Reflecting on 2024: A Year of Growing AI Momentum

Dear EncompaaS Community,

As we near the end of 2024, I’m incredibly proud of what our team has achieved in the last year. We’ve innovated new approaches to information management, solving challenges that exceeded conventional, human-scale capabilities for our customers. We’ve also welcomed new customers through our global partner ecosystem and gained some incredible industry award recognition for our AI-powered intelligent information management platform. Thanks to everyone who has contributed to making this possible.

As is becoming tradition, I wanted to share my key reflections on the year that’s been, and what I consider to be the top priorities for technology leaders in the rapidly evolving AI era ahead.

1: The AI Landscape Is Maturing

Within the last year, the AI landscape has developed and evolved at breakneck pace.

We have seen AI move from theoretical to practical applications, and organisations have considered how AI can change their business.

In line with this, there was an evident change in attitude maturation about AI – people have realised why AI projects worked or didn’t, and it’s safe to say that no technology has ever had this many beta testers.

As businesses have seen the trials and errors, along with the successes, of AI, there has been a change in perception from one of trepidation to one of enthusiasm.

2: Lessons Are Being Learned Quickly

There’s been lots of AI experimentation on both the vendor and customer side. Organisations are wading through the hyperbole and recognising AI’s transformational, disruptive power, while also realising like all transformations – it’s not easy.

Common obstacles I’ve seen organisations experience often relate to data quality which leads to the inability to find the correct data to support high-quality GenAI outputs.

Gartner currently predicts that 30% of GenAI projects will be abandoned by 2025, largely due to poor data quality. As their Research Vice President Jim Hare aptly states: “Data is the fuel for AI, so organisations need to prepare now to store and manage even larger amounts of data for AI initiatives”.

In understanding and overcoming these challenges, the value EncompaaS provides has come into sharp focus because it ensures your data is meticulously prepared and allows you to curate it for upstream GenAI and Copilot solutions. This dramatically improves the success of these initiatives. In short, investing in data preparation is crucial to generating reliable, high-quality outcomes.

3: Use AI to Prepare your Data for AI

Businesses are maturing in understanding the biggest challenge of GenAI – data quality – and recognising that when they address this, the results can be incredible.

This leads me to a key point I stress in all my conversations with customers and industry leaders – the best ‘first use case’ for AI is to use it to protect and organise your data so this strategic asset is ready to power your AI revolution.

AI can now be used to automate the discovery and understanding of structured and unstructured data at scale, organise and de-risk it efficiently, so you can use it for a variety of upstream purposes, such as accurate and responsible GenAI adoption.

We are seeing incredible results using OpenAI to extract critical metadata from unstructured documents, enabling us to auto classify them with incredible accuracy. Suddenly the petabytes of unstructured data we’ve avoided for years can now be leveraged in real time.

With virtually every AI research report indicating that data quality is the top factor influencing success or failure, the reliance on the information management sector to prepare data ahead of AI adoption – and do so quickly – will become even more important. Frankly, using AI-powered information management solutions is the best way to do this.

4: Specificity is Key

Once you’ve cleaned up your data and created a high-quality foundation, it’s primed and ready to plug into any number of AI services. But in my experience, the key to success is identifying specific business challenges that AI can solve, prioritising which use cases will drive the greatest value, and then identifying the right AI service to achieve this.

Don’t settle for a catchall approach to adopting AI – there are so many AI services available now that solve very specific challenges, it’s not a case of one-size fits all. The future lies in tailoring AI services to solve your business-specific challenges. With well-prepared data, you will now be able to curate specific data to specific AI services and deliver outstanding results.

5. 2025 – The Rise of AI Agents

With the proliferation of enterprise AI adoption, organisations will no doubt be looking for ways to streamline pilot to production in the race to prove ROI, unlock further budget, and boost competitive advantage.

This is where we will see a rise in the number of AI agents deployed, to autonomously undertake high-volume manual tasks and make decisions based on trained models, without the need for human intervention.

This will be particularly valuable for highly regulated enterprises, looking for smarter and faster ways to streamline information management tasks. However, like all AI technologies, its success will depend on the quality of your data. Accurate, relevant and well curated data is key, along with robust governance policies, so that the work performed by these agents is trustworthy and reliable.

Looking Ahead

I’m looking forward to an exciting 2025 which will no doubt be filled more positive disruption and innovation as we remain steadfast in our mission to help enterprises transform huge stores of data into strategic assets, paving the way for Generative AI success.

I want to take this opportunity to thank all of our customers, partners and team members for another stellar year. And of course, if your organisation is ready to take advantage of AI in a safe, fast and reliable manner – please get in touch with me and our expert team. We’d love to hear from you!

Happy Holidays and Best Wishes for 2025.

Jesse