functionality; as well as provide continuous input to the AI as outputs approach machine scale.

Ultimately, Enterprise customers have told us that the level of risk assumed in leveraging AI technologies must be up to them. Our long-term solution to integrating AI technologies into the platform revolves around a continuous and balanced approach to:

• research and evaluate new AI technologies against tenants of Enterprise trust
• explore the viability of AI-powered solutions across the entire response workflow and design with the human in mind.

Read more about Loopio’s approach to GPTs and see our technology in action.