Artificial intelligence (AI) has been altering industries as evidenced by Airbnb, Uber and other companies that have embraced its use to implement innovative new business models. Yet we may not fully understand how this emerging and rapidly advancing technology influences business model innovation. While many companies are being made vulnerable to new competitors equipped with AI technology, this study attempts to focus on the proactive side of the use of AI technology to drive business model innovation. Describing AI technology as the catalyst of business model innovation, this study sheds light on contingent factors shaping business model innovation initiated by the emerging technology. This study first provides a brief overview of AI, current issues being tackled in developing AI and explains how it transforms business models. Our case study of two companies that innovated their business models using AI shows its potential impact. We also discuss how executives can create an innovative AI-based culture, which rephrases the process of AI-based business model innovation. Companies that successfully capitalize on AI can create disruptive innovation through their new business models and processes, enabling them to potentially transform the global competitive landscape.

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Fluoxetine, a selective serotonin reuptake inhibitor, was compared with mianserin and placebo in a double-blind study. In total, 81 depressed patients were included. Patients were rated weekly on the Hamilton Depression Rating Scale (HDRS) and the Montgomery & Asberg Depression Rating Scale (MADRS). The duration was 6 weeks, and 52 patients completed the study. Significantly more patients on fluoxetine improved than patients on placebo. For mianserin no significant differences were found with either fluoxetine or placebo. Mean HDRS at the end of the study was also statistically significantly lower for fluoxetine, but not for mianserin, than placebo. Subscores of the MADRS showed improved sleep on mianserin at weeks 2 and 3. Suicidal feelings were reduced to a greater degree on fluoxetine than on mianserin and placebo at weeks 6 and 7. Fluoxetine induced weight loss, while patients on mianserin gained weight. Side effects were present in most patients on the two active drugs; those on fluoxetine experienced nausea and vomiting, and those on mianserin drowsiness.

1 There have been few controlled prospective investigations into the prevention of suicidal behaviour and by and large they have failed to demonstrate the efficacy of social work, psychotherapy or psychiatric treatment. 2 A group of 58 high‐risk patients with multiple episodes of suicidal behaviour was treated with mianserin 30 mg at night or placebo in a six month double‐blind trial of the efficacy of an antidepressant in reducing suicidal behaviour. 3 Patients were screened for depression, schizophrenia and organic disease. Patients were diagnosed as suffering from personality disorders according to DSM‐III criteria mainly borderline or histrionic. 4 There was no significant difference in outcome between the mianserin and placebo treated group at any point in the six month study. 5 An item analysis of the MADRS showed that at entry the item ‘reduced appetite’ predicted subsequent suicidal attempt. The total MADRS score did not predict further suicidal acts at entry but was highly significant at four weeks. At four weeks the items ‘reduced sleep’ and ‘reduced appetite’ were highly significant predictors of further suicidal acts and the items ‘lassitude’, ‘suicidal thoughts’, ‘inability to feel’ and ‘pessimistic thoughts' were significant predictors.

In a randomised double‐blind group comparison study of 40 patients with endogenous depression zimelidine appeared to be as effective an antidepressant as amitriptyline at 4 and 6 weeks using the Hamilton Rating Scale (HRS) and the Montgomery and Åsberg Depression Rating Scale (MADRS). At 2 weeks there was a significantly better response (P < 0.05) on zimelidine compared to amitriptyline on the clinician's global scale and 4 out of 10 items on the MADRS suggesting an early onset of action. A significantly better response to zimelidine was seen on the item somatic anxiety (HRS) while the effect on sleep and appetite was better in the amitriptyline group. There were significantly more side effects, raw and corrected, in the amitriptyline‐treated group. High steady state plasma concentrations of norzimelidine (>800 nmol/l) which were significantly correlated with age (r = 0.8) were associated with a significantly poorer response suggesting that a lower dose than 200 mg in older patients may be appropriate.

In a double-blind group comparison study of 39 patients with primary depressive illness zimelidine in a dose of 200 mg at night demonstrated the same order of antidepressant efficacy as maprotiline in a dose of 150 mg at night after either two or four weeks treatment measured by the amelioration or final score on the Hamilton Rating Scale (HRS) and on the Montgomery & Asberg Depression Rating Scale (MADRS).Both zimelidine and maprotiline demonstrated significant antidepressant activity at 2 weeks compared with 2 weeks prior treatment with placebo measured by amelioration on HRS (paired t 4.1 PCO.001, t 2.7 PCO.02) or MADRS (paired t 3.5 PC0.005, t 5.1 PCO.001).An item analysis of the MADRS showed significantly better sleep and appetite in the maprotiline-treated group compared with the zimelidinetreated group which is in accord with the pharmacology of the two compounds.

A new approach of coordination of decisions in a multi site system is proposed. It is based this approach on a multi-agent concept and on the principle of distributed network of enterprises. For this purpose, each enterprise is defined as autonomous and performs simultaneously at the local and global levels.The basic component of our approach is a so-called Virtual Enterprise Node (VEN), where the enterprise network is represented as a set of tiers (like in a product breakdown structure). Within the network, each partner constitutes a VEN, which is in contact with several customers and suppliers. Exchanges between the VENs ensure the autonomy of decision, and guarantiee the consistency of information and material flows. Only two complementary VEN agents are necessary: one for external interactions, the Negotiator Agent (NA) and one for the planning of internal decisions, the Planner Agent (PA).If supply problems occur in the network, two other agents are defined: the Tier Negotiator Agent (TNA) working at the tier level only and the Supply Chain Mediator Agent (SCMA) working at the level of the enterprise network. These two agents are only active when the perturbation occurs. Otherwise, the VENs process the flow of information alone.With this new approach, managing enterprise network becomes much more transparent and looks like managing a simple enterprise in the network. The use of a Multi-Agent System (MAS) allows physical distribution of the decisional system, and procures a heterarchical organization structure with a decentralized control that guaranties the autonomy of each entity and the flexibility of the network.

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