Demystifying AI: Turning Hype into Practical Solutions for Businesses
Artificial intelligence (AI) has rapidly transitioned from a futuristic concept to an integral component of modern business operations. However, amid the excitement, it’s crucial to distinguish between the hype and the practical applications that can genuinely benefit organizations.
In the webinar we hosted on Jan. 23 – “Demystifying AI: Turning Hype into Practical Solutions” – we explored how businesses can effectively integrate AI into their strategies. Mike Dietrich, CIO of McLaughlin Moore, walked us through valuable information.
CLICK HERE to watch the webinar or keep reading for a recap.
How It Began
OpenAI’s ChatGPT brought AI to the mass market in 2022. It created a lot of interest when it first launched, but it took businesses a while to really grasp the concept.
According to Mike, we have been here before – in 2012, when technology began moving to the cloud. In fact, the cloud was one of the key drivers of the sudden leap forward in AI. Without the infrastructure and services created as part of the evolution of public clouds, AI would be impossible.
Today, Oracle, Microsoft, AWS and Google Cloud Platform all offer cloud-based AI services that are provisioned through the same console that engineers use to provision other cloud services.
Business-Centric AI Concepts: A Short Summary
In short, AI attempts to mimic human intelligence. AI can be broken down into two categories:
- Artificial General Intelligence (AGI) – If we can replicate any human intelligence capabilities in machines, it’s AGI
- Artificial Intelligence – AI is when we apply AGI to solve problems with specific, narrow objectives
Defining Types of AI
Machine Learning (ML) – Algorithms learn from past data to predict outcomes on new data or to identify trends from past data. This is highly useful for automation, establishing policies and identifying complex anomalies. For example, ML can be used for detecting fraudulent bank transactions or cyberthreats. ML can scour a vast amount of data and detect what shouldn’t be there.
Deep Learning (DL) – Algorithms learn from complex data using neural networks and predicts outcomes or generates new data. Deep learning is a subset of ML that focuses on training artificial neural networks (ANNs) with multiple layers. It goes a step farther than ML, in that it can tackle complex data and multi-dimensional problems and generate new data. This can be useful in the finance or trading or medical fields, for example,
Generative AI – This is a subset of AI, ML and DL. Generative AI generates and creates new data. For businesses, it can augment responses with your internal structured and unstructured data, taking into account the nuances related to your business.
Key AI Terms
Large Language Models (LLM) – A type of AI that can process, understand and generate human languages. Language model is a probabilistic model of text. LLMs are trained on massive amounts of text data, allowing them to process and generate human-like text, mimicking the way humans communicate. LLMs are used in chatbots, virtual assistants, content creating, language translation and code generation. OpenAI, Meta AI, Cohere, Google and Microsoft all create LLMs.
Vectors and Vector Databases – Specialized database system that stores and queries high-dimensional vector data, with advanced distributed storage, scaling and complex query capabilities. A vector is a sequence of numbers, used to capture the important “features” of the data. Vectors represent the semantic content of data, not the underlying words or pixels.
Retrieval Augmented Generation (RAG) – RAG is making AI more business-centric, because it allows the ability to bring your own data into a LLM and speak the language specific to your business.
Agents – A software program that uses AI to perform tasks independently. Agents interact with the environment, take action and learn from feedback.
Hallucinations – This is model-generated text that is incorrect, misleading or nonsensical. An LLM may generate a coherent, rational response that is partially or totally fictional. The impact: When AI systems are being used to make critical decisions, such as medical diagnoses, financial trading or recommended next best actions to take during a cybersecurity breach, the impact of hallucinations can be disastrous. When AI cites sources in results, check to see where data is coming from and whether it’s factual and correct.
What AI Means to Business
Generally, there are two schools of thought about what AI means for businesses:
- A means to increase efficiency, automate tasks and reduce costs, including reducing headcount
- A means to innovate and accomplish goals previously considered out of reach. For example, solving global problems related to electronic health records, or innovating with autonomous databases or zero trust packet routing
In business, introducing AI is not the goal. Implementing augmented intelligence is the goal.
When business leaders implement AI with the specific goal to improve the decision-making speed and accuracy that leads to greater effectiveness and efficiency, they achieve augmented intelligence. Augmented intelligence is accomplished by integrating AI into a business’ existing value streams and creating new ways of working.
Common business value streams include quote to cash (sales-finance), recruit to hire (HR), procure to pay (SCM-finance), ideation to implementation (IT). An AI value stream is made up of AI-assisted enhancements to value stream flows aimed to simplify, accelerate and increase efficiency and effectiveness.
The goal is to make your people better at what they do by giving them enhanced tools in the form of AI to help them make better decisions faster and more consistently, drive out waste and drive in effectiveness.
Implementation Options for AI Value Stream and Augmented Intelligence
According to Mike, there are three options for implementing AI value stream and augmented intelligence in an organization. A foundational strategy, a value stream strategy and a platform strategy.
With a platform strategy, SAAS, ERP, CRM, SCM and HCM platforms are designed based on business value streams, simplifying the implementation of augmented intelligence. This platform is the fastest, SAAS-based, features embedded AI and is agent-based.
Recommendations for a Successful Journey to Augmented Intelligence
The best place to take innovation is to look at the areas where you make your money and/or where your pain points exist. Find where you need assistance and start with what is it critical for your business. Where would AI best change the dynamic? Look for opportunities to use AI to drive more customers or generate more revenue or save money.
In addition:
Leverage RAG: Prepare your data, especially unstructured data
Implement a factory-approach, preparing your data using a process of assessment, eliminating gaps, bringing data current, purging obsolete data and publishing.
Invest in a central office
To sustain transformations leveraging new innovations, develop expertise in change and decision management.
Adopt a platform strategy
This approach accelerates the adoption of new ways of working in your critical value streams and secures it from within a controlled environment.
Final recommendations for adopting innovative technologies
- Let go of the past
- Always adopt innovative technology
- Use a GPS model on your journey
- Institute a central office model
Looking for an MSP that understands the value AI can bring businesses? Contact us today to learn how we can help.
