Feb 26, 2021

The modern hurdles to widespread AI adoption

Machine Learning
Scott Zoldi
3 min
Despite the use of AI becoming more prevalent every year, it still only sits on the verge of mass adoption
Despite the use of AI becoming more prevalent every year, it still only sits on the verge of mass adoption...

Artificial Intelligence is used to inform and shape strategies across a range of industries, but there are still several challenges holding it back from widespread adoption. 2020 has proved the need for digital services and supporting AI is essential, but in many ways AI is not there yet. Ethical considerations must be addressed and operational difficulties, such as building a team with the right skill set, always provide an obstacle. 

COVID-19 has given organisations across the world the need to expand their digital services. At first glance this would appear to benefit the spread of machine learning. When more people move their financial transactions and activity online, there is more data to tally and learn from. The question now becomes – is AI robust enough for the challenge?  

In 2021 I believe AI will cross the chasm, becoming a reliable and safe, mainstream business technology — but maybe not how, or for reasons why, you might expect. 

Responsible AI

We often see technology developing at speeds that regulation cannot match. It can be a laborious task to bring new legislation into effect but, once ready, new tech can be swiftly implemented to meet regulation. This is why it is no longer good enough for AI-using organisations to ‘just do their best’. They must document and audit AI development around defined corporate standards of responsible AI. 

Organisations must formally document and enforce their model development and operationalization standards and set them in the context of the three pillars of responsible AI: explainability, accountability, and ethics.

  • Explainability: Organisations relying on an AI decision system must ensure they have an algorithmic construct that captures and communicates the relationship between the decision variables to arrive at a final business decision.
  • Accountability: AI models must be properly built and focus has to be placed on the limitations of machine learning and careful thought applied to the algorithms used.
  • Ethics: Adding to the requirements of explainability and accountability, ethical models must be tested continuously, and any discrimination removed.

There is no question about it, building responsible AI models takes time and is painstaking work. In a recent survey, more than 93% of data and analytics executives said that ethical considerations represented a barrier to AI adoption within their organizations. The meticulous and essential scrutiny is an ongoing process to ensure AI is used responsibly. This scrutiny must include regulation, audit and advocacy.  

Regulations play an important role in setting the standard of conduct and rule of law for use of algorithms. In the end, however, regulations are either met or not, and demonstrating alignment with regulation requires audit. Organizations that adopt technology such as model governance blockchains will be in the best positions to respond.

Operational difficulties

Building a team with the right set of skills can be difficult. This is exhibited across a range of industries, with analytics leaders consistently ranking it as a high or medium barrier to entry. 

Integrating new technologies, however, is often seen as the biggest problem in creating a machine learning framework. If an organisation is a long-standing business, it is highly likely it will face issues around legacy estates and integration of new AI technology into operational systems.  

The list of challenges is long but by no means outweigh the benefits AI brings with it. As its advocates become more vocal and industry grapple with the rapid acceleration of digital, we will see Responsible AI rise up to cement itself in industries across the world. 

By Scott Zoldi, chief analytics officer at FICO 

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Jun 15, 2021

Searching for the Top 100 Leaders in Technology

2 min
Have your say and nominate technology industry influencers and legends in our search for the Top 100 Leaders announced at upcoming LIVE event

The search is on for the Top 100 Leaders in Technology 2021 – nominated by readers of Technology magazine and open to all.

The initiative has been launched and nominations are now open, with the final, prestigious Top 100 due to be announced during Technology and AI LIVE running 14-16 September, beamed from London to the world.

This latest, definitive list of the leading executives and influencers in the industry will be announced at the event and shared across social media channels, this website, and presented in a special supplement that honours all of those named in our annual list.

The Top 100 Leaders follows on from the well-received Top 100 Women in Technology that BizClik Media Group (BMG) – publishers of Technology magazine, AI magazine and a growing portfolio of industry-leading titles – produced in March this year to coincide with International Women’s Day. 

“The Top 100 Women recognised the incredible and influential women driving our industry,” says Scott Birch, editorial director, BMG. “The success of that initiative encouraged us to recognise the Top 100 Leaders – individuals championing everything that we love about technology and embracing best practice that’s good for business.”

Nominations are already coming in, with some notable highlights including:

Rhonda Vetere - Herbalife

Bryan Smith - Expedient

Nominate your Top 100 Leader HERE

The deadline for nominations closes on Sunday 1 August 2021, and it is free to nominate. The Top 100 Leaders will be announced across our platforms and at the LIVE event.


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