What is Gartner saying?

According to the Gartner “Top Generative AI Questions,” Generative Artificial Intelligence (AI) will affect the pharmaceutical, manufacturing, media, architecture, interior design, engineering, automotive, aerospace, defence, medical, electronics and energy industries by augmenting core processes with AI models. It will impact marketing, design, corporate communications, training, and software engineering by augmenting the supporting processes that span many organisations.

Gartner’s predictions include:

• By 2025, more than 30% of new drugs and materials will be systematically discovered using generative AI techniques, up from zero today. Generative AI looks promising for the pharmaceutical industry, given the opportunity to reduce costs and time in drug discovery.
• By 2025, 30% of outbound marketing messages from large organisations will be synthetically generated, up from less than 2% in 2022. Text generators like GPT-3 can already be used to create marketing copy and personalised advertising.
• In the manufacturing, automotive, aerospace and defence industries, generative AI can create designs optimised to meet specific goals and constraints, such as performance, materials, and manufacturing methods. This accelerates the design process by producing an array of potential solutions for engineers to explore

Read the Gartner – Top Generative AI Questions for your Enterprise here.

Benefits of Introducing AI into an Organisation

As more organisations introduce AI technology into their business, it is becoming clearer where benefits can be achieved. We have grouped individual benefits into common themes:

Productivity and Efficiency

This is the most common benefit most organisations see when they start to use AI Tools or adopt AI enhanced business applications.

The ability to automate repetitive tasks and streamline business processes as well as remove annoying housekeeping tasks, like taking meeting minutes, from an employee’s day, allows them to concentrate on meaningful value-add work.

Some organisations have, in the past, struggled with attempts to reengineer key business processes as they have not had data or tools to identify and analyse the problem’s process steps or the ability to model out the new process to understand the impacts. The new AI tools make these tasks more achievable for all businesses.

Customer Experience

The customer experience space is one where AI technology has rapidly changed the way organisations interact with their customers. The ability to personalise the journey, provide quicker enquiry responses and enable customers to self-serve, have all provided benefits directly to the customer.

Internally being able to understand individual customer’s preferences, analyse the impact of campaigns or new products, assess the emotional sentiment of interactions and provide tailored feedback to your customer service teams, provide benefits to both your organisation and the customer equally.

These tools are also a powerful way to increase the reach and the scale of your customer offerings, which in turn can increase revenue.

Analysis and Insights

With the ability to easily analyse large amounts of data, identify patterns and make predictions, AI technology has changed organisations’ decision-making processes. Having access to insights provided by AI offers organisations the confidence to make informed strategic decisions and reduce the risk of errors.

AI technology can allow an organisation to model different business scenarios and provide insights into the impact and revenue benefits of each one. The same approach can be used at the individual level – as an example, a doctor can complete a risk assessment on all treatment options available for one of his patients.

Organisations have been utilising AI to analyse the state of their data sets as part of their quality assurance processes or to assess if their data could be used to train an AI model successfully. The data analysis insights have proved an effective artifact for organisations to request internal funding to improve data governance tools and make quality assurance process changes.

Cost Reduction

In a manufacturing setting AI technology not only reduces the time to design and build new product, but it also reduces the error rate typically made by humans meaning the product is more cost effective to produce.

An unexpected benefit of automating time-consuming tasks is freeing up employees to engage with strategic initiatives. This can increase employee engagement as they feel their work is directly meaningful to the outcome of the business and can bolster your competitive advantage.

Utilising AI capability to analyse an organisation’s energy usage can help them to optimise energy consumption and save costs. An additional benefit is the organisation can demonstrate meaningful steps towards climate change mitigation which again could offer a competitive advantage.

Considerations for Embarking on AI

Organisations often start their AI journey with their employees using the freely available models to assist them with everyday tasks. As employee skills using the tools grow, the business begins to see the benefits and may become interested in opportunities to implement the use of AI more broadly across the business. It is important as an organisation to consider if you are ready to begin using AI, by assessing these factors.

1. External Environment

What activities are underway in your sector, and are any of your competitors already embracing AI?

This can help you determine which areas in your organisation might benefit from implementing AI. It is also valuable to gauge how active your competitors are in this space.

Does your organisation operate across different geographical regions and are there cultural differences between these areas?

These types of differences may require individual models which are trained with cultural nuances or at the minimum a model with multilingual capabilities.

2. Value Proposition

Does your organisation have a clear vision of where the value can be gained by introducing AI?

Having a vision will help determine the benefits available and solidify where the organisation needs to begin.

3. Ethical Concerns

It is extremely important to determine if introducing AI into your organisation will cause ethical issues or potential harm exposing risks which the organisation may have to respond to. Ethical considerations should cover:

Human Safety and Dignity – Could there be a possibility of physical or mental harm to a person from the information or predictions produced by an AI model?

Fairness – Is there a bias in your data or model which will place certain groups at a disadvantage, or could skew the output?

Accountability – Who will own the AI models and be accountable for the advice or actions an AI model generates?

Trustworthiness – Can you challenge the model’s predictions to ensure they are accurate and can be explained?

4. Organisational Readiness

Are your people ready to embrace AI? Have they had training or time to use the tools to feel empowered? Do you have a governance framework in place to ensure AI is used responsibly. Do you have the capability to evaluate the output of an AI tool?

Allowing time for your people to “play” will improve their confidence and encourage an openness to change. It is also recommended organisations set up a support system for new users and encourage cross team collaborations.

Companies who have had success with AI initiatives have created diverse, cross disciplined teams who have been allowed to focus solely on the current project. It allows them to learn as they develop and respond quickly to challenges encountered.

Developing a Governance framework will help your organisation to confirm you are developing an AI initiative responsibly. The framework can also cover the process to validate an AI initiative to ensure the outputs are accurate and protect your corporate property.

5. Data

Is your data easily available for the AI model to consume? What is your confidence level regarding the accuracy of the data? Have you thought about how to silo confidential data from being consumed by an external facing AI tool?

It is recommended your existing data is reviewed and assessed for currency and accurateness prior to starting any initiatives. Protections should also be put in place to protect corporate IP and sensitive customer information. Time spent getting your data ordered will improve the effectiveness of the AI tools to provide quality results.

If you would like to read more about Responsible Generative AI the World Economic Forum has published a report here.

Artificial Intelligence Project Risks

In a recent presentation to the Project Management Institute (PMI) by Scott Ambler, he presented a common set of challenges organisations have experienced when they have embarked on AI projects. All of them have had to work out how to resolve these and, in the extreme, have halted a project when there has not been a solution. Taking time to assess your business’ readiness can help mitigate these challenges.

Expectation Setting

As AI has become the latest “big thing,” organisations are under pressure to get on board. This rush often leads to unrealistic expectations and misalignment with stakeholders about what they think they are getting versus what is finally delivered. Strong leadership can also be lacking, and the risk of reputational damage can impact AI initiatives in the middle of development.

In a paper recently published by the World Economic Forum, one of the main risks identified by the organisations interviewed, was the concern that AI development is outpacing their ability to manage potential ethical and societal risks across their business.

Data Issues

Over the past two decades many organisations have been working to consolidate and clean their internal data sources. Initiatives to move data from single individual systems into Data Lakes, or link them through integration layers, have been driven to improve the flow of information within the business and allow data driven decision making. The analytics undertaken from these collated sources have been completed by employees who understand the data and make accommodations for the quirks in producing reports or preparing data sets.

AI models using these data sources autonomously have really highlighted the inadequacy of internal data in both quality and quantity for many organisations. AI models require large consistent sets of data to work effectively to produce quality outputs the organisation is comfortable to share internally, and externally with customers.

Organisational Readiness

Organisations often started AI initiatives by co-opting people from a range of business areas or from other projects to help. They found the team members’ competing priorities led to delays and unexpected issues in the AI initiatives. These initiatives are iterative in nature so allocating time to complete can be hard to determine, often leading to time overruns.

Technical Knowledge   

Organisations who are wanting to build their own AI models struggle to find skilled people. They require people who have the knowledge to build an AI model without bias, protect it from malicious use and can confidently evaluate and validate the model outputs. As with most new technology, there is currently an exceedingly small pool of people with the right capabilities available, so taking extra time to find the right people could be necessary.

AI Frameworks

Like the challenges with expectation management, setting up an AI framework early helps keep AI initiatives on track and provides an escalation process if there is a problem. Creating an AI strategy and setting up a governance model can guide your organisation to confidently make AI decisions. In the World Economic Forum paper, organisations raised concerns that the opaque inner workings of an AI model made it hard to anticipate future risks.

In Summary

Humans are adapting to AI technology at a rate never seen before and are more likely to implicitly trust the advice given by the models. As a business, it is important to spend time working out how and where AI can work in your organisation.

AI technology can add significant benefit to a business with productivity gains, personalised customer experiences and improved data decision making capability.

The caution is to take time to put all the right building blocks in place before you begin a significant AI innovation journey. Then your organisation will be able to capitalise on the benefits with confidence, knowing the risks are being mitigated.

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