AI for Business Strategy Guide

Investing in AI for business strategy purposes offers long-term benefits, but short-term uncertainty is getting in the way. Grappling with economic pressures and geopolitical tensions, in addition to technology advancements, means 43% of CEO time is dedicated to a planning horizon of less than a year. Yet business success also depends on thinking up to two years ahead, and acting accordingly.

Organizations have always been in a race against time, but earlier waves of innovation have been arguably less disruptive. Shifting from on-premises IT to the cloud was a change that was rolled out gradually in many cases. Adopting mobile technologies and embracing social media were signficiant shifts as well, but AI is fundamentally changing how people create content, write code and automate everyday business tasks overnight.

This creates a particular tension where senior leadership teams are trying to balance speedy innovation and delivering differentiated digital experiences with strong security and governance. No wonder 26% of CEOs cite greater agility and faster decision-making as the top leadership capabilities, while 80% say they feel more pressure to ensure the long-term prosperity of the business.

The result can be digital decision paralysis, where decisions to enhance critical assets like websites or invest in a modern CMS are delayed or indefinitely stalled. In fact, 57% of executives feel they’re missing opportunities because they’re not making decisions fast enough. The risk is that competitors will prove quicker and more nimble, using their momentum to steal market share.

WordPress VIP strategic account manager Brant Williams said that to some extent, it’s only natural that business leaders take a beat before committing themselves to a particular AI roadmap.

“I’m seeing this as a more meaningful architectural moment than just, ‘Oh, we’re building a new set of web platforms.’ It’s clear the future is going to be a different path for them than it has been for the last couple decades.”

— Brant Williams, Strategic Account Manager, WordPress VIP

How can you get senior leaders in a large organization to move forward with strategic investments even when the full implications of AI for business strategy within the next 18-24 months are impossible to know for certain? Read on to understand why decision paralysis arises and what to do about it.

Core causes of AI decision paralysis

Making a decision becomes paralyzing when it feels more likely you’re making a mistake. No one wants to allocate budget towards technology that won’t work as expected or (worse) lead to negative outcomes.

Other root causes include:

Though it might seem like prudent caution, AI decision paralysis can hurt a company that takes a “wait and see” approach. Not only might rival organizations get ahead of you, but customers may come to expect AI-powered capabilities in the experiences you offer.

How an open and intelligent approach can overcome decision paralysis

Rather than treat every digital decision as an existential choice, set yourself up to pursue structured experimentation to learn and fine-tune your AI for business strategy.

Opting for solutions based on open source, for instance, helps address that fear of vendor lock-in because they offer greater interoperability and data portability. They also provide greater customization to address cybersecurity risks while meeting the needs of business functions like marketing.

Combine that openness with intelligence in the form of data and analytics that can help define the best strategy based on customer behavior, employee needs, and operational considerations. Instead of merely dropping AI into your organization’s workflows, you’re positioning your organization to gather insights and deploy solutions with the flexibility to keep pace with the future.

An open and intelligent approach ultimately helps reduce the risk of regretting your decisions, which makes it easier to move forward.

Balancing speed, security, and unique experiences

Adopting AI, or any other kind of technology, comes down to decisions about where to use it, where not to use it, and when to revisit those choices as tools and business processes mature. What helps is a framework that balances the three key priorities mentioned earlier:

• Speed: Overcome the fear of errors or bad outcomes by investing in platforms that allow for rapid development cycles and the rollout of new interfaces. These are all staples of a truly enterprise-grade CMS, helping content teams establish low-friction creation, review, and publishing workflows.
• Security: There’s no need to be paralyzed by the prospect of upgrading or migrating to a new platform if it’s built on zero-trust principles, with strong controls over content approvals and features that ensure regulatory compliance by design.
• Unique experiences: What your customers want and need two years from now may be unknown, but you can be sure you’ll need tools like a CMS that can offer a hybrid headless approach to further omnichannel efforts. A platform with plenty of integrations, plug-ins, and expert partners will also make it easier to contend with unexpected future requirements.

Brant suggests looking for common pain points: if your engineers are overloaded with work and your marketing team lacks the autonomy to get ideas and campaigns out the door,

“Not to make it too simple, but using technology to automate security, scale, and resilience is just non-negotiable. Identify the areas that meaningfully slow you down every time and see if they’re available to be automated.”

Responding pragmatically to AI innovation

Pushing back AI decision paralysis doesn’t mean you’re throwing due diligence out the door. Monitoring AI tools for bias or hallucinations, developing responsible use policies, and acting with transparency is still important. These should be natural steps in any IT investment evaluation and deployment, not hurdles that the business becomes too wary to jump.

Brant noted that technology standards are also helping ease some of the potential challenges in making an AI-powered CMS work cohesively with a CRM, CDP, and other parts of the tech stack. Model Context Protocol (MCP), for instance, avoids having to redo the same integration work every time a new AI tool emerges.

As you move forward, help senior leaders see technologies like AI as a replaceable service layer, rather than something hard-wired into the core platform. This can make some digital decisions feel less ominous or daunting.

An action plan to defeat AI decision paralysis

Strategic business decisions are rarely made by a single person. Creating an “AI decisions council,” either formally or ad hoc, based on emerging issues, could be another way to avoid getting stuck amid uncertainty.

Use this diagram to lead the discussion, tweaking and customizing it based on the specific nuances of your organization and the form of AI or other technology you’re wrestling with:

The rapid advancement of AI for business strategy proves that any certainty about what the future will look like over the next 18-24 months is unlikely. The only recourse is designing your tech stack with change in mind. Investing in an open and intelligent CMS and web architecture as a starting point? That’s an easy decision to make.