Copyright: cio.com – “12 AI predictions for 2025”

This year we’ve seen AI move from pilots into production use cases. In 2025, they’ll expand into fully-scaled, enterprise-wide deployments.

Generative AI has seen faster and more widespread adoption than any other technology today, with many companies already seeing ROI and scaling up use cases into wide adoption.

Vendors are adding gen AI across the board to enterprise software products, and AI developers haven’t been idle this year either. We’ve also seen the emergence of agentic AI, multi-modal AI, reasoning AI, and open-source AI projects that rival those of the biggest commercial vendors.

According to a Bank of America survey of global research analysts and strategists released in September, 2024 was the year of ROI determination, and 2025 will be the year of enterprise AI adoption.

“Over the next five to 10 years, BofA Global Research expects gen AI to catalyze an evolution in corporate efficiency and productivity that may transform the global economy, as well as our lives,” says Vanessa Cook, content strategist for Bank of America Institute.

Small language models and edge computing

Most of the attention this year and last has been on the big language models —  specifically on ChatGPT in its various permutations, as well as competitors like Anthropic’s Claude and Meta’s Llama models. But for many business use cases, LLMs are overkill and are too expensive, and too slow, for practical use.

“Looking ahead to 2025, I expect small language models, specifically custom models, to become a more common solution for many businesses,” says Andrew Rabinovich, head of AI and ML at Upwork. LLMs aren’t just expensive, they’re also very broad, and not always relevant to specific industries, he says.

“Smaller models, on the other hand, are more tailored, allowing businesses to create AI systems that are precise, efficient, robust, and built around their unique needs,” he adds.[…]

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Copyright: cio.com – “Cost, security, and flexibility: the business case for open source gen AI”

Travel and expense management company Emburse saw multiple opportunities where it could benefit from gen AI. It could be used to improve the experience for individual users, for example, with smarter analysis of receipts, or help corporate clients by spotting instances of fraud.

Take for example the simple job of reading a receipt and accurately classifying the expenses. Since receipts can look very different, this can be tricky to do automatically. To solve the problem, the company turned to gen AI and decided to use both commercial and open source models. Both types of gen AI have their benefits, says Ken Ringdahl, the company’s CTO. The main commercial model, from OpenAI, was quicker and easier to deploy and more accurate right out of the box, but the open source alternatives offered security, flexibility, lower costs, and, with additional training, even better accuracy.

With security, many commercial providers use their customers’ data to train their models, says Ringdahl. It’s possible to opt-out, but there are caveats. For instance, you might have to pay more to ensure the data isn’t being used for training, and might potentially be exposed to the public.

“That’s one of the catches of proprietary commercial models,” he says. “There’s a lot of fine print, and things aren’t always disclosed.”

Then there’s the geographical issue. Emburse is available in 120 different countries, and OpenAI isn’t. Plus, some regions have data residency and other restrictive requirements. “So we augment with open source,” he says. “It allows us to provide services in areas that aren’t covered, and check boxes on the security, privacy, and compliance side.”[…]

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Copyright: cio.com – “AI data readiness: C-suite fantasy, big IT problem”

Business leaders may be confident that their organizations’ data is ready for AI, but IT workers tell a much different story, with most spending hours each day massaging the data into shape.

Nearly nine in 10 business leaders say their organizations’ data ecosystems are ready to build and deploy AI at scale, according to a recent Capital One AI readiness survey. But 84% of the IT practitioners surveyed, including data scientists, data architects, and data analysts, spend at least one hour a day fixing data problems.

Seventy percent of those IT pros spend one to four hours a day remediating data issues, while 14% spend more than four hours each day, according to the survey.

The survey points to a fundamental misunderstanding among many business leaders regarding the data work needed to deploy most AI tools, says John Armstrong, CTO of Worldly, a supply chain sustainability data insights platform.

“There’s a perspective that we’ll just throw a bunch of data at the AI, and it’ll solve all of our problems,” he says. “It says our job as technology leaders can help educate our audience on what is possible and what it will take to get to their goal.”

The implications of the ongoing misperception about the data management needs of AI are huge, Armstrong adds. When he talks to other IT leaders, they all are struggling with pressure to adopt AI, Armstrong says.

“It’s a big, big issue, because if not done right, your organization could spend literally millions of dollars on the wrong solution set to achieve the wrong outcome,” he says.[…]

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Copyright: cio.com – “Agentic AI: 6 promising use cases for business”

AI agents will play a vital role in software programming and cybersecurity, but they will also change enterprise workflows and business intelligence, experts say.

Agentic AI is having a moment, as proponents see the benefits of using autonomous AI agents to automate manual tasks across organizations.

Agentic AI, which Forrester named a top emerging technology for 2025 in June, takes generative AI a step further by emphasizing operational decision-making rather than content generation. The promise the approach has for impacting business workflows has organizations such as Aflac, Atlantic Health System, Legendary Entertainment, and NASA’s Jet Propulsion Laboratory already pursuing the technology.

CRM leader Salesforce has since centered its strategy around agentic AI, with the announcement of Agentforce. IT service management giant ServiceNow has also added AI agents to its Now Platform. Microsoft and others are also joining the fray.

With AI agents popping up in so many situations and platforms, organizations interested in the technology may find it difficult to know where to start. A handful of use cases have so far risen to the top, according to AI experts.

Agentic AI will integrate smoothly with ERP, CRM, and business intelligence systems to automate workflows, manage data analysis, and generate valuable reports, says Rodrigo Madanes, global innovation AI officer at EY, a consulting and tax services provider. AI agents, unlike some past automation technologies, can make decisions in real-time, making process automation a primary use case.

“AI agents can automate repetitive tasks that previously required human intervention, such as customer service, supply chain management, and IT operations,” Madanes says. “What sets the technology apart is its ability to adapt to changing conditions and handle unexpected inputs without manual oversight.”[…]

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Copyright: cio.com – “Is Now the Right Time to Invest in Implementing Agentic AI?”

While vendors say their current agentic AI-based offerings are easy to implement, analysts say that’s far from the truth.

Software vendors’ pitches are evolving, with  agentic AI beginning to supplant generative AI in their marketing messages. Rather than just generating code or content for human review agentic AI will, they say, follow instructions, make decisions, and take actions much as a human worker would, without human intervention.

It’s more than just a smarter RPA

Agentic AI isn’t just a better version of robotic process automation (RPA): It promises to take enterprises places RPA never could.

“Think of RPA as a train on tracks — it can only go where the tracks are laid. Agentic AI is more like a self-driving car — it can navigate different routes and situations adaptively,” said Paul Chada, co-founder of agentic AI-based software providing startup Doozer AI.

What makes agentic AI autonomous or able to take actions independently is its ability to interpret data, predict outcomes, and make decisions, learning from new data — unlike traditional RPA, which falters when encountering unexpected data, said Cameron Marsh, senior analyst at Nucleus research.

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Copyright: cio.com – “AI-Native Software Engineering May Be Closer Than Developers Think”

Over the next three years, many organizations will use AI agents to write the majority of their software, moving developers into review roles, Gartner predicts. But challenges and questions remain.

Developers unimpressed by the early returns of generative AI for coding take note: Software development is headed toward a new era, when most code will be written by AI agents and reviewed by experienced developers, Gartner predicts.

Organizations and vendors are already rolling out AI coding agents that enable developers to fully automate or offload many tasks, with more pilot programs and proofs-of-concept likely to be launched in 2025, says Philip Walsh, senior principal analyst in Gartner’s software engineering practice.

By 2026, “there will start to be more productive, mainstream levels of adoption, where people have kind of figured out the strengths and weaknesses and the use cases where they can go more to an autonomous AI agent,” he says. “In the 2027 range, we’ll really see this paradigm take root, and engineers’ workflows and skill sets will have to really evolve and adapt.

In a recent press release, Gartner predicted that 80% of software engineers will have to reskill to fit into new roles created when generative AI takes over more programming functions.

These AI coding agents will be more advanced than the AI coding assistants in wide use today, but they will still need experienced programmers to check their work and tweak the code, Walsh says. In coming to software development, agentic AI — a rising trend that emphasizes autonomous decision-making over simple content generation — will push the boundaries of current AI coding copilots to enable AI-native software engineering to emerge.

While current AI coding assistants can write snippets of code, they often struggle to create software from scratch, but that won’t be the case for evolving coding agents, Walsh says.[…]

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Copyright: cio.com – “Beyond the Hype: Key Components of an Effective AI Policy”

In today’s rapidly evolving technological landscape, artificial intelligence (AI) plays a pivotal role in transforming businesses across various sectors. From enhancing operational efficiency to revolutionizing customer experiences, AI offers immense potential. However, with great power comes great responsibility. Creating a robust AI policy is imperative for companies to address the ethical, legal and operational challenges that come with AI implementation.

Understanding the need for an AI policy

As AI technologies become more sophisticated, concerns around privacy, bias, transparency and accountability have intensified. Companies must address these issues proactively through well-defined policies that guide AI development, deployment and usage. An AI policy serves as a framework to ensure that AI systems align with ethical standards, legal requirements and business objectives.

For instance, companies in sectors like manufacturing or consumer goods often leverage AI to optimize their supply chain. While this leads to efficiency, it also raises questions about transparency and data usage. A clear policy helps ensure that AI not only improves operations but also aligns with legal and ethical standards.

Key components of an effective AI policy

Ethical principles and values

It’s important to define the ethical principles that guide AI development and deployment within your company. These principles should reflect your organization’s values and commitment to responsible AI use, such as fairness, transparency, accountability, safety and inclusivity. If your company uses AI for targeted marketing, for example, ensure that its use respects customer privacy and prevents discriminatory targeting practices.
Data governance

Strong data governance is the foundation of any successful AI strategy. Companies need to establish clear guidelines for how its data is collected, stored and used, and ensure compliance with data protection regulations like GDPR in the EU, CCPA in California, LGPD in Brazil, PIPL in China and AI regulations such as EU AI Act.[…]

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Copyright: cio.com – “Finding Pearls Among Perils With AI Productivity Solutions”

AI in the enterprise can offer pearls of productivity. However, it also contains risks and can produce perils for CISOs and CTOs. Striking the right balance between opportunity and risk will likely be a successful equation for enterprise organizations.

In my last column, I shared the findings from my informal poll with leading CISOs on AI’s impact on people, policies, and processes. Feedback from many has been that it was an eye-opening article putting a spotlight on the momentum AI has gained in enterprise operations since last year.

Looking back over a year to my April 2023 column, I stated that from my perspective in the venture world, AI penetration in the enterprise has barely scratched the surface. What a difference 18 months makes.

Today, leading enterprises are implementing and evaluating AI-powered solutions to help automate data collection and mapping, streamline administrative support, elevate marketing efficiencies, boost customer support, strengthen their cyber security defenses, and gain a strategic edge.

Over the summer, I connected again with C-Suite members on these topics, asking several questions about AI’s changing influence within their organizations. Their answers are summarized below, and I think what you’ll find is also very enlightening.

What existing (or legacy) technologies within your enterprise is AI impacting?

From my discussions around this topic, it is clear that AI has truly begun to penetrate the enterprise in the past year, often being incorporated into existing systems, in some cases replacing legacy systems, and in others being a net new product deployment. While a year ago, most of these AI deployments were tire-kicking exercises and Proof of Concepts (POCs). Real deployments at scale are now occurring for a variety of use cases.[…]

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Copyright: cio.com – “The Real AI Training Gap? IT Leaders Believe In It, But Many Don’t Provide It”

IT leaders fear AI projects will fail without employee expertise. But with less than half offering formal AI training, falling short might be inevitable.

If you’re among the 95% of IT leaders who believe AI projects will fail without staff who can effectively use AI tools, chances are you’re not doing enough about it.

That’s because only 40% of executives and IT leaders say their organizations have formal AI training for employees, according to the recent Pluralsight AI Skills Report. And with CIOs increasingly responsible for setting the workforce AI training agenda, IT leaders may soon find themselves in the hot seat on AI preparedness.

And employees are taking note. Just over half of employees responding to a survey released in August by digital workplace vendor Slingshot said they felt adequately trained in AI.

“Anytime you have something, any sort of new technology that really upends the way we do things, it catches a lot of people off guard,” says David Harris, principal generative AI author at Pluralsight. “I think every businessperson knows that they need to implement AI in some way, but they’re not exactly sure how, and they’re not exactly sure how much their employees know.”

And the hiring market isn’t likely to help much, given that AI is such a new technology, Harris notes. Instead, he and other industry observers and experts believe all employees — developers, salespeople, and office workers included — can benefit from AI training.[…]

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Copyright: cio.com – “The Role Of AI In Operational Efficiency: Beyond The Silver Bullet”

AI has transformational power, but for most enterprises, focusing on operational efficiency rather than miracles is far more valuable.

Artificial Intelligence (AI) has earned a reputation as a silver bullet solution to a myriad of modern business challenges across industries. From improving diagnostic care, to revolutionizing the customer experience, there are many industries and organizations that have experienced the true transformational power of AI.

However, that’s not the case for the masses. And organizations that view AI as a fix-all are missing a huge opportunity—and are also likely to encounter significant challenges. When AI is applied in a way that overemphasizes its strengths and downplays its weaknesses, that’s when we run into problems.

While we tend to hear more about innovative, breakthrough AI use cases, the real value of AI lies in its ability to vastly improve operational efficiency. Is it less exciting than AI writing and producing its own songs or creating fine art in a matter of seconds? For sure. But for most businesses, a catchy tune or pretty picture aren’t going to move the needle.

The strengths of AI in modern business

AI’s ability to automate tasks, reduce errors, and make data-driven decisions at scale are its best lauded strengths. From predictive analytics to natural language processing (NLP), AI-powered applications enable faster and more accurate decision-making. In other words, the allure of AI lies in its ability to process vast amounts of data quickly, identify patterns that might be invisible to humans, and adapt to new information in real time.

These capabilities are undeniably valuable. In sectors like finance, healthcare, and manufacturing, AI-driven solutions have already proven their worth by optimizing supply chains, improving risk management, and enhancing customer service.[…]

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