Navigating the next wave of AI so you stay ahead, not get left behind

From the early days of rule-based automation to today’s generative models and agentic systems, Artificial Intelligence (AI) has moved from “interesting experiment” to business imperative. As the team at Proso AI noted back in 2021, AI was already “on the verge” of revolutionising many sectors. Fast forward to 2025—and that future is here. Yet for many organisations, the question is not if they will use AI, but how well.

This blog explores the major enterprise-AI trends that matter now, the key risks and barriers, and what you as a leader should prioritise to harness value rather than hype.

1. Why Now? The AI Inflection Point

AI has reached a point where it’s no longer purely research-lab work; companies are embedding it into operations, products and business models. For example:

• A recent survey found that nearly all companies say they are increasing their AI investments over the next few years.
• Major industry research demonstrates that while many firms have dabbled in AI, only a small minority consider themselves “mature” in deployment.
• The market and infrastructure supporting AI are accelerating: for instance rising demand for AI-ready data infrastructure and edge AI deployments.

For enterprises—especially those with large data volumes, complex workflows and evolving digital-business models—this means a strategic choice: treat AI as a novelty, or treat it as a core business capability. The latter will separate the winners from the also-rans.

2. Key AI Trends Shaping Businesses in 2025

Below are six standout trends with strategic importance for enterprise stakeholders.

2.1 Agentic AI and Autonomous Systems

With “agentic AI” (systems that act autonomously, adaptively and take decisions) gaining traction, organisations are moving beyond simply issuing “recommendations” to enabling machines to manage workflows.
These systems are becoming relevant in customer service, operations, logistics, and even decision-support in regulated industries.

2.2 Multimodal, Domain-Specific & Embedded AI

The era of simple text-based chatbots is giving way to multimodal systems (processing text, voice, image, video) and domain-specific AI models tuned to particular industries.
For enterprise leaders, this means AI expects to be more than a generic “tool”—it needs to integrate with your domain, data, and workflows.

2.3 Democratisation of AI & Low-Code/No-Code Platforms

AI is becoming accessible to non-data-scientists via low-code / no-code platforms, enabling business users to build, test and deploy AI solutions more rapidly.
For your organisation, this means broader responsibility across teams—not just a central AI team.

2.4 Governance, Ethics & Responsible AI Are Non-Negotiable

As AI enters more critical business functions, issues around transparency, bias, model risk, data privacy and regulatory compliance are front and centre.
If you don’t address governance and ethics proactively, your AI ambitions may be hindered or derailed.

2.5 Operational Value & Scale over Proof-of-Concepts

The hype around AI has given way to pragmatic questions: “Can we scale this?” “What measurable business value will this deliver?” Research shows that only a small proportion of companies have moved from POC to enterprise-wide value creation.
This emphasises the need for clear business cases, measurable KPIs and alignment with strategy.

2.6 The Infrastructure & Data Foundation

Building for AI isn’t just about models—it’s about data pipelines, compute infrastructure (including edge), governance, integration with legacy systems, and cross-platform interoperability.
For enterprises with complex systems (e.g., combining CRM, HCM, ERP, supply chain) this is a major consideration.

3. Why You—As a Decision Maker—Should Care

If you are in a leadership role (IT/CTO, business unit head, transformation office), here are the direct implications:

• Competitive gap widens: Those who embed AI into core business processes will gain cost advantage, agility, predictive insight and new business models.
• Risk of stagnation: Organisations that treat AI as “nice to have” risk being overtaken by more agile rivals.
• Talent & culture matter: The shift demands new skills, change in operating model, and alignment across business, data and tech teams.
• Integration with major enterprise systems: If your organisation uses platforms such as Salesforce, Workday, Oracle Cloud, then you must consider how AI hooks into those broader ecosystems.
• Governance & compliance are front stage: Regulators, customers and markets will take governance — and the ethics of AI — as seriously as you take your brand risk.
• Don’t just automate—reimagine: The real benefit comes when AI enables new ways of working, new services and new business models — not just incremental updates.

4. Practical Steps to Move Forward

4.1 Define Clear Use-Cases Aligned to Strategy

Start by identifying critical pain-points or opportunities where AI can deliver value (e.g., in customer service, supply-chain optimisation, predictive maintenance, talent analytics). Ensure the use-case aligns with your organisation’s strategic objectives.

4.2 Build the Data & Infrastructure Foundation

Ensure you have access to clean, trustworthy data, scalable infrastructure (cloud/edge), integration capabilities with existing systems, and governance mechanisms to manage risk.

4.3 Assemble the Operating Model: People, Process, Technology

Treat AI implementation as transformation, not just a technology rollout. This means:

• Upskilling and reskilling (given the talent gap)
• Establishing cross-functional teams (business + data science + IT)
• Defining clear metrics for success and accountability
• Embedding governance and monitoring from day one.

4.4 Start Small, Scale Fast

Pilot projects are fine—but design them with scale in mind. That means: repeatable architecture, clear business metrics, and integration into core systems (CRM, ERP, etc.). Avoid the “sandbox trap” of many AI initiatives.

4.5 Monitor, Govern, Iterate

Measure the outcomes of your AI initiatives (ROI, productivity uplift, user adoption). Implement governance frameworks for model lifecycle (bias, drift, transparency) and evolve accordingly.

5. Pitfalls to Avoid

• Treating AI as a “project” rather than a business capability
• Skipping governance, ethics or responsible AI frameworks
• Ignoring integration and data underpinnings (models alone don’t deliver value)
• Failing to align initiative with measurable business outcomes
• Underestimating change management and the need for cross-functional collaboration

6. Looking Ahead: What’s Next in the AI Journey

In the medium term (2026-2028) you should anticipate:

• Widespread deployment of multi-agent systems, where AI agents coordinate among themselves and with humans.
• Deeper verticalisation of AI: domain-specific models (finance, healthcare, HCM) that outperform generic models.
• Enhanced focus on AI energy and resource consumption (edge deployments, sustainability) as infrastructure cost and environmental impact become critical.
• The blurring of AI and standard business operations: the question will not be “Do we use AI?” but “How are we optimising with AI?”
• Intensified regulatory frameworks globally — you’ll increasingly need to comply with AI-specific rules, auditability of models, data sovereignty, explainability.

Conclusion

AI is no longer an optional technology trend—it's a fundamental driver of enterprise competitiveness. For leaders working within large, complex organisations (especially those using enterprise platforms like Salesforce, Oracle or Workday), the imperative is clear: move beyond experiments, embed AI as a capability, invest in infrastructure, govern responsibly, and align with business strategy.

As Proso AI’s 2021 article put it: “AI is transforming every walk of life… the world is on the cusp of revolutionizing many sectors through artificial intelligence and data analytics.” Today, that cusp is behind us. The question now is: will your organisation lead the transformation, or be transformed by it?

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