How Does AI Consulting Work?

AI consulting services are the next step for software development companies during the next decade. Why do I say this? It’s simple; the job of a software developer is becoming less hands-on and more focused on orchestrating complex systems. Companies know that the shift demands a strategy, how that strategy is executed however is up to the business. In this article, we’ll be going over the AI consulting route, a strategy that helps software development companies continue to add value in an evolving tech landscape. 

The introduction of AI has presented an opportunity, not a threat, to companies working in custom software development. The decades of expertise leading up to this point have given us the knowledge required to push companies forward using new technologies. However, the most challenging aspect, which has been difficult for years, is finding that perfect match and getting companies and clients to embrace the change. However, this is what’s going to make AI consultancy so inextricably valuable during this time of change. 

How Years of Software Development Have Prepared Us For AI

Artificial Intelligence seemed to form a rain cloud over businesses that are now scrambling for a cover, that is, a new strategy. However, these companies working in Marketing, Retail, Finance, Healthcare, you name it, might lack the required expertise to effectively execute a digital transformation with AI. But like any change, a little guidance can make a world of difference. 

Software development before modern tools has forged a toolkit for providers that directly carries over to success with analyzing businesses and integrating AI. Here are 3 key carryovers:

Agile Development: We’ve recently talked about the benefits of agile development with AI but it’s certainly worth a reiteration. Agile methodology aligns significantly with the iterative nature of AI development, a context where models are trained, evaluated, and refined over and over. The experience gained from agile development methodologies will be great when identifying opportunities and eventually integrating AI since it facilitates efficient development and adaptation to evolving requirements.

Data Management and Processing Ability: Software development has always been about working with mass amounts of data since applications need to analyze and manage large volumes of it. This experience has provided a solid foundation for AI, where again, data plays a crucial role. Software developers have expertise in data modelling, database management, and manipulating data, which are all going to be essential for AI algorithms to learn and perform up to standard.

Algorithmic Thinking: Breaking down a complex problem into micro steps like you would when writing code is going to serve well when approaching Machine Learning algorithms. What algorithmic thinking does here is slow you down and allow you to identify the specific objectives, data requirements, and preprocessing steps necessary for the system.

So first of all, what does a company look for in an AI consultancy? Well, it isn’t just one thing, it’s going to be unique in every case, but the following questions are typically the best to have answers to before seeking help:

1. How critical is it for you to implement an AI solution?

   • What does your company stand to gain from implementing AI? Before you can expect consultants to deliver you a high-quality product, you must first understand and believe in the vision. 

2. Does your consultant know what they’re doing?

   • It might seem like an oxymoron but depending on the consultant's background, their experience levels with AI and your industry can vary. Look for the ones who have been around for a while and who have a deep understanding of AI technologies in addition to relevant industry experience.

3. How will an AI system align with your business objectives?

   • Is your staff prepared to handle a new system? Companies that have succeeded for decades know that any changes made to workflows need to align with the company's values and goals. 

It’s the small details that make a big difference when it comes to selecting an AI consultant. But just like any software project, companies have to understand the purpose of integration.

Working With Your Chosen Consultant

Okay, so you’ve found someone you’re comfortable with - you’re confident in the vision behind your AI digital transformation, you work out the details with your consultant and now it’s time to get to work… What does the process look like?

When collaborating with an experienced team, the initial step involves conducting an assessment of your current software development resources and initiatives. What this assessment will do is identify any gaps or shortcomings in your current approach. These gaps are going to be the focus of your work together as it’s where you’re going to implement the solutions. 

Next, you’re going to go over it together. By doing so, you can align on the specific challenges and areas that require attention.

During these discussions, the team will want your input and gather additional context about your priorities. This approach with an emphasis on collaboration ensures that the proposed solutions are not only appropriate but align with your strategy and vision so that they can be sustained long-term. Then, you’ll take a look at the recommendation outline which is where you’ll see the solution proposals. You can then give your input and begin to think about logistics.

Beyond this, the process becomes focused on refining and implementing the solutions. It will prioritize user training, as your team will need to understand the unique aspects and functionalities of the AI solutions that they wouldn’t be able to get answers to anywhere else. 

What is it Going to do For You?

We could list the benefits of AI and make it look pretty but the fact is even the benefits change depending on the context. What AI brings to the table for a finance company won’t be the same as what it brings to a healthcare organization. The true value of AI consultancy is its ability to customize and tailor solutions to your specific industry and business needs.

You’ll walk away with actionable insights and, if you execute them effectively, an infrastructure that will give you a competitive advantage and allow you to scale.

The Takeaway

Trying new things as a business is an ongoing process and it can be very difficult initially but remarkably rewarding down the line. This is a digital world now, and it’s moving towards automation and efficiency. This time is unique in that businesses get to try different things to find what works. The right AI consultant can work wonders for you - but in order to do so, you must be willing to embrace and adapt.

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

How to Use AI-Powered Coding

Programmers are going to have some fun with this one. Picture the start of a typical day as a developer, all the priorities you need to set, preparation for any briefs you’ll get, and then the contingency planning on top of it all. Of course, nowadays with tools like Chat GPT, Visual Studio IntelliCode, and CodeSpace among other resources, the programming process has improved lightyears from what we had 10 years ago. 

With that in mind, let me ask you this (if you are a programmer); how much easier is it to approach a day on the job now compared to when you started programming? Better yet, how much easier would it have been for you to learn to code with the tools available today?

Answers to these questions will vary. A lot of software developers will argue that you need to learn the principles of programming without the “shortcuts” first and then introduce them as you get better. Others will argue that modern programming tools have made it much easier when learning to code and made the process more enjoyable. There really is no right or wrong answer to these questions because it comes down to one thing; these tools are the future. 

The Basics of AI-Powered Coding

AI advancements in the software development space will only get better and make the process more streamlined. Among the many tools programmers are leveraging in 2023, AI-powered code is a top choice to enhance the efficiency and effectiveness of their coding process. 

For the first example, we’re going to be looking at OpenAI’s Codex. Codex is an AI code assembler that uses machine learning to translate natural language into code. Here are 3 examples of using it: 

1. You’re creating a feature that tracks sample inventory in a LIMS system:

You could say to Codex: “Develop a functionality that can record and track the inventory of samples in the laboratory”

Codex will then: 

• Design a plan for a database that will store the following information: sample ID, sample type, quantity, and location.

• Implement CRUD (Create, Read, Update, Delete) operations for managing the sample inventory.

• Develop a user interface that allows users to see and update sample inventory records as they go.

2. You’re creating a feature for insurance software that calculates premiums based on risk factors: 

You could say: “Build a component that calculates insurance premiums for different insurance policies based on factors such as age, location, and coverage type.”

Codex will then:

• Design a data model that can store policy details, consisting of customer information and risk factors.

• Develop algorithms that calculate insurance premiums based on the risk factors provided and the parameters of the given policy.

• Lastly, implement validation checks to ensure the premium calculations are appropriate.

3. Now you want a feature that automatically tracks budgeting and expenses for a FinTech platform:

You could say: “Create an interface that users can use to set budgets, track expenses, and generate reports on overall spending”

Codex will then: 

• Create the design for the interface where users can input budget categories, set spending limits, and track expenses.

• Develop algorithms that will categorize expenses based on predefined rules and transaction data automatically.

• Generate visual reports that users can use to understand their spending habits, see how well they adhered to their budget, and recognize trends in their expenses.

Codex is a great platform, but it’s not the only one. Platforms like Tabnine, Snyk, and IntelliCode all perform unique roles that help software developers perform at the next level— but the key is knowing how and when to utilize them.

What Tools to Use and When

As a software developer, your time down to the second is highly valuable, so anything that can streamline repetitive processes or reduce manual effort is a go. Software depends on context which means that the tools a developer uses will depend on the specific requirements of the project and the developer's preferences.

With that in mind, here are some ways the AI-powered coding tools we’ve mentioned can be used in different situations:

1. Project: You’re building a mobile app for a transportation company

Tabnine will be a great resource in this case to handle the following: 

• The coding process for the mobile app will be shortened thanks to Tabnine’s code completion ability.

• The system can give suggestions for implementing features such as map integration, user location tracking, and in-app notifications. 

• It will get the right APIs involved so that third-party transportation services can be integrated.

  2. Project: You need to design and build a Web App for an E-Commerce store

The Web App development process can become a lot more satisfying using IntelliCode because of the following: 

• Whether it’s HTML, JavaScript, CSS, Angular, React, Vue.js, or you name the web app development language or framework - IntelliCode can suggest relevant code completions based on the context using Deep Learning algorithms.

• Because IntelliCode is so great at detecting what is necessary to complete a code, its error-prevention ability is significant

  3. Project: Develop a Node.js-based REST API

Snyk will be great for the security aspect of your REST API. 

• It identifies any known vulnerabilities in the open-source libraries you're using with your API and then provides insights and actionable recommendations to mitigate these vulnerabilities.

• The good news is that this is not a one-and-done tool. Snyk can continue to monitor the system over time and keep identifying gaps to fill.

Why AI-Powered Code?

The advantages that come with properly utilizing AI coding tools are immeasurable. Software developers are performing at a new level in today’s world thanks to these advancements. This means that now is the time for a business to get on before you start getting dramatically outperformed. 

AI-Powered code relies on 5 key components which include; the data source, Natural Language Processing (NLP), code parsing, Machine Learning, and most importantly feedback and iteration from the software developer. The interaction aspect is what allows the AI models to refine their predictions and generate accurate, or more contextually appropriate coding suggestions. 

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

Introducing Computer Agents

Most people’s knowledge of AI stops at Chat GPT. The “GPT” part of which stands for “Generative Pretrained Transformer”. Didn’t know that? Good, let’s break it down:

Generative: The AI model's ability to generate new content rather than relying solely on pre-existing data

Pretrained: The system has been trained on a large dataset to learn patterns, structures, and relationships in language.

Transformer: The underlying neural network infrastructure that allows the model to process and generate text quickly and efficiently. 

To say the absolute least, OpenAI set a whole new bar and got an absurd head start once they introduced Chat GPT, which has placed OpenAI among the top 20 most visited websites in the world during the 6-month span the platform’s been out. But this success alone won’t sustain them forever, especially since it’s influenced rival companies and researchers who are working tirelessly to catch up by developing their own products.

The Other AI Systems on The Rise

When you give a tool to the public, it’s a certainty that they will do whatever they can to make it as beneficial to them as possible. This mentality has led to the creation of Computer Agents which essentially act as a computer’s personal assistant. In case you’ve never heard of a computer agent, we’ll break the concept down.

The entire premise for computer agents is to do one thing: automate the tasks required to reach goals. This advanced AI infrastructure goes beyond basic copywriting and idea generation. It’s more so tailored to cater to the needs of enterprise businesses.

There are 3 key types of agents: Deliberative, Hybrid, and Reactive. While all are unique in their processes and abilities, they share the common goal which is to complete tasks as quickly and efficiently as possible. But not in the same way that Chat GPT responds to your prompts, computer agents use APIs to work with applications and services to perform tasks on your behalf.

Here is a closer look at each one:

Deliberative Agents: Deliberative agents use advanced planning and decision-making algorithms to perform complex tasks. Ever heard someone say “Use your brain”? Well, these systems do just that. They take their databases of past experiences and use them to analyze problems and make informed choices based on the situation they’re presented with. While they may not have a brain, they generate a plan of action to achieve the intended goal. These helpers are great for tasks that require foresight and optimization.

Reactive Agents: These tools are the complete opposite of what we know about deliberative agents. As the name implies, the system responds to its environment, except without any clear understanding of its purpose. The agent's behaviour and the rules they follow are usually pre-determined and don’t depend on reasoning. It has no learning capacity and instead relies on inputs to trigger pre-programmed responses. For tasks that need quick responses to a predictive environment (like an assembly line), this tool can be a game changer.

Hybrid Agents: With hybrid agents, you get the best of both worlds. These are your self-driving cars, Siri on your iPhone, and even the robots used for manufacturing. These systems combine the strengths of both deliberative and reactive agents to form one process. They can reason and plan like deliberative agents but also react quickly to dynamic environments just like reactive agents. They’re that perfect balance between preplanned actions and on-the-fly adaptation, which makes them great for tasks that require flexibility. 

You might be wondering then, “What’s the point of deliberative and reactive when there’s a hybrid?” Each agent serves a unique purpose, and there are going to be times when a hybrid isn’t as suitable a choice. Here are 4 reasons why: 

1. Complexity: While hybrid agents can handle a wide variety of tasks with the conjoined capabilities of deliberative and reactive agents, the integration does add complexity to the system. In some cases, the simplicity and efficiency of reactive or deliberative agents are simply better for the task which as everyone in IT knows - tasks never need to be more complex. 

2. Application Requirements: Different applications have different requirements. Some tasks prioritize speed and real-time responsiveness, while others require careful planning and decision-making. Having that variety of agent types allows programmers to select the most suitable one based on the application’s specific needs.

3. Specialization: As we said before, reactive agents are best for applications where immediate reactions are crucial, such as emergency systems— especially cybersecurity. Deliberative agents, on the other hand, will cover the big-picture tasks. It’s just like a development team, members have their unique roles and abilities but all contribute to an end goal. 

4. Resource Constraints: Depending on the available computational resources, it may be more practical to use simple reactive or deliberative agents as opposed to a complex hybrid agent. This consideration becomes crucial when dealing with limited processing power or memory constraints in resource-constrained environments.

How it Helps Businesses

Artificial Intelligence, Machine Learning, Autonomous Agents, you name it— it’s cool, but how can people use it to help their business? These systems integrate to fit the custom requirements of your product or service. As the trends suggest, if a company is not trying to automate as much of their workflows as possible, they’re putting themselves at a major competitive disadvantage.

Why would a company not want to move toward automation? Among many reasons, here are the most common:

• A simple lack of awareness/underestimating new technology

• Resistance to change

• Security concerns

• The complexity of the process

• The investment

• Volatile business environment

Most of these concerns are to be expected since the process of digital transformation— especially with AI involved— can be a significant undertaking with no clear guarantees. However, this is what it takes for companies to survive in times of change.

If a business leverages AI, ML, and these autonomous agents with a clear strategy defined and aligns them with their unique goals, they can embrace automation and adapt to the evolving landscape. What we’re getting at here is that leveraging the capabilities of systems like deliberative, hybrid, and reactive agents, such as a GPT, can drive efficiency and innovation, especially in the long term.

Frameworks Behind Computer Agents

Integrating computer agents into a business’s internal system requires a structured approach. Because of this, there are several framework options:

1. Behavior Tree (BT) Framework: The Behavior Tree framework is used for designing reactive agents. It structures an agent's behaviour into a hierarchy of tasks and conditions. The agent then evaluates these tasks and conditions in real-time and makes decisions based on its immediate environment and the rules you’ve given it.

2. Belief-Desire-Intention (BDI) Model: The BDI model is a very popular framework used in designing deliberative agents. It focuses largely on the agent's beliefs about the world, its desires or goals, and its intentions to achieve those goals. This model puts a big emphasis on reasoning, decision-making, and planning capabilities.

3. Sense-Plan-Act (SPA) Architecture: SPA is a popular hybrid architecture used to create highly intelligent agents. Each step is part of a cycle that is sensing the environment, planning its actions, and executing those actions. This is considered the foundation of intelligence that every robot needs.

   The architecture enables agents to combine reactive behaviours with some higher-level deliberative processes. This makes responsiveness happen in real-time through the reactive component, while also providing the ability to reason, plan, and then make strategic decisions using the deliberative component. 

4. Multi-Agent Systems (MAS): Simply put, MAS frameworks involve multiple agents working together to achieve common goals. Using a MAS framework, agents can coordinate their actions and communicate to solve problems that would be impractical for a single agent to handle alone.

The Takeaway

With a solid infrastructure in place, businesses can get the most out of integrating computer agents into their workflows. Whether it's leveraging reactive agents for quick responses, deliberative agents for strategic decision-making, or hybrid agents for the best of both, businesses first need to think about how their goals align with their technology selection. 

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.