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AI, its effects and the integration of AI tooling has been in full swing since the launch of ChatGPT in 2022. The current trends show the adoption and use of AI will continue its exponential growth, with experts speculating the AI market will be worth over 2 trillion dollars by 2032.
It shouldn’t be a surprise to anyone who has used AI tools before, as AI tools have the capability of automating key business processes, saving owners and employees hundreds of hours and thousands of dollars, which can be redirected into other aspects of the business.
This blog will look at what’s happening in industry and provide the reader with ideas and information on how they could best harness AI in their business or as someone working with AI tools.
Agentic AI can be understood as giving AI systems a defined goal, and allowing them to process the request, analyse data and execute actions on your behalf. This harnesses the strength LLMs have when it comes to parsing or reading large volumes of data, extracting the meaning or key aspects and providing a transformation based on your intended goal.
Some examples of Agentic AI being used are in the Customer service management software space. Companies have setup integrations that would have an AI agent “analyse their customer support tickets and identify trends or recurring issues that have been consistently popping up”.
An Agent workflow could take the following steps to achieve this goal:
Companies deploying these types of agents are seeing results such as:
Agentic AI enables automation and transformation of your business data and routine tasks. Whether its reviewing expense reports, project tracking or managing and enhancing sales pipelines, Agentic AI has the capability to assist in many areas.
The requirements for getting started with Agentic AI are that the AI tool must be able to access the business systems or data, whether it is your accounting software, CRM or customer service tool. This adds a layer of complexity above regular AI use however the ROI is significant if implemented correctly.
Whilst Agentic AI does use aspects of business data to execute goal-based actions, the full power of AI in the data context comes from completely integrating LLMs within your business context (CRM, business software, calendars, emails and documents), creating an LLM that possess the knowledge base of all your business functionality, history, customers, direction and processes.
This enables you to harness large language models (LLMs) as a tool that can be used as a virtual assistant at every level of an organization, providing employees with easy access to company knowledge regarding internal procedures, business analytics, compliance risks and operational tasks.
Some Industries already using Enterprise AI include:
Financial Services – utilized in various ways such as in analytical forecasting and fraud detection by detecting anomalies across thousands of accounts and transactions.
Marketing – Having an LLM trained on your customers behaviour and spending habits, allows for AI tools to create effective personalized marketing campaigns leading to an increase in sales and customer engagement.
Cyber Security – training LLMs on your business security posture and providing them with key data points regarding network and device traffic, allows for AI systems to assist in detecting potential security breaches or points of vulnerability within your organization.
Real examples of this AI trend are financial services which have implemented an integrated AI system with their entire digital infrastructure (accounting, CRM, trading system, news feed, client data). This allows them to use AI to:
Enterprise AI LLM integration allows for the creation of a tool that understands your business context, goals, and data points. A reliable software development company can help implement these intelligent solutions to align perfectly with your business needs. This creates opportunities to enhance all areas of your business—from customer service and cybersecurity to marketing, analytics, and the development of effective knowledge tools your team can benefit from.
Like Agentic AI, the key consideration in implementing Enterprise AI is to ensure proper connection between data points in your organization and the AI tooling you will use, whilst also maintaining strong security and privacy.
Bonus Visit: How to Build an AI Agent
A step up in AI tooling from general AI models, is harnessing the power of Specialized models. This is a growing AI trend where companies are developing or adopting specialized models which allow them to excel in their industry or domain.
As opposed to general-purpose models which possess data about a lot of ideas and concepts, specialized models possess significant amounts of data about concepts, ideas and frameworks based around a specific industry or domain.
For example, in the legal field, there are AI LLMs which have been given large amounts of data around case law, past cases, outcomes, regulatory requirements and other industry specific information making them excel in assisting legal professionals with providing their service.
Organizations deploying these specialized models are reporting:
Medical clinics using specialized AI trained on medical literature, clinic data and patient records are seeing a significant improvement in medical diagnoses assistance when it comes to diagnoses and patient care compared to general AI models.
If you work in a specific industry, using a generic AI model is still appropriate for generic everyday tasks. However, for optimizing your business performance using a specialized AI model for assisting in delivering your industry specific outcomes, will deliver much better results. Due to the development in this space, these models are accessible for most businesses which are willing to expend capital to invest in this tool.
According to IBM’s definition, multimodal AI refers to machine learning models capable of processing and integrating information from multiple modalities or types of data. These modalities can include text, images, audio and other forms of sensory input.
With AI tools such as ChatGPT being able to interpret data from inputs other than text, an AI trend which has been taking hold, is applying multimodal to improve various business processes.
An example would be the monitoring of a manufacturing line using AI. The AI system would:
As mentioned above, multimodal AI has enabled the automation of many beneficial processes within the manufacturing industry. From AI systems which can visually detect product issues, to audio-based AI systems which can monitor for production machine issues, these all create an enhancement for the current state of manufacturing and are being implemented as we speak.
An interesting example that popped up into the media around multimodal AI, was its use in agriculture, specifically for cow milk production. This is using AI which does the milking as well as real-time monitoring of each cow being milked, ensuring that additional actions can be taken based on each cow’s real-time data to enhance milk production. This use case of multimodal AI provided an increase in both productivity and cost reduction due to labor savings.
Read more about this use case: https://www.theguardian.com/australia-news/ng-interactive/2026/mar/01/the-cows-beat-the-shit-out-of-the-robots-the-first-day-the-tech-revolution-designed-to-improve-dairy-farming
Retail theft costs the industry billions of dollars each year. Multimodal AI is being used to assist in providing real-time video monitoring assessing for suspicious behaviour, audio cues for abnormal activity and facial recognition against known suspects to enhance retail store security. This ultimately provides an enhanced security posture for many retail stores as it is more effective than relying on a human watching the same feed.
As we can see the trends going towards AI being used in businesses, having access to our personal and proprietary data, it is only logical that compliance is becoming a large trend within this space. To protect both the business as well as customers information from being misused. Furthermore, with Agentic AI being able to now execute actions on our behalf, who becomes responsible for a mistake that’s made by an AI agent?
Key regulations affecting business:
Organizations now need to consider the following:
When deploying any AI into your business, think about what potential impact it could have based on your industry specific compliance requirements, as well as what data you are giving these models access to and whether there are guardrails in place for any AI tools that execute actions autonomously.
Many large organizations are already building specific AI governance teams to audit and maintain procedures and policies regarding AI implementation. For smaller to medium sized organizations, it will still be important to do necessary due diligence before deploying AI systems.
AI in 2026 is now becoming business as usual for many organizations, with its impact measurable and processes becoming increasingly standardized and repeatable. As every forward-thinking AI development company continues to innovate, new capabilities are being developed while the foundational principles of security, compliance, and regulation remain unchanged.
The competitive advantage in AI is having the right AI tool to solve your business problem. We are seeing the businesses doing well in 2026 being the ones who can correctly identify processes in their business or aspects of the businesses that can leverage AI’s strong analytical and data processing skill sets to enhance their workflows.
If you’re ready to see how AI can transform your business, contact us now for a free consultation and quote.
Let’s discuss where AI creates value for you →
Omar is the CTO of Solacecode. He is an IT professional, with extensive experience working in multi-national organisations across various industries. He has worked in different IT disciplines ranging from managed services, software development, cyber security and artificial intelligence.
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