Most people simply don't make the best use of tech in their businesses.

But if you sidestep these six common stumbling blocks, I guarantee you'll see the shift...

6 Tech Stumbling Blocks Your Business Should Avoid

Mistake #1 - Resisting the Inevitable.

When I started at my construction company, I saw firsthand the industry-wide aversion to technology. But here's the twist - tackling fears head-on revolutionized our operations. Trust in the potential of change.

Mistake #2 - Neglecting Frontline Needs

One thing I've learned in my journey from being in the Marines to the tech industry is to be at the frontline. Directly engaging with the operational front is not just advisable; it's where the majority of your energy and focus should be directed.

Mistake #3 - Missing the Flexibility

When the market changes, the rigid are left behind. Adapt, improvise, and overcome - bathe in agility, your operations will thank you.

Mistake #4 - Neglecting the Basics

In this digital age, the significance of documentation and streamlined workflows can't be overstated. Despite their seemingly traditional nature, they serve as the backbone of efficient operations.

Mistake #5 - Underestimating Human-Tech Synergy

Contrary to popular belief, technology and the human workforce aren't mutually exclusive. The right tech integration can significantly enhance the work environment, fostering conditions ideal for employee retention. Embrace the synergy, and watch your team thrive in a tech-empowered workspace.

Mistake #6 - Settling for Off-the-Shelf Solutions.

It's like using a hammer for all problems because that's all you have. Customized tech solutions ensure you approach each problem with the right tech tool.

In today’s data-driven world, enterprises are drowning in information but starving for insight.

Raw data, in its unstructured form, is little more than digital noise.

To solve that problem, an enterprise needs a conductor – a framework that can harmonise disparate data points into a coherent narrative.

And that is where the knowledge graph comes in.

A knowledge graph, however, is like a well-trodden trail through the forest. It connects the trees, revealing hidden pathways and illuminating the overall landscape.

The power of knowledge graphs lies in their ability to uncover hidden patterns and correlations.

By mapping out intricate connections between data points, we can identify trends, predict outcomes, and optimise processes with unprecedented accuracy.

For instance, the financial world is a complex ecosystem of transactions, risks, and regulations.

Knowledge graphs are being employed to untangle this web. By connecting entities such as customers, accounts, transactions, and market data, banks can construct a comprehensive view of their operations.

This enables them to detect fraudulent activities, assess creditworthiness, optimise investment portfolios, and personalise financial products.

A healthcare organisation can also leverage it to analyse patient data, identify disease outbreaks, and develop targeted treatments.

But building a robust knowledge graph is no small feat.

It requires a deep understanding of data engineering, machine learning, and domain expertise. It’s an investment in time and resources, but the payoff is immense.

Knowledge graphs are key to unlocking the hidden value within your organisation’s data, and a bridge between raw data and actionable insights.

And by embracing the technology, you can transform your business and navigate the complexities of the digital age with confidence.

Is your enterprise leveraging knowledge graph to get insights from big data?

As AI continues to mature, we find ourselves at a critical juncture where it could become a commodity.

Historically, mature technologies reach a point where their underlying principles are widely understood, and building them becomes commonplace.

AI, it seems, is following that trajectory, and that shift poses challenges for AI startups.

While behemoths like Microsoft and Google can attract users and invest substantial resources in AI, smaller startups face an uphill struggle.

Their comparatively modest budgets make it difficult to compete with the giants.

And the real crux lies in operational costs.

Training AI models is expensive, but running them can be ruinously so. Consider generative AI services — each query requires fresh thinking, consuming substantial resources.

Even for advertisement-supported companies like Google, offering AI-generated summaries across billions of search results threatens profit margins.

The adoption landscape also reveals nuances. While surveys show widespread AI usage among white-collar workers, paying customers remain a subset.

Microsoft's AI Copilot, priced at £30 per month, exemplifies this gap. OpenAI, despite undisclosed annual revenue (estimated at £2 billion), falls short of justifying its lofty £90 billion valuation.

The long and short of it?

AI's commoditisation is inevitable, but its impact on startups and industry giants alike warrants close scrutiny.

As the AI landscape evolves, balancing innovation, cost, and adoption will determine who thrives in this new era.

What do you think will be the most significant challenge for AI startups in the next five years?

There's growing talk that the pace of advancement in Artificial Intelligence (AI) is slowing down.

While recent breakthroughs have been undeniable, some argue we're hitting a wall.

Here's a quick look at both sides of the argument:

Against the slowdown:

The field is still young, with vast areas unexplored. The recent boom in areas like generative AI and natural language processing suggests a wellspring of innovation remains.

Plus, the increasing focus on explainable AI and ethical considerations could lead to a new wave of responsible advancements.

For the slowdown:

Perhaps the low-hanging fruit has already been picked.

Maybe the fundamental challenges – like true general intelligence and real-world adaptability – are proving tougher nuts to crack.

Additionally, the vast computational resources needed for some projects could be a bottleneck.

What do you think? Is the golden age of AI behind us, or is this just a temporary lull before the next big leap?

Share your thoughts in the comments below.👇