8 Ways to Incorporate Emerging Technologies into Your Strategic Planning
This practical guide presents eight essential approaches for integrating new technologies into organizational strategy, featuring expert insights on evaluating AI, drones, and other innovations against business objectives. Industry specialists share frameworks for measuring ROI, including a three-step evaluation process and a structured testing protocol that ensures technology investments deliver tangible value. These methodologies help decision-makers cut through market hype while identifying creative applications that solve core business challenges and enhance human capabilities.
Align Technology Solutions With Business Goals
When we help clients with strategic planning and creating their IT budget for the next year, we often look at where migrations or transitions can be made for greater efficiencies, capabilities, or cost-savings. Instances where we recommend emerging technologies is when it makes technological or business sense to make a shift. For example, we assess apps or infrastructure for cloud-readiness when a server comes to end of life to recommend cloud hosting if it is suitable to their requirements. Another example is recommending modern cybersecurity solutions when a business is seeking cyber insurance or looking to reduce risk to their business. We work with our clients to understand their business goals, then recommend a technology solution that meets them - whether emerging or already established.
Three-Step Framework Tests Technology Against Business Needs
Incorporating emerging tech into strategy has become a core part of how I future-proof my business. A good example was integrating AI-driven analytics into our customer success operations. Instead of jumping in blindly, I used a three-step evaluation framework: relevance, scalability, and measurable value.
First, I assessed relevance—did the technology solve a real operational pain point? Then scalability—could it grow with us without heavy infrastructure costs? Finally, measurable value—would it improve KPIs like retention or efficiency within a quarter? After small-scale testing, the data showed a 20% reduction in churn, which justified a full rollout.
That experience taught me to treat new tech like an investment, not a trend. If it doesn't align with the business model or deliver quantifiable impact, it stays on the sidelines. Separating hype from opportunity takes discipline, but it ensures innovation actually moves the needle rather than just sounding impressive.
AI Adoption Requires Clear ROI and Value
We integrated AI into our strategic planning by first automating our own internal support tickets. Our evaluation framework is simple: does it solve a genuine business problem and offer a clear return on investment? We prioritise practical applications over industry hype. This ensures that any technology we adopt delivers measurable value, enhancing either efficiency or security for our clients.
Data-Driven Evaluation Through Structured Testing Protocol
Our team implemented Azure OpenAI services into a document classification workflow for an enterprise client. The task appeared suitable for LLMs but we performed a side-by-side evaluation against current solutions through a basic scoring system which measured accuracy performance and infrastructure expenses and operational complexity and system maintenance. The system required additional security measures to achieve required latency standards so we restricted GPT-4 usage to assistive functions instead of core operations.
The project success depended on creating a minimal viable product through rapid development. A .NET Core test environment with decision logging functionality enabled us to compare the model's performance against a rule-based classifier through structured evaluation. The real performance data obtained from testing allowed us to verify vendor claims. The system remains unused when new features fail to deliver quantifiable benefits or create additional security threats.
Creative AI Applications Release Human Potential
AI represented the major breakthrough for our company. I focused on determining how generative tools could release human potential instead of pursuing every new trend that emerged. The AI system enabled us to create fabric movement simulations for various body types which reduced our sampling process while maintaining our design instincts.
I evaluate creative potential through the actual outcomes of technology rather than focusing on the technology itself. The value of exploring new tools becomes evident when they create more freedom for my team members and when they help women experience greater body acceptance. Our organization will avoid pursuing any approach that generates unnecessary stress or noise.
Drone Technology Solves Core Structural Problems
Incorporating emerging technologies into my strategic planning isn't about chasing the latest corporate buzzword. It's about finding a hands-on tool that fixes a clear, structural weakness in my trade.
The emerging technology we successfully incorporated was drone-based thermal imaging for structural inspection. The old strategic plan relied on dangerous, slow, physical climbing and guesswork to find hidden moisture—a huge risk and source of inefficiency.
My evaluation framework to separate hype from genuine opportunity was simple and hands-on: The Core Problem Resolution Test. Before committing to any expensive technology, I asked one clarifying, structural question: "Does this solution instantly and demonstrably reduce the labor required for the hands-on inspection without compromising the integrity of the finding?"
The drone technology passed this test immediately. It solved a foundational hands-on problem—finding hidden leaks safely and accurately—that directly cost us time, money, and structural reputation. We didn't use the drone for general photos; we used it for the critical, non-negotiable structural task of moisture mapping in a fraction of the time.
The strategic impact was profound. We shifted capital from high insurance liability to high-precision equipment, transforming our business from a reactive repair service to a proactive structural consultant. The best strategic plan is built by a person who is committed to a simple, hands-on solution that uses technology to secure the structural integrity of the entire operation.
Customer Problem Framework Guides AI Implementation
We're integrating a Google-powered AI stack for automated quality control of video edits, evaluating it through what I call the 'customer problem working backwards' framework. Instead of implementing AI because it's cutting-edge, we started with a specific pain point: our QA team was spending hours checking for misspellings, grammar errors, branding inconsistencies, and narrative issues across thousands of videos monthly.
Our evaluation framework has three gates: First, can we build a terrible version in two weeks? We prototyped Google's Video Intelligence API checking for text accuracy in lower thirds within ten days. Second, will it solve a problem customers complain about weekly? Absolutely—typos in final videos were our top complaint. Third, can it work with 80% accuracy? Google's stack achieved 94% accuracy catching errors humans were missing due to video fatigue.
The integration revealed unexpected value. Beyond catching spelling errors, the AI identifies when editors use outdated brand terminology, flags when narrative flow breaks between segments, and ensures compliance language isn't accidentally removed. It processes every frame consistently whether it's video #1 or #1,000 that day—something human reviewers struggle with.
This framework killed other 'exciting' technologies we evaluated. Automated video generation from text? Customers had plenty of footage; they needed quality control. Real-time collaborative editing? The problem wasn't collaboration but consistency. Google's quality control stack passed because it solved a daily frustration that was measurable, fixable, and scalable.
The framework's power is its cynicism about technology and optimism about problems. We only adopt emerging tech if it eliminates something that genuinely frustrates users daily. Google's AI checking for typos isn't sexy, but it prevents the embarrassment of shipping videos with errors to Fortune 500 clients.
Four Lenses Framework Cuts Through Tech Hype
When we first eyed AI for customer insights, the hype was deafening, but we needed a framework to cut through the noise. We used a simple yet powerful evaluation framework I call the "Four Lenses": Alignment, Feasibility, Impact, and Momentum.
First, Alignment: Does this tech truly fit our business goals, or is it a shiny distraction? We asked ourselves if AI would improve how we understand and serve customers or just add complexity.
Second, Feasibility: Can we realistically implement this with our current resources and skills? We didn't want to chase moonshots without the infrastructure or talent to land safely.
Third, Impact: What tangible benefits can we expect? We looked for clear metrics—better retention, faster decision times, or lower costs—that would justify the investment.
And lastly, Momentum: Is the technology gaining reliable adoption in our industry, or is it just a flash in the pan? We tracked market signals and case studies to gauge staying power.
By applying these lenses, we saw AI not as a magic wand but as a tool that, when thoughtfully integrated, could elevate our customer strategy. This kept us from falling for every flashy promise and focused our energy on real value.
So, my advice is don't get swept up in buzzwords. Develop your own focused filter, whether it's "Four Lenses" or something equally practical and use it to keep your strategic planning grounded, yet open to innovation.
