Threats versus potential benefits: Weighing up the enterprise risk of embracing AI | Computer Weekly

Risk assessment and prioritisation

“People don’t ask what AI adoption looks like in practice,” he says. “CISOs can build data lakes of epic proportions, with multifactor authentication, user attribution, secure access, and so on. Then AI comes along and maps a numerical representation into its workflow, embedding models, and then vector databases, getting the outputs through retrieval augmented generation (RAG) workflows and so on, and the security controls are lost.”

This matches Office of National Statistics (ONS) figures that suggest the most common barriers to AI adoption are difficulty identifying activities or business use cases (39%) and cost (21%). Some 16% of firms cited a lack of AI expertise and skills.

“If the underlying processes are flawed, AI cannot fix that. It will just amplify chaos,” says Cassidy.

“As always, start with the problem, not the hype, and don’t adopt AI just because you think you should. Ensure you pinpoint specific business challenges – customer service bottlenecks or slow cycles – and build from there.”

Reduce risk with clear usage policies and guardrails for single-workflow pilots – perhaps summarising reports, assisting queries, or automating invoice generation – then measure the impact.

Did it work? Did it reduce cost or increase value? Learn from that and build a roadmap from evidence, not enthusiasm, Cassidy advises.

Further mitigation strategies

Regardless, you likely do not want to jump into AI straight away, and you do not want to plug all your sensitive or regulated data into an off-the-shelf model to train it either, adds Tony Lock, distinguished analyst at IT market watcher Freeform Dynamics.

“Once you put data into the language model, you can’t take it out again. It’s just subsumed into the pattern,” says Lock. “That’s why RAG is around, so instead of feeding information into an LLM, you cleanse everything.”

And what if your model is pulled from the market? While open source, parallel developments and application programming interface (API) gateways can help protect organisations, Lock suggests we also cannot know exactly how risks will play out when it comes to, say, OpenAI losing an in-progress lawsuit about its rights to use others’ intellectual property.

Once you put data into the language model, you can’t take it out again. It’s just subsumed into the pattern. That’s why RAG is around, so instead of feeding information into an LLM, you cleanse everything

“If you’re told by a judge that you need to take all that information out, that you’re not allowed to use it for training purposes, you’re likely going to have the entire language model start again with properly secured data that you’ve acquired,” says Lock.

Penalties could ensue. How will the AI suppliers then respond? Will they pass on related costs to customers? Will customers themselves be penalised? These are unanswered questions that might require specific legal advice.

Before you bet on using specific data in a particular model, it might be wise to remember that there are multiple AI-related lawsuits in the pipeline.

National regulations are complicating the environment. For example, the UK government currently favours some yet-to-be-devised sort of “opt out” of AI process for intellectual property (IP) owners.

Yet in the European Union, for instance, that will not work, because everything typically has to be “opt in”, notes Lock. And to opt in, users have to be told exactly how their IP is going to be used.

“Maybe the US courts will not enforce action. But then again, all those companies have European, UK, Japanese subsidiaries that could become liable, maybe even the local CEO,” he says.

At the same time, it can pay to wait. After all, there can be only one “first mover”; later entrants may benefit from a relative lack of obstacles that early adopters had to tackle.

The top recommendation

Databricks’ Williamson recommends enterprises get their data house in order first, even if that delays adoption. “Data processing and organising is hard, even for companies with money and a huge in-house team,” he says.

Usually, data is just not ready for AI. That means a need to inventory, audit and map all structured and unstructured data. A cleaner, deduplicated, standardised, accurate and relevant data foundation may require silo consolidation too, well before adding AI on top, he points out.

The good news is that fixing data “in the broader sense” will buy time for enterprises to consider their approach and generate benefits – including cost savings, storage efficiencies and the removal of legacy or shadow IT – for the whole business.

Rubrik’s Cassidy believes opportunities are typically about “smart delegation” of tasks and the democratisation of data-based intelligence across the business. “And AI offers SMEs a genuine levelling-up capability.”

Implementation plan and timelines

Robbie Jerrom, senior principal technologist for AI at Red Hat, says enterprises should focus on working out what they should do with AI, and take as much time as they need to do that.

“First, understand your need, then narrow the use case. Don’t try to boil the ocean,” says Jerrom.

One thing organisations can do is calculate the tokens required for a given AI enablement, although it is not always easy.

First, understand your need, then narrow the use case. Don’t try to boil the ocean

“Writing some small bits of Python code, maybe 10 minutes’ work, might use 45,000 tokens. Map it back to cost, and it’s maybe a couple of cents. But if you scale that up, and have 10 developers doing it all day long, how much is it? Every time an AI agent goes out and talks to something, for example, it uses tokens.”

Pick something small, get some experience running something trackable, and build something from which the business will learn.

Sandboxing can reduce risk, especially when considering more autonomous systems such as agents. Examine whether it can be trained in the company’s static policies, for example.

Perhaps ask a model to review a contract, compare it with previous contracts, and show the differences, confusions or irregularities. You might notice two irregularities, but the model might highlight something different to think about in addition. Changes over years, for instance, might signal a possible challenge in the customer relationship that had not been previously picked up.

AI can help discipline your thinking and apply method. Afterwards, double-check results and re-evaluate. Can you tune the model to better align with need, or try an alternative?

“Some of the boring use cases are where you’ll start to see value,” says Jerrom, noting that while generative AI (GenAI) makes mistakes, so do humans.

Education and training for workers are equally crucial. Most will need help learning how best to use their AI services.

“This can get you into a lot of hot water,” warns Jerrom. “AI is already everywhere.”

Next steps for enterprise AI adoption

Sue Daley, director of technology and innovation at TechUK, says all AI has “huge potential” for businesses. Regardless of shape, size or sector, it is key to understand exactly how AI can drive efficiencies and effectiveness. “What do you want it to do and what are you looking to achieve?”

As with any other technology, is AI the appropriate tool? Sometimes benefits might be agentic, while others might require a small language model or very specific approach.

“Small language models may be more appropriate for a specific business need or issue in their supply chain, logistics or operations. Context will be so important,” says Daley.

Play “mindfully” in a sandbox or safe environment to learn what AI can do. Examine compliance, security policies and practice, and ethics around responsible innovation. Consider upskilling needs. Acquire perspective from people and build cross-functional teams across the business.

“Start with education and awareness. Consider your organisation at all levels, from board level to middle management and individual workers,” says Daley. “Find ways to bring people on the journey with you. It’s a change management process, affecting a lot of people’s jobs.”

Even if enterprises think of GenAI tools as just another chatbot, many chatbots have not satisfied customers. Benefiting from AI requires serious thought, including on how the next version or product is evolving. Again, the top tip is that outputs can only be as good as your data inputs, she says.

Freeform Dynamics’ Lock adds: “Understand how to get AI working so your people say it actually helps them, rather than it being just something else to ‘get around’. When they’re picking AI up on their own, remember some might be doing advantageous things you hadn’t thought of – or  something they shouldn’t. User effectiveness and happiness are crucial.”

Finally, don’t forget there are different classes of AI – some of which the business may already have experience with.