Future-Proofing Your Skills: What AI and Quantum Mean for Your Organization

Here's a sobering statistic: 95% of generative AI pilots are failing to deliver their intended benefits. That's according to MIT research from 2025 and it should give every leader pause. Not because AI isn't transformative (it absolutely is) but because technology alone has never been the answer. The organizations succeeding with AI, and those preparing intelligently for quantum computing, share something in common: they've invested as heavily in their people and their principles as they have in their platforms.

The New Technologies Shaping Transformation

Generative AI

Let's start with generative AI, because it's no longer a future consideration. It's here and it's already separating the prepared from the panicked.

Generative AI is built on large language models that have been trained on vast datasets to recognize patterns and generate human-like responses. The frontier models (ChatGPT, Claude, Gemini, Llama, DeepSeek) are impressive. But impressive technology doesn't automatically translate into business value. The organizations getting real results are those treating AI implementation with the same rigor they'd apply to any major change program.

Octopus Energy provides a useful example. They've automated a third of their customer service emails using AI, achieving satisfaction ratings above 80%. That didn't happen by accident. It happened because they defined a high-value use case, piloted it with clear metrics and built the internal capability to manage and improve the system over time.

In healthcare, AI is now detecting 3% more cancers in mammogram screenings than human radiologists alone. In software development, GitHub Copilothas increased coding productivity by nearly 56%. These aren't hypothetical benefits. They're measurable outcomes from organizations that have done the hard work of implementation properly.

Quantum Computing

Quantum computing sits at an earlier stage of maturity, but the trajectory is clear. We're currently in what's called the NISQ era (Noisy Intermediate-Scale Quantum) with machines running tens to hundreds of qubits and high error rates. That sounds like a reason to wait, but it isn't.

Goldman Sachs is already exploring quantum approaches to derivatives pricing. Roche is investigating protein folding simulations. Energy companies are looking at grid optimization, and aerospace firms are preparing for quantum-secured communications. The common thread? These organizations started building their quantum readiness before they needed it.

Here's what quantum readiness actually looks like: mapping where quantum could create business impact, auditing your current technology stack for compatibility, analyzing the skills gaps in your teams, benchmarking against your industry ecosystem and reviewing your governance frameworks. You don't need a quantum computer to do any of this. You need strategic foresight.

And if you're handling sensitive data with long-term value, you should already be thinking about post-quantum cryptography. The threat isn't theoretical. It's called "Harvest Now, Decrypt Later." Adversaries are collecting encrypted data today, waiting for quantum computers powerful enough to crack it tomorrow.

When to Upskill Versus When to Hire

This brings us to the talent question and it's one I'm asked constantly: should we build these capabilities in-house or should we bring in external expertise?

The honest answer is that you'll probably need to do both, but the balance depends on your strategic priorities and your starting point.

For generative AI, the most critical internal capability to develop is prompt engineering. It sounds simple, but it isn't and it's often the difference between an AI tool that delivers real value and one that generates plausible-sounding nonsense. The good news is that more effective prompting often delivers similar benefits to expensive fine-tuning of models. That's a skill you can develop across your existing workforce relatively quickly.

For specialized roles (AI engineers, data scientists, technical architects) you're looking at a tighter market and higher investment. Experienced developers and data scientists command salaries well north of £100,000. If you're recruiting, make sure you're aligning to your actual technology stack, not chasing credentials for their own sake.

Here's a framework I use when advising organizations:

· Develop in-house when you need widespread capability across your workforce, when the skills align with your long-term strategy and when you have time to build competence before you need to deploy it. Sponsor master's degrees in data science, invest in platforms like Coursera or Udacity and create secondment opportunities with organizations further along the curve.

· Recruit permanent staff when the capability is core to your competitive advantage and when you can offer a compelling proposition beyond salary. The best digital talent wants interesting problems, modern ways of working and genuine autonomy.

· Engage freelancers when you need specialized skills for defined periods, when speed matters more than cost efficiency and when the work is genuinely project-based rather than ongoing.

· Build strategic partnerships when the capability sits outside your core mission, when scale requires it or when you're genuinely better off letting specialists do what they do best. Just make sure you maintain intelligent client functions internally: product managers, technical architects and business analysts who can hold partners to account and protect your interests.

When you're evaluating candidates and lack deep expertise yourself, focus on the fundamentals: check qualifications, review portfolios (GitHub is your friend for developers), conduct competency-based interviews and set practical exercises that mirror real work. Use mixed panels of internal and external assessors to compensate for your own knowledge gaps.

Ethics and Sustainability as Strategic Imperatives

Technology decisions are ethical decisions. That's not a philosophical position. It's a practical reality.

Consider the work of Caroline Criado-Perez in Invisible Women. She documented how crash test dummies were designed around male bodies, resulting in women being 47% more likely to be seriously injured in car crashes. That's not a technology failure. It's an ethics failure: a failure to ask who was being designed for and who was being left out.

Every AI system you deploy, every algorithm you implement, carries the same risk. Have you audited your training data for bias? Have you tested your outputs across different user groups? Have you built feedback mechanisms that surface problems before they cause harm?

Privacy is equally fundamental. GDPR compliance isn't a checkbox exercise. It's a framework for building trust. Your customers and citizens expect to know what data you're collecting, why you're collecting it and what you're doing with it. Generative AI complicates this further, because models can surface sensitive information in unexpected ways. Your governance frameworks need to evolve accordingly.

And then there's sustainability. Training a single large NLP algorithm can generate around 200,000 kilograms of CO₂, roughly equivalent to the lifetime emissions of five cars. Generative AI models are even more carbon-intensive. If your organization has made net-zero commitments, you need to account for the environmental impact of your AI adoption. That doesn't mean avoiding AI. It means being honest about trade-offs and choosing use cases where the value clearly justifies the cost.

I've found five questions useful for boards and leadership teams working through these issues:

1. Have we actively identified and addressed bias in our data, algorithms and processes?
2. Do we genuinely place customer privacy at the heart of how we design and operate?
3. Is our workforce diverse enough to reflect the society we serve?
4. Are we tracking and reducing the environmental impact of our technology choices?
5. Are our products and services accessible to everyone who needs them?

If you can't answer yes to all five with evidence, you have work to do.

The Path Forward Through AI and Quantum

Digital transformation has never been about technology for technology's sake. It's about using AI, data and digital services to make your organization better: for your users, your workforce and the world you operate in.

The organizations that thrive over the next decade will be those that invest in skills with the same seriousness they invest in systems. They'll be the ones asking difficult questions about ethics and sustainability before they become crises. And they'll be the ones who recognize that future-proofing isn't about predicting the future. It's about building the adaptive capacity to respond to whatever comes next.

The technology will keep evolving. Your principles shouldn't.

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