AI in Business: “Building the Right Competencies Is Key”

AI promises to revolutionize various aspects of business – for example when it comes to delivering new types of customer engagement, strategic innovation and business transformation. But the journey to AI adoption is not without its challenges.

In an interview with DLD, Ana Paula Assis, the General Manager of IBM for Europe, Middle East and Africa, highlights the most important aspects of successfully implementing generative AI systems in practice. Make sure to watch her DLD24 talk to go deeper.

Where do generative AI systems deliver real value for corporations?

From our extensive work with enterprises across Europe, Middle East and Africa, we see several areas where generative AI is already showing compelling, measurable value.
 
One of them is customer care, where IBM has been developing conversational AI systems for several years. We’ve deployed more than 1,000 chatbots and virtual assistants for enterprise clients around the world. Generative AI gives us the opportunity to take traditional chatbots to a new level of performance and outcomes.
 
We are also seeing great traction around digital labor. Generative AI helps to automate repetitive tasks and simplify workers’ lives, which drives productivity.

How does this change the tasks that people perform?

When you streamline workflows and let these AI assistants find information, you save time and effort so you can focus on tasks that really matter, that truly have an impact on your performance.
 
Take coding as an example. By applying our watsonx Code Assistant, a generative AI-powered tool, developers and IT operators can develop software more quickly, translate code from one programming language to another and use natural language prompts to get accurate results.

These capabilities boost productivity, enable organizations to better understand their code and accelerate code generation – while maintaining trust, security and compliance, which is a very important aspect for many business applications of AI.

Creating Trusted AI for Business

Watch the DLD24 conversation between Ana Paula Assis and Hubert Burda Media manager Marc Al-Hames

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What does it take to implement AI into everyday processes?

The economics of AI have fundamentally changed. Not long ago, deploying AI required deep expertise, lots of time for data cleansing, and substantial infrastructure costs. It is no surprise that so many projects never made it out of the “pilot purgatory”. Today, large language models have significantly accelerated the process and reduced the expenses involved.

What remains, however, is the need to ensure responsible adoption of this powerful technology. At IBM we are convinced that ethical, governed AI not only mitigates risks, but also enhances customer trust and corporate reputation.

What solution do you see to enhance transparency and trust in AI?

We have introduced watsonx.governance, an end-to-end toolkit for AI governance across the entire lifecycle to enable responsible, transparent and explainable AI workflows. That includes, for example, monitoring AI models for bias and drift, documenting the origin and evolution of datasets, enhancing accuracy and quality of models, identifying bias and the need for retraining, generating reports and dashboards, and translating regulatory demands into enforceable polices.

For example, a healthcare company is using these AI governance tools to provide detailed logs of how the AI system reached its conclusions. This transparency not only builds trust with patients, but also ensures compliance, reduces the risk of misdiagnosis and increases accountability.

Which challenges do you see for AI adoption?

We know that to harness AI’s full potential while maintaining fairness, transparency and compliance, data and AI governance tools are essential. The good news is that leaders are taking action. We recently conducted a study among senior business executives in Europe, and of those who are already using generative AI – or are ready to do so this year – 96 percent are already engaged in shaping new ethical and governance frameworks.

Additionally, building the right AI competencies in an organization is key. Our study also showed how European business leaders are taking action. 95 percent of the executives we surveyed said that they are taking steps to ensure they have the right AI skills in their organizations.

Opportunities and challenges: Corporate executives in Europe are eager to deploy generative AI tools but also have ethics and privacy concerns, the IBM survey Leadership in the age of AI (PDF) shows.

What’s your advice to corporate leaders who are taking a wait-and-see approach?

AI is a huge opportunity for both business and society. It can help unlock $4.4 trillion annually in productivity gains and offers a critical advantage for organizations that fully embrace it.

I would encourage those who are watching from the sidelines to get their feet wet. You can start small, exploring how AI can transform a specific area of your business. The approach should be to explore, experiment, and then rapidly iterate and expand the integration. So my advice here is to start early, because that creates momentum and confidence.

Which milestones do you expect AI to reach in 2024?

We will see how early adopters of AI start to outperform their peers, so the incentive to leverage AI will only grow. But again, as important as doing it fast is doing it right. Organizations will realize that being able to trust AI models is essential and will therefore search for governance solutions that not only meet upcoming regulation, like the EU AI Act, but also get them ahead of the curve to ensure overall trustworthiness.
 
We foresee that businesses will be able to better measure the return on investment in AI and that will accelerate the move from experimentation to implementation. This will increase the pressure on decision makers to address key considerations such as data lineage, provenance, privacy, and security – which means that having a robust and automated governance process will be essential.

There will also be an increased focus on building AI skills. The current gap is a serious obstacle to the successful application of AI across industries, and both employers and employees need to move fast. We believe that generative AI will not replace people, but people who use generative AI will replace people who don’t. That is why last September IBM announced a new global commitment to train two million learners in AI in three years, with a focus on underrepresented communities.

We have a unique opportunity to leverage AI to make businesses and communities more productive, innovative and responsible in 2024.

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