Fewer Tools, More Strategy: Why We Don’t Chase Every New AI Tool

The Temptation of “The Next Big Thing”

If you are in need of entertainment on a random Saturday night, then take a look at the futuretools.io website. It’s one of many Artificial Intelligence (AI) tool aggregator websites that have popped up over the last couple of years. This site says it “Collects & Organizes All The Best AI Tools So YOU Too Can Become Superhuman!” Who doesn’t want to be Superhuman? But this single site alone lists over 3,500 AI tools! So, in the course of becoming superhuman, we could very well become super-confused. Every day, new AI tools become available that promise to help us in every aspect of work and life. But how do we choose? Great question!

At Digital Relativity, we believe creativity and innovation don’t come from adopting every new AI tool. They come from intentionally choosing a few good tools and learning to use them well. Using these tools allows us to spend less time on repetitive, mechanical work and more time focusing on strategy, creative thinking and meaningful collaboration. This mindset shapes how we select, evaluate, and apply AI technology across our agency.

The Problem with Too Many Tools

The exponential growth of AI tools has created an environment where it’s easy to confuse novelty with progress. Individuals and teams can experience “Shiny Object Syndrome.” I’m guilty of this too — I love a shiny new piece of tech! If left unchecked, we can begin chasing every new AI release that makes big promises, spending more time exploring and learning new interfaces than we save by actually using those same tools. The result is often reduced efficiency, duplicated functionality, cluttered communication across platforms, and inconsistent results.

There are also more serious risks. Unvetted AI platforms can cause multiple issues: they can mishandle confidential data, introduce bias or misinformation, misuse content for model training, depend on unstable or unreliable vendors, or fail to comply with data protection laws. For example, an AI note‑taking or transcription app that automatically stores meeting audio or transcripts on external servers could unintentionally capture personally identifiable information (PII) or HIPAA‑protected data. This could potentially expose a partner to compliance violations under data‑protection laws such as GDPR (the European Union’s General Data Protection Regulation, which sets strict rules for how personal data can be collected, stored, and processed) or federal U.S. privacy laws such as HIPAA (Health Insurance Portability and Accountability Act), as well as other applicable state privacy acts.

Many of these concerns are made worse by the fact that countless AI tools are actually built on the same large language models (LLMs) and simply repackaged with additional functionality, features, and interfaces. At first glance, this might seem like a good thing — and sometimes it is beneficial. However, even trustworthy, secure, and accurate LLMs can be repurposed in ways that are none of those things. Without a clear process to review and evaluate new AI tools, organizations risk wasting time, duplicating effort or even creating vulnerabilities instead of using AI to solve real problems and think more strategically.

That’s why we’ve adopted a structured approach to AI evaluation and implementation that helps us invest time and trust only in the tools that deliver measurable value.

The DR Evaluation Framework: How We Choose What’s Worth Our Time

Every AI tool we consider is tested against ten core priorities that balance innovation with responsibility. These criteria guide whether a tool becomes part of our approved ecosystem.

1. Use Case Alignment  –  Does it solve a real problem or fulfill a defined need identified by our departments or partners? Does it duplicate solutions found in tools already on our approved list?
2. Data Security & Privacy Compliance  –  Does it protect confidential or proprietary data and follow all applicable privacy regulations?
3. Ethical & Responsible AI Use  –  Does it align with our policy on avoiding bias and protecting intellectual property?
4. Quality of Output (Accuracy & Reliability)  –  Are the outputs dependable, verifiable, and free from major errors or hallucinations?
5. Automation & Time-Saving Potential  –  How much manual effort does it reduce, and does that time savings enhance strategic work?
6. Learning Curve & Usability  –  Can teams and partners learn it quickly and use it efficiently?
7. Cost & Value for Investment  –  Is the licensing or subscription cost justified by its long-term ROI?
8. Ease of Integration  –  Does it work smoothly with our existing workflows and platforms?9. Tool Availability & Vendor Stability  –  Is the vendor credible, well-supported, and likely to improve the product over time?
10. Customer Support & Documentation  –  Will our team have access to the help, documentation, or training needed to make it successful

Each question helps us see through the “AI hype” and evaluate whether a tool meaningfully supports our mission. If it doesn’t meet our standards across these priorities, it doesn’t make it onto our approved list.

Fewer Tools, More Strategy

By focusing on a small, vetted group of tools, we’re creating a culture of dependability, consistency and innovation. As much as AI companies want you to believe their products will solve all your problems, that just isn’t reality — all AI tools are not created equal. Because we limit ourselves to a smaller set of approved tools, our teams really get to know each one — its quirks, its strengths, and its unavoidable weaknesses. That depth helps us match problems with the best AI solutions and apply them strategically across a wide range of use cases. We become more fluent in the tools themselves, which naturally translates into higher-quality results. Since we’re all using the same core set of tools, it’s easy to share ideas, techniques and insights directly through those platforms. This shared fluency drives innovation, creativity and strategic thinking — the things that truly define our mission as a company.

By mastering these select tools, our team delivers consistent, high-quality work without the distraction of constantly chasing the next new thing. Those limits aren’t restrictions — they’re catalysts for creativity. When we understand our tools deeply, we can push them in unexpected ways, finding fresh ideas within the boundaries we’ve chosen.

The Discipline Behind Innovation

At Digital Relativity, we’ve learned that slowing down to think critically is our real advantage. Our intentional approach lets us experiment with purpose and evolve without losing sight of what matters most: relationships, strategy and creative innovation.

I heard a quote recently at MAICON 2025 (Marketing Artificial Intelligence Conference): “We become more human as we begin acting less like machines.” Choosing fewer tools and more strategy isn’t about doing less — it’s about using AI to help us make our work unmistakably human.

Overwhelmed by all the latest AI tools and advancements? Contact us to schedule an AI consultation.